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Semantic segmentation metrics in Keras and Numpy. IoU, Dice in both soft and hard variants. Mean metrics for multiclass prediction. See https://ilmonteux.github.io/2019/05/10/segmentation-metrics.html for discussion
import numpy as np
import keras.backend as K
import tensorflow as tf
def metrics_np(y_true, y_pred, metric_name, metric_type='standard', drop_last = True, mean_per_class=False, verbose=False):
"""
Compute mean metrics of two segmentation masks, via numpy.
IoU(A,B) = |A & B| / (| A U B|)
Dice(A,B) = 2*|A & B| / (|A| + |B|)
Args:
y_true: true masks, one-hot encoded.
y_pred: predicted masks, either softmax outputs, or one-hot encoded.
metric_name: metric to be computed, either 'iou' or 'dice'.
metric_type: one of 'standard' (default), 'soft', 'naive'.
In the standard version, y_pred is one-hot encoded and the mean
is taken only over classes that are present (in y_true or y_pred).
The 'soft' version of the metrics are computed without one-hot
encoding y_pred.
The 'naive' version return mean metrics where absent classes contribute
to the class mean as 1.0 (instead of being dropped from the mean).
drop_last = True: boolean flag to drop last class (usually reserved
for background class in semantic segmentation)
mean_per_class = False: return mean along batch axis for each class.
verbose = False: print intermediate results such as intersection, union
(as number of pixels).
Returns:
IoU/Dice of y_true and y_pred, as a float, unless mean_per_class == True
in which case it returns the per-class metric, averaged over the batch.
Inputs are B*W*H*N tensors, with
B = batch size,
W = width,
H = height,
N = number of classes
"""
assert y_true.shape == y_pred.shape, 'Input masks should be same shape, instead are {}, {}'.format(y_true.shape, y_pred.shape)
assert len(y_pred.shape) == 4, 'Inputs should be B*W*H*N tensors, instead have shape {}'.format(y_pred.shape)
flag_soft = (metric_type == 'soft')
flag_naive_mean = (metric_type == 'naive')
num_classes = y_pred.shape[-1]
# if only 1 class, there is no background class and it should never be dropped
drop_last = drop_last and num_classes>1
if not flag_soft:
if num_classes>1:
# get one-hot encoded masks from y_pred (true masks should already be in correct format, do it anyway)
y_pred = np.array([ np.argmax(y_pred, axis=-1)==i for i in range(num_classes) ]).transpose(1,2,3,0)
y_true = np.array([ np.argmax(y_true, axis=-1)==i for i in range(num_classes) ]).transpose(1,2,3,0)
else:
y_pred = (y_pred > 0).astype(int)
y_true = (y_true > 0).astype(int)
# intersection and union shapes are batch_size * n_classes (values = area in pixels)
axes = (1,2) # W,H axes of each image
intersection = np.sum(np.abs(y_pred * y_true), axis=axes) # or, np.logical_and(y_pred, y_true) for one-hot
mask_sum = np.sum(np.abs(y_true), axis=axes) + np.sum(np.abs(y_pred), axis=axes)
union = mask_sum - intersection # or, np.logical_or(y_pred, y_true) for one-hot
if verbose:
print('intersection (pred*true), intersection (pred&true), union (pred+true-inters), union (pred|true)')
print(intersection, np.sum(np.logical_and(y_pred, y_true), axis=axes), union, np.sum(np.logical_or(y_pred, y_true), axis=axes))
smooth = .001
iou = (intersection + smooth) / (union + smooth)
dice = 2*(intersection + smooth)/(mask_sum + smooth)
metric = {'iou': iou, 'dice': dice}[metric_name]
# define mask to be 0 when no pixels are present in either y_true or y_pred, 1 otherwise
mask = np.not_equal(union, 0).astype(int)
# mask = 1 - np.equal(union, 0).astype(int) # True = 1
if drop_last:
metric = metric[:,:-1]
mask = mask[:,:-1]
# return mean metrics: remaining axes are (batch, classes)
# if mean_per_class, average over batch axis only
# if flag_naive_mean, average over absent classes too
if mean_per_class:
if flag_naive_mean:
return np.mean(metric, axis=0)
else:
# mean only over non-absent classes in batch (still return 1 if class absent for whole batch)
return (np.sum(metric * mask, axis=0) + smooth)/(np.sum(mask, axis=0) + smooth)
else:
if flag_naive_mean:
return np.mean(metric)
else:
# mean only over non-absent classes
class_count = np.sum(mask, axis=0)
return np.mean(np.sum(metric * mask, axis=0)[class_count!=0]/(class_count[class_count!=0]))
def mean_iou_np(y_true, y_pred, **kwargs):
"""
Compute mean Intersection over Union of two segmentation masks, via numpy.
Calls metrics_np(y_true, y_pred, metric_name='iou'), see there for allowed kwargs.
"""
return metrics_np(y_true, y_pred, metric_name='iou', **kwargs)
def mean_dice_np(y_true, y_pred, **kwargs):
"""
Compute mean Dice coefficient of two segmentation masks, via numpy.
Calls metrics_np(y_true, y_pred, metric_name='dice'), see there for allowed kwargs.
"""
return metrics_np(y_true, y_pred, metric_name='dice', **kwargs)
# keras version
def seg_metrics(y_true, y_pred, metric_name, metric_type='standard', drop_last = True, mean_per_class=False, verbose=False):
"""
Compute mean metrics of two segmentation masks, via Keras.
IoU(A,B) = |A & B| / (| A U B|)
Dice(A,B) = 2*|A & B| / (|A| + |B|)
Args:
y_true: true masks, one-hot encoded.
y_pred: predicted masks, either softmax outputs, or one-hot encoded.
metric_name: metric to be computed, either 'iou' or 'dice'.
metric_type: one of 'standard' (default), 'soft', 'naive'.
In the standard version, y_pred is one-hot encoded and the mean
is taken only over classes that are present (in y_true or y_pred).
The 'soft' version of the metrics are computed without one-hot
encoding y_pred.
The 'naive' version return mean metrics where absent classes contribute
to the class mean as 1.0 (instead of being dropped from the mean).
drop_last = True: boolean flag to drop last class (usually reserved
for background class in semantic segmentation)
mean_per_class = False: return mean along batch axis for each class.
verbose = False: print intermediate results such as intersection, union
(as number of pixels).
Returns:
IoU/Dice of y_true and y_pred, as a float, unless mean_per_class == True
in which case it returns the per-class metric, averaged over the batch.
Inputs are B*W*H*N tensors, with
B = batch size,
W = width,
H = height,
N = number of classes
"""
flag_soft = (metric_type == 'soft')
flag_naive_mean = (metric_type == 'naive')
# always assume one or more classes
num_classes = K.shape(y_true)[-1]
if not flag_soft:
# get one-hot encoded masks from y_pred (true masks should already be one-hot)
y_pred = K.one_hot(K.argmax(y_pred), num_classes)
y_true = K.one_hot(K.argmax(y_true), num_classes)
# if already one-hot, could have skipped above command
# keras uses float32 instead of float64, would give error down (but numpy arrays or keras.to_categorical gives float64)
y_true = K.cast(y_true, 'float32')
y_pred = K.cast(y_pred, 'float32')
# intersection and union shapes are batch_size * n_classes (values = area in pixels)
axes = (1,2) # W,H axes of each image
intersection = K.sum(K.abs(y_true * y_pred), axis=axes)
mask_sum = K.sum(K.abs(y_true), axis=axes) + K.sum(K.abs(y_pred), axis=axes)
union = mask_sum - intersection # or, np.logical_or(y_pred, y_true) for one-hot
smooth = .001
iou = (intersection + smooth) / (union + smooth)
dice = 2 * (intersection + smooth)/(mask_sum + smooth)
metric = {'iou': iou, 'dice': dice}[metric_name]
# define mask to be 0 when no pixels are present in either y_true or y_pred, 1 otherwise
mask = K.cast(K.not_equal(union, 0), 'float32')
if drop_last:
metric = metric[:,:-1]
mask = mask[:,:-1]
if verbose:
print('intersection, union')
print(K.eval(intersection), K.eval(union))
print(K.eval(intersection/union))
# return mean metrics: remaining axes are (batch, classes)
if flag_naive_mean:
return K.mean(metric)
# take mean only over non-absent classes
class_count = K.sum(mask, axis=0)
non_zero = tf.greater(class_count, 0)
non_zero_sum = tf.boolean_mask(K.sum(metric * mask, axis=0), non_zero)
non_zero_count = tf.boolean_mask(class_count, non_zero)
if verbose:
print('Counts of inputs with class present, metrics for non-absent classes')
print(K.eval(class_count), K.eval(non_zero_sum / non_zero_count))
return K.mean(non_zero_sum / non_zero_count)
def mean_iou(y_true, y_pred, **kwargs):
"""
Compute mean Intersection over Union of two segmentation masks, via Keras.
Calls metrics_k(y_true, y_pred, metric_name='iou'), see there for allowed kwargs.
"""
return seg_metrics(y_true, y_pred, metric_name='iou', **kwargs)
def mean_dice(y_true, y_pred, **kwargs):
"""
Compute mean Dice coefficient of two segmentation masks, via Keras.
Calls metrics_k(y_true, y_pred, metric_name='iou'), see there for allowed kwargs.
"""
return seg_metrics(y_true, y_pred, metric_name='dice', **kwargs)
Display the source blob
Display the rendered blob
Raw
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt\n",
"import matplotlib as mpl\n",
"import numpy as np"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"plt.rcParams.update({'font.size': 13})"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"scrolled": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Using TensorFlow backend.\n"
]
}
],
"source": [
"import tensorflow as tf\n",
"from keras import backend as K"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Segmentation Metrics\n",
"\n",
"For each metric I implement a Numpy and a Keras version, and verify that they give the same results. Examples are input images with a squares and circles."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"def metrics_np(y_true, y_pred, metric_name, metric_type='standard', drop_last = True, mean_per_class=False, verbose=False):\n",
" \"\"\" \n",
" Compute mean metrics of two segmentation masks, via numpy.\n",
" \n",
" IoU(A,B) = |A & B| / (| A U B|)\n",
" Dice(A,B) = 2*|A & B| / (|A| + |B|)\n",
" \n",
" Args:\n",
" y_true: true masks, one-hot encoded.\n",
" y_pred: predicted masks, either softmax outputs, or one-hot encoded.\n",
" metric_name: metric to be computed, either 'iou' or 'dice'.\n",
" metric_type: one of 'standard' (default), 'soft', 'naive'.\n",
" In the standard version, y_pred is one-hot encoded and the mean\n",
" is taken only over classes that are present (in y_true or y_pred).\n",
" The 'soft' version of the metrics are computed without one-hot \n",
" encoding y_pred.\n",
" The 'naive' version return mean metrics where absent classes contribute\n",
" to the class mean as 1.0 (instead of being dropped from the mean).\n",
" drop_last = True: boolean flag to drop last class (usually reserved\n",
" for background class in semantic segmentation)\n",
" mean_per_class = False: return mean along batch axis for each class.\n",
" verbose = False: print intermediate results such as intersection, union\n",
" (as number of pixels).\n",
" Returns:\n",
" IoU/Dice of y_true and y_pred, as a float, unless mean_per_class == True\n",
" in which case it returns the per-class metric, averaged over the batch.\n",
" \n",
" Inputs are B*W*H*N tensors, with\n",
" B = batch size,\n",
" W = width,\n",
" H = height,\n",
" N = number of classes\n",
" \"\"\"\n",
" \n",
" assert y_true.shape == y_pred.shape, 'Input masks should be same shape, instead are {}, {}'.format(y_true.shape, y_pred.shape)\n",
" assert len(y_pred.shape) == 4, 'Inputs should be B*W*H*N tensors, instead have shape {}'.format(y_pred.shape)\n",
" \n",
" flag_soft = (metric_type == 'soft')\n",
" flag_naive_mean = (metric_type == 'naive')\n",
" \n",
" num_classes = y_pred.shape[-1]\n",
" # if only 1 class, there is no background class and it should never be dropped\n",
" drop_last = drop_last and num_classes>1\n",
" \n",
" if not flag_soft:\n",
" if num_classes>1:\n",
" # get one-hot encoded masks from y_pred (true masks should already be in correct format, do it anyway)\n",
" y_pred = np.array([ np.argmax(y_pred, axis=-1)==i for i in range(num_classes) ]).transpose(1,2,3,0)\n",
" y_true = np.array([ np.argmax(y_true, axis=-1)==i for i in range(num_classes) ]).transpose(1,2,3,0)\n",
" else:\n",
" y_pred = (y_pred > 0).astype(int)\n",
" y_true = (y_true > 0).astype(int)\n",
" \n",
" # intersection and union shapes are batch_size * n_classes (values = area in pixels)\n",
" axes = (1,2) # W,H axes of each image\n",
" intersection = np.sum(np.abs(y_pred * y_true), axis=axes) # or, np.logical_and(y_pred, y_true) for one-hot\n",
" mask_sum = np.sum(np.abs(y_true), axis=axes) + np.sum(np.abs(y_pred), axis=axes)\n",
" union = mask_sum - intersection # or, np.logical_or(y_pred, y_true) for one-hot\n",
" \n",
" if verbose:\n",
" print('intersection (pred*true), intersection (pred&true), union (pred+true-inters), union (pred|true)')\n",
" print(intersection, np.sum(np.logical_and(y_pred, y_true), axis=axes), union, np.sum(np.logical_or(y_pred, y_true), axis=axes))\n",
" \n",
" smooth = .001\n",
" iou = (intersection + smooth) / (union + smooth)\n",
" dice = 2*(intersection + smooth)/(mask_sum + smooth)\n",
" \n",
" metric = {'iou': iou, 'dice': dice}[metric_name]\n",
" \n",
" # define mask to be 0 when no pixels are present in either y_true or y_pred, 1 otherwise\n",
" mask = np.not_equal(union, 0).astype(int)\n",
" # mask = 1 - np.equal(union, 0).astype(int) # True = 1\n",
" \n",
" if drop_last:\n",
" metric = metric[:,:-1]\n",
" mask = mask[:,:-1]\n",
" \n",
" # return mean metrics: remaining axes are (batch, classes)\n",
" # if mean_per_class, average over batch axis only\n",
" # if flag_naive_mean, average over absent classes too\n",
" if mean_per_class:\n",
" if flag_naive_mean:\n",
" return np.mean(metric, axis=0)\n",
" else:\n",
" # mean only over non-absent classes in batch (still return 1 if class absent for whole batch)\n",
" return (np.sum(metric * mask, axis=0) + smooth)/(np.sum(mask, axis=0) + smooth)\n",
" else:\n",
" if flag_naive_mean:\n",
" return np.mean(metric)\n",
" else:\n",
" # mean only over non-absent classes\n",
" class_count = np.sum(mask, axis=0)\n",
" return np.mean(np.sum(metric * mask, axis=0)[class_count!=0]/(class_count[class_count!=0]))\n",
" \n",
"def mean_iou_np(y_true, y_pred, **kwargs):\n",
" \"\"\"\n",
" Compute mean Intersection over Union of two segmentation masks, via numpy.\n",
" \n",
" Calls metrics_np(y_true, y_pred, metric_name='iou'), see there for allowed kwargs.\n",
" \"\"\"\n",
" return metrics_np(y_true, y_pred, metric_name='iou', **kwargs)\n",
"\n",
"def mean_dice_np(y_true, y_pred, **kwargs):\n",
" \"\"\"\n",
" Compute mean Dice coefficient of two segmentation masks, via numpy.\n",
" \n",
" Calls metrics_np(y_true, y_pred, metric_name='dice'), see there for allowed kwargs.\n",
" \"\"\"\n",
" return metrics_np(y_true, y_pred, metric_name='dice', **kwargs)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"# keras version\n",
"def seg_metrics(y_true, y_pred, metric_name, metric_type='standard', drop_last = True, mean_per_class=False, verbose=False):\n",
" \"\"\" \n",
" Compute mean metrics of two segmentation masks, via Keras.\n",
" \n",
" IoU(A,B) = |A & B| / (| A U B|)\n",
" Dice(A,B) = 2*|A & B| / (|A| + |B|)\n",
" \n",
" Args:\n",
" y_true: true masks, one-hot encoded.\n",
" y_pred: predicted masks, either softmax outputs, or one-hot encoded.\n",
" metric_name: metric to be computed, either 'iou' or 'dice'.\n",
" metric_type: one of 'standard' (default), 'soft', 'naive'.\n",
" In the standard version, y_pred is one-hot encoded and the mean\n",
" is taken only over classes that are present (in y_true or y_pred).\n",
" The 'soft' version of the metrics are computed without one-hot \n",
" encoding y_pred.\n",
" The 'naive' version return mean metrics where absent classes contribute\n",
" to the class mean as 1.0 (instead of being dropped from the mean).\n",
" drop_last = True: boolean flag to drop last class (usually reserved\n",
" for background class in semantic segmentation)\n",
" mean_per_class = False: return mean along batch axis for each class.\n",
" verbose = False: print intermediate results such as intersection, union\n",
" (as number of pixels).\n",
" Returns:\n",
" IoU/Dice of y_true and y_pred, as a float, unless mean_per_class == True\n",
" in which case it returns the per-class metric, averaged over the batch.\n",
" \n",
" Inputs are B*W*H*N tensors, with\n",
" B = batch size,\n",
" W = width,\n",
" H = height,\n",
" N = number of classes\n",
" \"\"\"\n",
" \n",
" flag_soft = (metric_type == 'soft')\n",
" flag_naive_mean = (metric_type == 'naive')\n",
" \n",
" # always assume one or more classes\n",
" num_classes = K.shape(y_true)[-1]\n",
" \n",
" if not flag_soft:\n",
" # get one-hot encoded masks from y_pred (true masks should already be one-hot)\n",
" y_pred = K.one_hot(K.argmax(y_pred), num_classes)\n",
" y_true = K.one_hot(K.argmax(y_true), num_classes)\n",
"\n",
" # if already one-hot, could have skipped above command\n",
" # keras uses float32 instead of float64, would give error down (but numpy arrays or keras.to_categorical gives float64)\n",
" y_true = K.cast(y_true, 'float32')\n",
" y_pred = K.cast(y_pred, 'float32')\n",
"\n",
" # intersection and union shapes are batch_size * n_classes (values = area in pixels)\n",
" axes = (1,2) # W,H axes of each image\n",
" intersection = K.sum(K.abs(y_true * y_pred), axis=axes)\n",
" mask_sum = K.sum(K.abs(y_true), axis=axes) + K.sum(K.abs(y_pred), axis=axes)\n",
" union = mask_sum - intersection # or, np.logical_or(y_pred, y_true) for one-hot\n",
"\n",
" smooth = .001\n",
" iou = (intersection + smooth) / (union + smooth)\n",
" dice = 2 * (intersection + smooth)/(mask_sum + smooth)\n",
"\n",
" metric = {'iou': iou, 'dice': dice}[metric_name]\n",
"\n",
" # define mask to be 0 when no pixels are present in either y_true or y_pred, 1 otherwise\n",
" mask = K.cast(K.not_equal(union, 0), 'float32')\n",
" \n",
" if drop_last:\n",
" metric = metric[:,:-1]\n",
" mask = mask[:,:-1]\n",
" \n",
" if verbose:\n",
" print('intersection, union')\n",
" print(K.eval(intersection), K.eval(union))\n",
" print(K.eval(intersection/union))\n",
" \n",
" # return mean metrics: remaining axes are (batch, classes)\n",
" if flag_naive_mean:\n",
" return K.mean(metric)\n",
"\n",
" # take mean only over non-absent classes\n",
" class_count = K.sum(mask, axis=0)\n",
" non_zero = tf.greater(class_count, 0)\n",
" non_zero_sum = tf.boolean_mask(K.sum(metric * mask, axis=0), non_zero)\n",
" non_zero_count = tf.boolean_mask(class_count, non_zero)\n",
" \n",
" if verbose:\n",
" print('Counts of inputs with class present, metrics for non-absent classes')\n",
" print(K.eval(class_count), K.eval(non_zero_sum / non_zero_count))\n",
" \n",
" return K.mean(non_zero_sum / non_zero_count)\n",
"\n",
"def mean_iou(y_true, y_pred, **kwargs):\n",
" \"\"\"\n",
" Compute mean Intersection over Union of two segmentation masks, via Keras.\n",
"\n",
" Calls metrics_k(y_true, y_pred, metric_name='iou'), see there for allowed kwargs.\n",
" \"\"\"\n",
" return seg_metrics(y_true, y_pred, metric_name='iou', **kwargs)\n",
"\n",
"def mean_dice(y_true, y_pred, **kwargs):\n",
" \"\"\"\n",
" Compute mean Dice coefficient of two segmentation masks, via Keras.\n",
"\n",
" Calls metrics_k(y_true, y_pred, metric_name='iou'), see there for allowed kwargs.\n",
" \"\"\"\n",
" return seg_metrics(y_true, y_pred, metric_name='dice', **kwargs)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Input images\n",
"I will build simple geometrical figures and use those as \"objects\" for assessing segmentation metrics. For example, see below how to generate circles and diamonds with numpy"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 936x288 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"x,y = np.meshgrid(np.arange(-7,7.1), np.arange(-7,7.1))\n",
"\n",
"fig, (ax1, ax2) = plt.subplots(1,2,figsize = (13,4))\n",
"\n",
"# ax1.contourf(x, y, circle, alpha=0.5)\n",
"# # ax1.scatter(x, y, circle)\n",
"# for i in range(len(x)):\n",
"# for j in range(len(y)):\n",
"# ax1.text(x[i][j], y[i][j], '%d'% circle[i][j], ha='center', va='center')\n",
"\n",
"circle_fuzzy = np.minimum([1], np.maximum([0], 25-x**2-y**2)/20)\n",
"\n",
"ax1.contourf(x, y, circle_fuzzy, alpha=0.4, vmin=0, vmax=1)\n",
"# ax1.scatter(x, y, circle_fuzzy)\n",
"for i in range(len(x)):\n",
" for j in range(len(y)):\n",
" fmt = '%d' if circle_fuzzy[i][j] %1 ==0 else '%1.1f'\n",
" ax1.text(x[i][j], y[i][j], fmt % circle_fuzzy[i][j] , ha='center', va='center')\n",
"\n",
"diamond = np.minimum([1], np.maximum([0], (3 - abs(x)) + (3 - abs(y)))/3)\n",
"\n",
"ax2.contourf(x,y,diamond, alpha=0.4, vmin=0, vmax=1)\n",
"for i in range(len(x)):\n",
" for j in range(len(y)):\n",
" fmt = '%d' if diamond[i][j] %1 ==0 else '%1.1f'\n",
" ax2.text(x[i][j], y[i][j], fmt % diamond[i][j] , ha='center', va='center')\n",
"\n",
"for ax in (ax1, ax2): ax.set_axis_off()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Segmentation masks - for now only do object (circle, diamonds), will add background later on. Truth value mask is zero/one outside/inside of object. Predicted mask has continuous values."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"def fuzzy_circle(xy=(0,0), r=4, fuzz_factor=0.8):\n",
" x0, y0 = xy\n",
" max_fuzz = fuzz_factor * r**2\n",
" circle = np.minimum([1], np.maximum([0], r**2 - (x-x0)**2 - (y-y0)**2)/max_fuzz)\n",
" \n",
" return circle"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"def fuzzy_diamond(xy=(0,0), r=2, fuzz_factor=1.5):\n",
" x0, y0 = xy\n",
" max_fuzz = fuzz_factor * r\n",
" diamond = np.minimum([1], np.maximum([0], (r - abs(x-x0)) + (r-abs(y-y0)))/max_fuzz)\n",
" \n",
" return diamond"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fine_grid = np.meshgrid(np.arange(-7,7.1,0.05), np.arange(-7,7.1,0.05))\n",
"x,y = fine_grid\n",
"\n",
"zz = fuzzy_circle((2,0), r=3, fuzz_factor=0.1)\n",
"plt.contour(x, y, zz, levels = [0.99], colors='b')\n",
"zz = fuzzy_circle((1,1))\n",
"plt.contourf(x, y, zz, alpha=0.5, levels=[0,0.25,0.5,0.75,0.99,1.25], cmap = 'gray_r')\n",
"zz = fuzzy_diamond(xy=(-3.5,-3.5))\n",
"plt.contourf(x, y, zz, alpha=0.5, levels=[0,0.25,0.5,0.75,0.99,1.25], cmap = 'gray_r')\n",
"plt.gca().set_aspect(1)\n",
"plt.gca().set_axis_off()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Compute IoU and Dice metrics for series of two overlapping circles"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" explicit np function\n",
"IoU 1.00 1.00 \n",
"Dice 1.00 1.00 \n",
"IoU 0.52 0.52 \n",
"Dice 0.69 0.69 \n",
"IoU 0.25 0.25 \n",
"Dice 0.40 0.40 \n"
]
},
{
"data": {
"image/png": 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kEV8sRd2wxcgR2KsPUdiC4k2gb4X35wN5wDRglPNcA+Aedg5FrMXOkwOAi4C52CEJdwNXArnAbHY9IVmqJwIqnDn5PxCb/yey5wK/BFjFzvyvmPvbgOJKfLtods350OdAbaAJ0Jw9XzFYjG3/vwQmGVOpzxpVjbTo9zgR0oFT2NnQJ9zPfVzGmzRn9W6v30rqyg85/x2ABPJiujKvQx6JudtJysshJS+H5IIg/v3+0v2USgo5CSlkJwURs5KWGwHqk5F6HOPPTCC/vg/jr/i+c/iYTzkn9HAbMBZ7AvCd9gIodWBEqAMMYWdDn1jucKjAX1buthbbe3e4BbANf2vsyUfrbvzW7BlGBPoylZg91xUjgOXOv7sBCex6YlKZYgTsSUOoIFmC7bUEuBR7UpS2h/f8ijF9Kvn1lfIEEdKAk7H5fxKQVO5wqMAvn/9rqL78b0a5/Gf3E4Ec4Bts+/+NMWRXQ1xqP7To9yBnXN6lwPl1yOw3hK9lLKeQRRp12LLqQ86TY5nYrAxfcR6JGdmkZGwhLWMDjTLW0zDLVMOiTH5KfU1Yl96QDQ3SyGpQi5wG8RTU/RcPvPcDJzRcSptOt/NUg8as4wtO53tOKMkl+UfgXeAjYyg87EEqFYZESAYuBoYB/dl1lbWVwHTskJcVVE8Dv5s4Cnx38GS7NLLib+L5WfXJiLqSUW0f4d7HADKoX7aEtv5ltGIZrVlJi7IYiud3YOFPV/Pqz3XIqmyBfzBSsEVI+YJkPPAEsBGIA97GFiNfAAt1joDyChGSsFewLgCOZtf8XwX8is3/ZbiU/3sRANoARzq3JuWOlQKTgPeA942hoPrDU6BFv6eI0BW4IZ78i4fxQfzlvEE/fsFPkDe4bMZqmv1cl835DVlfO5bCmJW0yKiOAr+yfJTJzqsIhst589Y4ChMAiohmAscyiqv4gtOzSoh+DXjRGFa4GLJSniFCZ+B64BJ29uiXAvOxDf2vQKY70VkjGNnyKkad1Jpl/WIoTs4jYeMzjHi2DlsSALoyt/VyWq7LI6kwi7S0DTRss5XabQuIb2IQH0CA0rzmrPrhDEZ/O5BJGdUUug87dhnsldPh5Y6tAD4AXsKYNdUUj1K7EKEjNv8vZWePfhm75v9md6I7KA3YeQLQkZ0nL9nAG8D/jGGJO6HVXFr0u8wZp3cOcEMblvS7nv9xOW+Q6lwJCyLBbFJW/kGH6fPpsnyfX8xTDI1Zn9aGJe0asLFNMtubiJ2YxwYacAP/ZQxnGOz4v/8DvjXGU70WSh12IkQDZwE3AAPKHVqAzY0ZwA4XQvtTfTKinmHE0YMZPySdLe1CzxcSs20TdZd8zwnjSona53j5AuLj1tC0XQYNeu4grmHo+TSyZh/Jr19fxaiZMRRX15j7WOzwoj5AbyDZeT4IjAbO1fH/qjo4+X8GNv8Hljv0Bzb/f4WI6BVPxObbSUC7cs+Pw7b/X+n8v+qhRb9LnEv4I4C/AukA0zkyeCQzfADbSVq3jFaz5tNlUQHxh/NSeLWoRU5cV+Z2asHKXvHsqHMFo+a+wyVHlBIVVZsstpG62uB7GnjZmEqP7VUqLImQCNwC3AjUdZ7eAUzAjoP1TI/zS1zb51pe+SdAKf7C9TSas4COc9bQNNM5jz8gGTRouJpmPbNJ6RTEFwCIoSizI7+PHsEz36SQU52Nvw/ogC1G+gM/AWdjTA4iPiAFY7ZWYzyqBhAhAbjJudV3nt4B/IjN/90n7EWOlth5SgPBWTMc1gHPAP/Vob+Hlxb91czp2b8uiuJ7rmJUna8ZQgHxq/swfdo/eSSmKaubzafzzNDE2chjaMOyRktpvT6HlJjvOaHtQ9w7JJVt0Q9wH59wzsog/nux4/60t01FFKdn71rgXnYW+6uAr7EFp6u9+gDX899mxzCx9bW8PPEkvk3vxYz6V/DGJetovGgWPRYUEVslRfkO4uKW0brrJur1LCa6NkAshZt7M+Pdm3nup2rs+Q+phR3vvxE7ubgpdhWh14CH/tw7QKmDJEIUcDXwL3YW+2uw+f8jHsj/apSAXY1wCNDIeW4dcB/wlvb8Hx5a9FcTEfzART7KHjyXj5s9zD20ZjkLab94Isd85HZ8bkkgL+Z8Pro+huIkgJn05C4eYxzHzwPuws761z9SFdacnTCHAQ9he7rALm/3NjDPrbjKO4tP6z7K3Re2Zckgg5R9wtkvZFGn6jbg2guDyEpatFlF80GFxNYFSCB/9UB+eutaXp7hd+/c/3zshEqwQyyeAZ7CmBy3AlLhycn/c4GH2bnU5lJs/s9xKy4P6YWdy9TCebwQuwTvGG3/q5YW/YeZCAKcIgQfO54fOj3GXfTgNwAKiM2aT5fxs+mx2N0o3eWn1HcU07q1Z/HAaEoSAcYxmLt4jJn0ngTcaQxTXQ5TqQPm5P9JwGNAV+fptcBb2FV4XNePKckv8LfzujBviJ9gIIgE19Fo5mQGTMqhVrX1PAbx+ZbSptMamg4sISoFoBY5C4fw9RsX8v7C6oqjgqbYYiS03OdW4FHg/zBGhyGofXLy/3hs/vdwnl6PLfZ/cSsujxLsvKZLgHrOc9Ow7f9PrkUVYbToP4xEaAS83JU5Q55hBIOYCEAR0bmLaPfTdPrMKSOgvwBHDIWB/kzp04oV/QOUxRQQRyPWk00q2EvstxqD9rKpsCBCA+BF7BrbAFuwS9ZNAPeHrtUmKzCGoeccxbQzA5TFGTCbqLdgKkdN3EgD19bULiXgX0T7nutpNKAMfzxAI9ZPuJ2nXmnFinyXwmoHXAZ0ch7fhzEPuhSLCgMi1AP+i52oD/aE8T3s5FXX89/DAtg9Sc7HLr8L9v/tJmPIci2qCKFF/2HgnN1fBjwL1BrATwUTGRRfhr9wOa0m/0K/GUXE6Hi1vUhie2x/pvQvIjZ4Ja/lzqD3caUEovyUrSsjcI0xfOt2jErtjZP/FwH/wW4iVYBdEvJrKr8B1WHnp5T1NHq0Hps7ZVF72a8cOWEVzTe5HVdIETExf3DEUZuo1y+ILxBN8dbj+eGF63hppoth9QTOBu4ApmPMVkT8GKMrjyngz/w/H3gBu1HcDuAj7C61nsn/MBCLXdnobOyE303AcGMY42pUYU6L/ioW6t1vzdIhy2hDC1bMuZw3xh3HuEbLabk2j6Si/X4RtYtVNE8uJmrY+XxU73LeYDHttddfedIeevdnYRt/T/RQtWJZbEtWxM+nc+5lvNm8FzOappGVsogOnlktqKIs0tLm0/m0AuKbgCd6/UMMkAG8il13/Hld6rNm20Pv/hzsyf8W14IKfw2wqxx1dB5rr/8h0KK/ioR69+PJf/Yh7q11C8/yIsN/ErsLnToEQpCLeffaRPLrFRLDvTzEM4zQXn/lGXvp3X8FuxOsJzzKXZ1u5PmbC4nNfo8LPoii1Ds7++1HEJ/vdzoeuZ5GgzzU6w922cG/O/+eDFyBMctcjEe5YC+9+6OA792MK4IIdlO9y9Be/0OiRX8VcNbcf6sfU4a+zhW0ZSlBxCym7biJDJrmdnyRIJHcmMFMOKEhGd0AptEHp9f/v8AtxlDicoiqhnLW3H8DexkaPNi7/ylnX9qVeacC5JGw8UtOfTeb1LDb9Kdir39T1nz7CP98uZrX9q/oSOx+K6nYYu8utNe/xnDW3H8VuzoXaO/+4VSx1/914AZd27/ytOg/RCK0FoJf3M5THR7jLnwY8onLnMLRY5bTqrq2mK8xujCvVS9mnhZDcdIOYrmKUbzPhT8B5xpDptvxqZpFhBbAGKAzHuzdv4S3Gr7A3+5NJrdREAmuoOXPExg0uYxA2BakFXv9a5Gz8B888VhnFrg2+Ri74+g1wCDn8XfAMIxxMyZ1mInQDLuLcze0d7+6VOz1nw6caQxab1WCFv2HQITj4ij46BWuSb2I9wBYSfMp4zn2pxKidWLXYZJAXszx/HBSAzZ1WUWzYAcW+gqJWwUMNcYba56ryCfCMcAn2Mv567Fr8G9wM6byRjKi+9944Y4oShPyicucxF8+X0ULz0zUPVQbqd9gPp3PLyEqKYaiLZfw9sND+WKFy2Edie2JTAYuxZi3XY5HHSYiHA18BqRj53Y8hN1cSlWPFsA92P//DcAZxjDD3ZC8T4v+g+CM37sRGJnOZv98OhenkcVMen3+G92XuB1fzWAYwORec+i66VaeGZBJ3VZAPnCpMXzmdnQqsolwPfYSfgA7nOcpbE+/Z/xC3wv7Mm1YJnUWjWXI6B3ER9wQuDwSE2fS69wC4hv7CBYP4sdnb+G5yS6HVRe73vgzwHwd5hN5RLgG+D8gCjuc5wls+6OqVy3gTuxwn0LgKmOcHli1R1r0HyARYrDJfhUY+jL1m3t4aC2IWU2zzW7HVxMVEx14ieHHnMLYvp9zJstpfR/wsDG6FrKqWiJEA88B1zlPfYrdaMdTf2sdWRB/Ba+1HcikbjPp+bshbObsHrBSAv6Z9DplK7W7ArRn0YePcvd7UZR6oXHbCuQCxwLPoQ1uWBMhChgJ/M15agx2XLmn8r+GCQDDsWv7gz0B+6cx6GiLPdCi/wCIkAR8eQlvDTyCP8pm0fO9Y5mwyu24FPRi5hG9mXn2VlI5j48Yz3HvApcbg+6HoKqEM2FvNHAcUAI8D86Oex4wiAkpr3HlDf/hxg9asCo1EEar81SF+XTus47GxxnEV5+Nk59hxL8TyXe74Q9grwg1xp4cXqs7+YYnEeKxJ/knYfP/v3ho/o5iCHAt4AM+By4wBl0ivQIt+itJhBTgm7/z9FFPczsAPzD4tWW0We9uZAognoLoUxh7Zh2y2pYQ4ELe4xPO/RS40BjdEEUdGmeFrrHA0UA2dvzuUleDKucMPk9/m0seSSS/fiZ1Fn3COR+7HZMbVtOs5SLan1OGP6Yum6f/m78/4fLKPgD9gBHYSYfjgaEYo0NBwoizQteXwDFADvAIsMjNmNQedcEO90nEboZ4tq7ss6sa1RN0sERIA8b9k4f/LPgX0PEbLfi9o4D44k84+8NVNJ0aRSkfMIyLeOds4FMRYt2OT4Uv54T/e2zBn4VtVDxT8F/Aew3e5aInEsmvn0vihnEMHut2TG5pxuoVXZj3jp+yHZup2+cWnr1nC2nRLof1C3A79mRxMPANIsnuhqQqyznh/w5b8G/FLsmqBb83zcNO7t2O7fn/yrlCqxza078fIqQIwQkP8q/u9/AIBswcun0xjaN0lRhPMpzADwNbseIvQYSreZXXuXIscJb2+KsD5TT444De2A1h7nHuPeEy3mj0P65/JI7C2ttJWjuaoe/nk1jjL2lvpm693+h+cRn++DSyfnuWWx7yQI9/Q2wPcRp2mcETMUZ3FfcwZ0jvd0Bf7Lr7/wRdGjIMNAUeBlKACcCpxrDD3ZC8QYv+fXAa/O/v5cE+D3IfQcTMpvvnMzjyd7djU/s2mPFHt2XpoBU0Nx35QwqJ+ww4X8f4q8pyLul/C/THFvp34aENd87ng/qvc8XjcRTWzqbW6tEMfT8SV+g5WJmkp8+mx6Vl+OPTyfz1WW55PJlct/O/HrbwrwtcgjHvuByP2gtnDP/X2F2Xt2Dz3zMn/Gq/GgGPYjfN+wa7ln+N7xDR4T174ST82GRy+lzDK0EDZhY9PtOCPzyMZ/DkeXQe+wnnvmWQfOAs4C0R/ZtX++cMCfsCW/Bvwfbwe6bgB7iGV/rFUVg7h+TVn3Hme1rw7yqdzMzu/PaOn7IdmaQfeSsj/15EtNv5Hzp5fB6Y5HIsai+cVfpGYwv+rdgefi34w8t64F7sUJ+TgQ9FCLgbkvvc/gD0JGcd/teAo8vwZ43h9FEz6P3JTHr/4XZsqvKm0H92ErlrrmLUW1EU76jHxguAB9yOS3mbk/8vY3dX3YZHG/xPOWvmXLp8OZozPigi1u0ebE+qy+ZNXZj3np+yok3U638Xj13idkxAJvAD0AWRxojUQaTGFyNe4eT/f4HjsfMwdEhP+FqDLfzzgKHY/VRqNB3eswci3F2HzEe2k7zjZp57pQUrddxlGPNRJl2Zd1p7FnU9imkspv0wY/jQ7biUN4lwG7ZxKMJOwFzlakDl1CYr0Jh1MZ2Zn9yfKY3djidcrKVJswV0utggvr8waeTtPD3R7ZgcLYALFDYhAAAgAElEQVQ7gI8w5la3g1Egwk3YvTiKgX8Ay92NSFWBDthhdQHsBl6vuRyPa7SnvwIRhrZgxSML6cCPHLOsFcu2ux2TOjQGMa1YHp1CDmMYSipbXxehh9txKe8R4WTgSefhM3io4AeYyDHXTKH/M+fzYVe3YwknTVi7uilrvgOYzNE3fsLZbdyOyRGLHec/ApEr3A6mphPhOOzmW2ALfy34I8NC4H/Ov18Uob+bwbhJi/5yROiUxPZ3xjCUOmTRjiVxbsekDp3Bx1ecMiaP+E3tWMJ7XBjnp3SMCPXcjk15hwjtgQ8AAd7DLrXoGV9w2smdWXByPAXpsezQZWgPUEd+n5lG1qwgvqj3ueCfv9GtttsxUaEYQaSvm8HUZCK0Bj4C/M79z+5GpKrYD8BXQBTwmQhNXI7HFVr0O0RI81E25i0uTezMAgqI2zKWIZ8F8ev4pwhQSFzJd5z4YQmBgpP4jse5szE28WPcjk25z1mLfwyQjC32PTX861Hu6nQKY68FmEPXL3WPkIPTk1nfJZC/upjo2k9z291bSY1yOyZ2FiPRwOeI6LCtauas1PcFdqWXX4F33Y1IHSavAnOxq2eNdhZsqVG06AecGd0f3c/9Lc9gDKX4C3/g+A91vevIspl6OZPp/3EQCd7Gv7mUN/sB/+d2XMpdzopO7wNtscN5ngU8c7J/Dh/Xu5WRd/kw/lU0mzqdo+a7HVO4ClBa1ouZn0RRkr2d5LZ38+jfyrzRDI7CbixUDxiNSI0rRtzi5P872HHfa4B/46H8V1UqiB2+mQH0AF5zJm7XGJ74tPOAW0/ji2Pv5WEMmKn0/WQDDbe6HZSqeovosGYBnb4BeID7TCw7rhLhLLfjUq66CTgJu7Tbw+CdbdtrkxV4mWvvjqE4aSupy7/jhPFuxxTuEsgv6Mrcj3wEi9fTaNB/uOlot2MCyoAngI1AT+Bcd8OpUa4DTsOu8PIw6CZOES6Xnb/n84GL3Q2netX4ol+EDkLwwfu5H4DFtBu/gE4r3Y1KHU5T6D97Dl2/uItHvygkDuB/ItRxOy5V/URog93ABeA/wGYXw9nNM4zom0p2i0Jito1lyKc63LBq1GXzpqas+QHgZwZcP59OKW7HxM5i5F/Axy7HUiOI0IKdE/dfwJ50qci3FrssM8B/RGjoZjDVqUYX/c6wnjcMvphbGTltKa0nTmTgVLfjUoffVPrOHcDk+Y1Ytwg7vu95t2NS1cu5rP8aEAf8iB3L6yk38Z9fpnLUB7/Qd0weSTrcsAodwR+zE8lbUUJU0nPcfJ1HhvmsAeYA3dwOJNI5+T8KSAAm47GJ++qwGw/MBFKAl2rKMB9PfMq56FbgyATytpzC2InjOO5nU+P/S2oOwZjhvPj9k9xW1pU5w3SYT41zE3A0dgOuV1yOZY+u4PVmc+i2dDHt17odSyTqwryvfASLN1Gvn0eG+YSkIXIOIg+5HUgEuw67AV8O8KLLsSh3vADkA6dSQ4b51NjNuUTokMaW34bzYswW0t/oxhxtVGug4xg3oA3LjplDV/owfXMxMR2NYYvbcanDyxnWMxfby/8wHuvl/4wzT1hK64wk8nRpzsPsD47osYrmp0RRkvsA9/21Mwuy3Y4JSMRehYoFTseYL12OJ6I4w3rmY3v5H0d7+WuywcDN2N2XOxrDBpfjOaxqZLd2aFjPs9wS8wj3ch8P6EY3NdRk+k8rJGZbN+ZyF4/pMJ8aQAQ/8DoeHdYzgpEthzLmhtv494N1yExyO55I59FhPnnsXDbyJURS3QwmkpQb1qfDehTUsGE+nvh0c8Hw0/jiyIt5lzJ8pb/Se4rbASl3FBJXMo0+XwL8k0fowtxhIpzodlzqsLoS6I8Hh/XUItv/Lx68xYfxraPxrC2k57odU01QfpjPy1zb2+14HF9iN+9qgN0dWlWNS4Bj0GE9aqfyw3wiephvjSv6RUiMovhfT3MbAItpNyGDhttcDku5aCFHrF5L4xlRlPIkdwA87vQGqQjjbMbygPPwVWyPqme8yWXHp5DTvJCYbeM5doLb8dQUKWTnNGL9RICfGHhZEdFeyP8g8BxQAlyGSE+X4wl7IsQCoXkSr2GX6VUu8VNKbbICocd38ESbFbS4cw1N7l1Pw4fW0/DB1TS9Zzkt71hEu1vO54P6hymUrcDbzr8fcUaDRCQvfLBVtxFX8HrdtixlB7FbJ9N/htsBKfdNYsBPpfiLTuR7BjGhG3Ce2zGpw+JGbM/pcuylfc9oyuroE/h+GMA8Oo8vJK7E7Zhqkg4snBlFSXY+CU2f4+aBbsfj2IDdrRd2Li2rDt71QBPsJnw/uRtKzXMyX6e9ylX9ptHnknU0eiCfhHdHM/TsgUxMuYw3Gg1kYrsWrOrXhHW9G5LRtSEZ3Zqy9siWrDy6HUuObcGK5H5MSa5Ftv83ul2zimb/nMSAYc/zt15HMfVQh0J+h920qx1wxaH/tN5UoybyipAeT/7ypbRJakgGM+j16Ux6/eF2XMobBjP+6LYsHfQCf+VGXlgOHGEMxW7HpaqGCKnACuzYzX9hl0b0jAkMOmcQEy/NJSHjXS56VVcSq36LaddlOa2GxlK4+RWuuS6FnFK3YwKSsMPQSoBOGLPO5XjCkgi1sCf7acCD2HHcqhq8wF97n8HoUxuxoXvFYxupN/9zzhwNUJusxBasbFJGoLSUQCkYCVAaCFAW8FPmn06fP3cjv5w3boijMC30OIiUraTFL+9x4dgHuO+PsoPrrB8A3I492W5tTORt1FbTiv6RVzJqxCiuRhtWVVEsO6Jas6z5UMb8ZRtpDYEbjOF/bselqoYIjwP/wK7ac6/L4eyiNlmB9TQaFUtR6iSOfud33SDQFUF8vokcc00hsXV7MPuVB7jfK6vmdAGmYMxEtwMJVyI8iM3734G7XA6nRllL4381Zn2vIFKaTcrqbFIyMqmTsY7GGZupm8NBzJ1txLq0RqxvUIesBilkN0xmexNxvtAkBrwzkEkfHUSogp0/0xL4hzF/btwWMWpM0S9CM2AJBKPHMPSbRPI26drXak8m8ZdmH3DBpdjdGVsbQ77bMalDI0IjYBl2CcS/A0vdjWh3d/Nw20t456QfGaQbBLpoBS3bLKL9sChKtj/Hzdc0YZ2XevsmYUyO20GEGxHqYXv5E4A7gEXuRhS57uXB9pfy1qmjOeOHKfTf0IuZ9fryS9sGbGw0ix5zckkuPBzfN53M5O7M7tGEdd0/4rwX3+LSBRM4duvdPNo+gwb5r3JNZeu97th5X9lAS2OIqDmfNanofwO4rB2Lpt3Mcz+4HY/ytk845zIfwabjOP4eY3jE7XjUoRHhZeAaYArwhMvh7CaOAt9D3NslnoIot2NRMJFjLi8gvkkHFn7wJP94z+14ysnGjjl+A2O8MPQoLIjwAvBXYDro5/nh0IW5CR9y/tXtWTwYYDN1Fn7KOZ9Udxx+SqWMgAEoIK7kKkZdnUxu4+n0+fhUvvpwK2mVyZuHsVfXnjCGOw9rwNWsRoxtEaFxKlsvacLqsnP4RHvR1D61ZlnDHzi+yetcQRTFI5wVH1SYEqE+dmJWEHjH5XB2cyWjGp/NJ/W14PeONiwdD7CUNqdtpF6M2/GU8zx2fP9FbgcSLkSogz3hN+xcoUVVoee4qedU+r7QnsWDg0jZCppPnsTA792IJVTwA9QiJzafhCzBSF+mnb+cViNHMLJlJb7Mm879X0VIPjyRuqNGFP3AtcN5ybeSlv6LebuL28Eob1tOyw2FxGY1Zj2n80UacK7bMalDcjUQAKYB612OZRcd+CPuJYaPfJnhj8aTH+12PMpqxPq18RSsKyWQ8DpX/MXteMqZ6Nz/zc0gwsyVQDR24u4al2OJKB1ZEP8HHW66iefvi2dH2naS1o3llJe+46QfM0l3fTnUEqLLvmDoVxMZ+NYOYralkNP8aW4bOYV+F5ZfKnQPlgILsDtjX1w90VaPiC/6RYj2U3rNcF7CT5At1NnkdkzK2ww+VtJiJsAN/BfsZWEVhpz1loc7D792M5Y9eZI7BgUoiy0iJreABF0pykMakDETYB5dTvHILr0APwO5QC9EvLKJmGc5u29f5zz0XP6HuxasjG/Nsv5BpGwR7ca9z7A31tE4y+24KlpEhzUfMOylNTT51Yfx9WPqsIV0eNjPPkf6hP5eboikXXo980l2GJ15Et/Wb85qdhCzbQGdlrsdkPK+WfSYV4av5Fh+pD0L+4igG+OEp9OAxtge/nkux7ILP6UM4OchAEtoo/uFeEwrli/0U1aQR2LLTzm7ndvxOIqBcc6/b3AzkDBxEtAC2ATMdjmWiNKU1dHdmJM2k15jxnLKSz8yaGoQv2cniRYSVzKWU777kYFv7iBm2zJaz0skz7+Pt0zD7treEbuUZ0SoCUX/DU5vLatoPlOX6FSVkU9i0Ubqzwe43q7aeb27EamDFCqMPNfL9wD3darF9qbFROXNodtit+NRuwpQWlqHLXMAJnDsELfjKecb534YImn7fKUqn/+eLUjDRTRF8hvdrh3LkFNv4dmODdmQPIduS73Yu783i+iw5n0ueHEuXZbeyeNH1Ccj6ni+r72Hl5ZiN+yCCDrBjugKWIROLVn+l5P4ljJ8pbPpMdftmFT4mEfnmQCX8SaJ5F7obO6kwoQI7YDjgCJggsvh7GYYHwwBWEuT2WUEgm7Ho3bXkhWzBGMyaDBgEe0OdcfPqrIRmIVdfvZyd0PxLhFaAidjNzUbt5+Xq/1IJNc3n863dGPuqSfy3ZUN2FDL7ZgOVhGxpSDUIif2Va464WuGvDiRgeft4aXfYReAONtZECLsRXTRD1w/nJfwYdhM3d+3U8tL6y0rj1tFi03bSFn5EwO3xVMQB1zmdkzqgITG8v4E3tprYTDjUluwsq8BM5vuOuzAo1LZlp1I3rIgvsA7XHy82/GU8zV2suEql+PwsuHYzZZC8yDUQYqmSGbQ+8a2LB1Uhq9kCv3f30ZtT32mHqwGZNT3UxYzkEkXj2PwWRUOZ2GXeQ1gF4QIexFb9IsQBVzYjNUEkeB8OumW2+qAfcD573zAsDGbqQda9IcNZwJfaNWFb/b1WjecwtjmxUTnbqHO4i2ka0HiYY1ZNwtgKW2OdTuWcmZgN5nTHuw9EMEHXOI89Fz+hxM/pUynz9XtWTy4DF/JRAa++zsdV7kdV1WZzlHzZtN9tAEzmAmXf84ZJ1Z4Sejv57JImNAbsUU/cDSQcj3/W/cOFz29nNYb3A5IhSOhD9M3RFNUCHQToanbEalKOQqoA2Rgd+L0lFt55rfHuPPRrzl5jNuxqH1ryprlfsoKC4hvOokBXrvE39DtADyqJ9AA2ALofJlDMIFjz+vG3NOCSNkv9PtwCe0qu7Nt2PiVPgt+p+O3AEMZc8MoruxX7vA87JWi1kB7N+KrSpFc9J8O0JYlf+STWOR2MCp8pbI18Hee3jDYdqqd6nY8qlJOd+5/dTWKvahFtr8umUm6TKf3+SkLJpG7DGA8g490O54KBiByOyJh3wNZxUL5P93VKMJcb35N6svUswyYmfT6bAGdVrod0+HyMwNmLqHNjwJyCW/feDJfhybJB7FX1sCuBhfWIrLoF0GE4NBezKA3v0bsH6mqHp2Z3/5R7mn5d/4NOxsT5W2ebfQv4L0Gw3i/oQ+duxsu6rBlKcBKWnip6BfgCeBJoLPLsXiNZ/M/nMzgyNxRXDlyLl2+mkXPRW7Hc7iN59jJW6i9xE9Z7Ll8XL5XP9R5FPbtf0QW/cAR7VnUYgZH8jh3naUrdalDsZAOSw1wLBNIYvuxkbYtd6QRoQ32Mmw+sNDlcHbzOHde/T9ueKE7v7V1OxZVOU1Yu0wwwWxSOi2nZYLb8TgMEVSMVBURmgFdgB3YXVXVQTqe72sbfGYq/ea4HUv1EMYzeOzXDHnl3/z9t3IHfsMu4dlPhHSXgqsSkVr0n3Y6XwCwneQNhP/cC+WiHFIKtpO0NoZijueHKOAEt2NS+xS6BDsTKHMzkIpasCKmEeu7Cshqmuo8ozARx47CeArWGMQ3mjO8tFFfqCdbi/6dQvk/G/a95aras7t5pN2XnDrkZL5u5nYs1W0raXlraZJ5Ae+3jKYoVDzuwI7tF+AU96I7dJFa9J8eKvrX0FQn8ahDlkGDJQDO35U2sN7m2Uv7/+CJbn6C0bkkrt9KWp7b8ajKq83WJQCLaN/H7VjKmYvdh6I3Ijqp1/L0fB6va8S66H/wxC2nMva6wUzw0nC2alWbrLgvOfX0xbQd4bfnjhFxVS3iin4R0tLZfNRRTCOIlC2k/Qq3Y1LhbyltFwOcwlh8lA2JhKW7IpEItbArd5Vhe/o8ZQA/HwmQQf0lbseiDkxj1i0GyCS9ZxHRXmk7i4HQ0Iuw7oGsCiIkAMdgJ1/qMt0HYTRnXJRMbqMC4jJn0KvGbmiaRG7ssfx4UVuWDnqfCwazs+g/0VkSPix55YOrKvXqw3TxYdhO8rodxJe4HZAKf+tolFVE9PY6ZNGGpWlAS7djUnvUA/ADK4ACl2PZTSPWtwdYTTNdYCDMpLItO4qS7WX4439ioJd61UNFf799vqpm6AZEAavRDbkO2OmMqdOTWUMNmCn0/6KYGE8Nj6xOuSQXzqPztwCn8eXl6WzOxu6GHQ8c4WpwhyASi/6ePZkFQDYpOmZWVREhm1prF9FuRzLbwa4Drbwn9HtZ5moUe9CKZbHJbG8cRIJraLrR7XjUgYtjRwbAXLq2djuWcpYBmcA2twPxAM/mfzi4i8dOEvBtpu7vy3RvI6bTZ34e8ZtiKar1LLf0Z+ffVdi2/xFZ9HdmPgBbqJPhciwqgnzGWZ9dyWsTZ3AkhHHSRzjPNvrn8VFTASkgLrMm96CFs0TyMgDW0biV27GUsxi4ArjN7UA8IJT/ntuQz+vS2RzoxpwTABbQUYdGASCsoNVMgOMYN4Sdf1dh2/5HZNF/Hh/xMte8u5D2nmv4VTgTmrNqk/MgbJM+wnm20X+Mu5fcwsibJzLoM7djUQenFjkZAFup7aWefrBteZLbQXiAZ0/6ve5pbusbS1FKPvGbltA24nbdPViz6T6/FH9RXTI7nMlnoSFjYdv+R1TRL0IdoFmA0qISolblkaQ78aoqdQS/b23DEnyU9dDJvN7iTOJtA5QAa1wOZ4+C+M1ammxxOw51cNLJzADIJallCQGv5X+TmryCjzOJtwN2Ev8qd6MJP3fz6PQp9Ht/Pp0n6jLnO+0gvmQDDeYC3MnjoSt8XUUIuBjWQYuooh/n7Ks2W1f7COp2l6rK3cozlyyhHa1Zlgq0cDsetYsezv0qPLo+dz02Jbodgzp4ieTlO5N54zw2mfdYYD7wiNuBuKgbtqZZg13VSB2AMvxmDt2W/UZ3XVmsgtn0+HU6vT85i89exU7mjSVMJ/NGXNF/O0/yMwPq92B2O7eDUZGngPitAL3sanBhe4kvQnn20n4rlsXmkvjS1bx6oe4QHt5Ck3nn0M1LQ3zWO/c1+TPJs/nvdYnk+k7ku7o+tK90TzJouG02PRd2YkESYT6ZN9KK/na9mElrlidHURLtdjAq8mSTsgmgI78DtHc3GlVB6ER/tatR7MEZjG6USH6DRPLq6qXz8JZA/iaATdRr7HYs5YSWgO2ASKS165Xl2fz3Mj+lbKT+Kw/yryv8lOqH0z70ZFY6O4eOhWX7H2kfDg0bYleZ2k7ydpdjUREon/jtAA3IsHfKS0LDLTw3Zr4Vy2sDFBGjn0thLoaiPIB8Emq7HUs5xdh16QNAHZdjcYtn89/LzuOjBgkUpNdn4xFlBPQy5F74KfWN4JlrNpN+mrNDb1i2/5FW9DcIFf05JOvGHKrK5ZOYC+D8nYVl0kew0O/Dc+uVN2RDGkAhsXlux6IOTSyFuQA7iEt1O5YKtjr3XpprUJ08m/9edhLftgbIJUmXON+HMgLBRPLS09lSqwMLIUzb/4gq+oVgQ6cHlmxStXFVVW47SXnwZ09/TW1cvSr0+8hyNYo9qMOWVIBCYrUzIszFsSMPoJBYL/X0w85iNyyLkSoQyv+t+3yV2sUR/NEaYBupWvTvx3aSMwCcDWDDsv2PmKJfhNhktqfGUUgZvuIC4nX2vqpy2aTkgg7v8Rpn+bS62Fmy2S6Hs5sUslMBCojXzogwl0B+LkAx0V4r+kMnuzXuc0kEH1DfeahF/wFoxPpWAJmk1/gdePcndGLkLOQRlnkWMUU/5Yb2FBOtDas6LHKoVTCVPp8PZTRAA6exUe6rh50hmwPeW4Iiidw0gHwStKc/zCWSly8YU0JUrR3Eein/v8LuzDvW7UBckAZEAfnYfTpUJfgpJY2sVgBraaI9/fuxibqhot8AqSLEuRzSAfPSB9ahapBPAm9zcc4GGi5wOxgVmYL4zRy6L/iNHnnYSXNpbsekgJ29Lp7s5ZtL15nraDRzM+meG3qkDoyPYNBPWYFB5A+OSHE7nnKWAzMxZtN+Xxl5Qvmv+XUAzuOjBtGUJBQTlZdFmnaW7scammYAdGGeOJN56+/7Hd4TUUX/GppxHw8s/54TfnI7GBXZ4tgRGj8bluP6IpCnJ/GdzpfffMj5X6zT3XgjQoDSXIBVNPfaSX+s2wG4xNP571VbqFM0g16frqbZTLdjCQe5JBcWErMtnh20ZxGEYfsfltsI70UCQDTFhW4HoiJbP37p9hS3JT7BnSylbbzb8SjAyX9gh6tR7EOA0kjqZKnR/JQVAxQQH+N2LOU0AM5FZDLGvOx2MNXM8/nvRT9wwtYkcr85jnHN3I4lXCym3eSfGHjkdpLrAWHX/kdS0R9owAaOZnJiXTbV2ky9HLcD8joheFIrlvVMIteXS1JwOa1nGXzfuh2X1zVhTceuzEv9iPNZSttIyqFwFvo9lLkaxV7cw0Pt2rOw8Sqabwzi98Ra2Eto22k1zQaVEqgVoDSnGat/bMsSTwyNbMiGTulkDvJTWquMQE4m6T96bNhmEKCUgJdO5NKBS4DGQE0r+j2d/x71F+DSuXRNN4jmfyX9Qr85T/CPZhtoVI8wrKG99IF1qALn8RGvck2X3sw8yu1gvE4IntSJ+b1rkevzAbXI9XVifm8heJLbsXmdwRcECNgxfWGX9BHK043+/dz/2In8cLVXevuX0LbTClqeWkJUikGkhKiUFbQ8dQltO7kdW0M2dKrHxlMDlKYISIDSlHpsPLUhG1yPLUQwQYAy/H63Yykn9Lcf5WoU7vB0/nvQX4Ab27Go7kAmSm2yNP8PgJ+y0GIRYdf+e6IBqiIBpwjDIJ5bvcNrWrGsZ7Tz/xUSTSmtWNbTpZDChnF6+bTo9xRPN/o+gj6AMvye+GxaTbNBQXy7FIdBfFGraTbIrZhC0skc5MPsEpsPE5VOpuuxhYSK/lICXir6w7YQqQKezn8PuhSIuZh3GMU1HMNEzf9Kas7KeufycXJTVkMY5lrYBbwPZWWEPn+NuBpJGEgid48nfHt7Xu0kdmlInL83bWS8IfR78OTfr0GCgvGJ/WxyfXhPKYFaB/J8dfJTuscY9va8SwR26fHzglC7VxM/kzyd/x6UDlDqlIB+53xR83//ejJrwMl813ItTVhDs7DLtUhKkNLQH7APE0k/12GRS9IeG6u9Pa92Eufvy/l7K933q1U1Cf0ePJn7QWdImJ8yT8QXoHSPc5729nx1KiOwxxj29rwbDOID8FPmpUY/1OtVEz+TQj+zl668eFkmQKGz2FMUdi9Tzf/98xMMAOywS/SHXa55ogGqIn8W/eJcSld7t5zWs4orXOgpJsByWs9yKaSwIQT9oEW/x3i60TdIGUCAUk/E14zVP/oI7rKJkY9gSTNW/+hWTCGZpP8YRHaJLYiUZJLuemwhoaI/QKkXi/6auDmVp0/6PegtoMgpXImhWPO/knxO0e+cMIVd+x9Jw3tKiokGdp6Jqb0z+L5dQGd09Z4Dt4P4bcXENCkk1k/NbGC9KPR7iHY1ir0I4isFiKYkkA9FbscTWqXDi6v3hFbp8OrqHQAGCQBEUeKlor8QWI1HN6g7zDyd/x40CaCEqGuB5BSyi1uyYqzm//5FURILkEcihGH7H0nF8ebN1AUghqKE/bxWYQv/ZbTVIv8AjWHoV7fzVNN8EtOAzW7Ho4CdvwdPjPusqJDY7BiKk5LZnrCN1Hy34wFb+Huhkd+TDTRc4JVGfk9KCSQA1GdjttuxlLMIGIoxc90OxAWezn+PmpRG1ibgqR7Mzl5AJ8/km1fz30+pL56CugB/cASEYfsfSZfCMn5mAMfzfeZ3nDDG7WBU5Ari8xURk+o83OhqMCokw7mv7WoUe3EvDz31Opc9sZ6GuiNvBCghKhGgHYu9tgOs61eRXBLKf6/tkOxpb3HpSgPBWApTA5REUj14WDRhbboP419GK7Pdnl9m7O89XhNJv+QNOaTwMwMSs0ktcDsYFZmEILkkxpYS5QOyjKmxjazXeLrof56bVs2ja0YpUTpRPsztIC7WIAE/ZTsakuGlHeB92CE+NVGo8yWFyKprDqs1NCu+mlf/9jR/v18/m/Yvja2pQSQ4g96CPcH22kn/fkVScmQDRUXExBUSWxM3J1HVoBEb0u7gqVt/5mgIw7P8SGUMeUAudmOiRJfD2aPtJBe7HYM6dHkkJgFEU+y1sfN3AfMQOdHtQP6/vfuOj6pK/zj+uTOTXkgjkMTQIYLSu3SRIgooyIK9r4q6urv+dnXdVdy1rVtc1rZ22V17BaSp9A5CAOkQSiAJJQkJSUibzPn9cWYksKiUhHNn5nm/XrwCA5ivIWfuc8895znnm3fypQBd08QajuNX3uL2/ftoUmI6hz9YQ9et1/DxEw8wGSBPKfPtl89UwBT93i9+7lM8ys/4aFw8R2Rdv6hzsRyNdqAsjx46uabziBP4/j1sN9s/icfbPsYfb+rHou6ms4hzc4zIaIBwKuxW9McDMcBR00EMse34t7sc0lEZVu0AACAASURBVGyxz8gfbKCj87DeP+qX1/+AKfq98oYzmzRyWyZQIBt6RJ2LpjQGII8U/UHYie/fI/5H/5QBzdiT2JTsXo041NR0FnFuKgiPAQinwm6P9n3FbrC+L9l2/NvZKKYmvcpdD13Hu7eYzmJvCg8W2TSJ8L7gl+MskLr3AOTqYiyTWEps+Yhf+LcoymoX/X55px/AbDvTt4/0IwBhVMaYziLOTQXh0QBRlBWYzlKLxfFiN1ibC9h2/NvZOjodTSI/w0JZkZSFHiNKliGeQkt2pQxi/vWtyNo3nDngp9f/QJvp37uLFgDEcyTJcBYRgGI5mgiwm+YA2WbTiJPs9X5MNZriFJZxyQGASI4l4X/LQEUt5UQkAjSg+LDpLLU0Rh/OlYdSwbqZ17bj386yaVp1iORNFlhdWdvBdB67asuWjiG4I44SG+Z9yS+v/4FW9GeuoSsA8RTKwBd1LpajKQDf0g1grdk04iSZ3o+tjKY4ha8YWlBBWJGLmvDGHIgznUecvWNENgZow/Ys01lq8X3Pf2s0hVm2Hf92N5vhswCas6ubTEr8r0iOhaaQ1xHgz/zW9wXyy+t/oBX93/qK/lhKUgxnEQEmlEpnJMeSa3Cwno5uYIPpTOIEvoKnpdEUp1CDi3ySsgDSyJH3Jj/lxuWqJCzZQqmBLNhtOk8tvkJ3jdEUZvnGvxT9Z+jX/G1FOeFHoihveCFbm5jOYzddWNveRU3oARptXkO3NO/LUvTbwI6tXFhyjAjCqYyL4Wi46UAicHhwqI8YN+cBJlNG9CalgrYntl3tQfdNjgNst7xvH+k7ARqSL0W/nzpMw2SF5YigfL/NevR/BTwEfGE6iEG5wEEgCr3cSZymQhLd6+j0FUA7NnczncdeFC3Y1Q3gXa5fjW4LnaUUdjqN+7QFVNGvFB43IZkfMp4NtN8eSpX06xd1xk2I5y1uP/YS90Fwz6jZkrdtr+/fxXaz/cu4ZFMOKZlHiLPTWnBxBgpJSAGIo2in6SwnyQFeQKn1poOYctL4l9n+M/Q0v5ujwJNIYasIjknt5JXBtvQojiVXEFb0GH8s9b7st9f/gCr6vdbcxtvcx4vbC0iSAydEndrPBb69In476AOcbS/6D/G3dZ1Z96eV9PrOdBZxdkqISQVozAE7recHKEcp6bpi45t+u/uSkfmvc+czT/PI4+VEVpvOYxcXsL8pQCadvzpGVHPvy357/Q/Ioh9OKM6EqBPDmX3ptbzfNkaffeO3gz7A2bboBzhMsvuYXFD9Vq1NvHaa6b8YuBHL6mc6iA3Yevzb3V28tvIDrrXTXhXj5jB80URevP8RnvmSANg7E4hF/7cADmpa9GJFB9mJLupCNCVhTdnb+xGeiVNYNcgmXrvybeZrgw3f36IpcbRkZ6OWZMm6fj9TSViobxPvIObvMp2nlj7ATcClpoPYQO3NvLYb//5gC+3K99KkqBur25nOYgdL6Lv/Fe7du5CBxwDfTL9fbuKFwBwU2y08OfMYHNeZdaNbsEsuruKctWVrKwfKsZh+lBKzVCnKTWcSp7TH+yMGaG00ySm8z7XDr2LavZ1Yd4npLOLM7OeC5grLEUPJdptt4u3h/TjLaAobUIocYDt6M68UrWchmhLHwzz7i+6sGdue71qYzmNKb5Z3bMWOtI8Yf9D7Ugf0Jt61SmG307hPW8AV/UqhFI7pX3IlAK3IamM4kggA6exrAzCdkQDTzKYRP8S7mc/379Pjx/6sCW9z67cACRS2CqHKaTqPOH2HaZgB0JS9K01nqaUZ0BB9Cm8w9+ivzTf+uxtN4adKifFk0mUBQHdWj4yiNOwn/krASSU3oT3fjRjC3Nse5HnfjY/veuLX1/+AK/q9pk1jFACNOChFvzgnTtyORApagxT9fsL379PTaIpT+Iyxh44Qt8dFTWgG25qaziNOjweHo5gGrQH6s2iV6Ty1+L7Hv0Qpj9Ek9uEb/72MpvBjo5j2eQEJO8Koih3MvMtM5zmfLDwMZMFIJx7XdlrP/we/3AVYSNFva/MXMqCslCiiKWvckMOxpgMJ/5XB9nQXNWGbactOWm9Vih2mM4kftQgoBppgw37dW7lwFUAz9sqEhJ/IIyWtBmdkOBUHhvB1tuk8tQREIVLHlgMFQAqQ9hN/VpxCKTGe3/H05Boc7jRyu1wcRMt8+rO4ZwOONikn/Mh1vPe69+VWQAKwH1hnLt25C8iiXykqKoj4ag7DALiQrXJxFWetGbszALxPj+TianNKUQ3M9v7Sdkt8pjJ6JUAyhzKk0YB/OEDjNgCp5K50YpsJ9QT0vpVyYK7hLLahFG5gpveXtnva5y9e467sRfR/D6AHq0fGUxhlOlN9a8qe5Ay2XQowhZtfWkM3X19+31Kxad4lpH4rIIt+r++X+KSSK0W/OGuHaLR/HoMqv+AqkKLfX9h2Xf/z/HJnOeFHwqiKbcaeRqbziJ9WRFwbgC6stdPSHgt4H3gHpY6ZDmMztl3i509GMe2zfBK3hVEVexGbLjSdpz6lkptwGXNvcOJxbaPNvHv4V+2x7vs+8vvrfyAX/TNmMkJVEUIlYdUyoybO1kf8LHcw88JW0isfWGE6jzgts4AadA/zGMNZTlBFmNpFi1XlhBdGUB5uOo/4cQUkJlYSluTCXTaWTzebzlNLAfAQSk00HcSG5gBVQAYQZziL3yolxnM1nz/5CWP/+SL3z/zpv+HfanBW5JKyrh+LX6z1cjK6VWcpsMBIsDoUsEW/UhzOp+H8NHK4lbc36kkRIc7cXAZf5P3pZ0pRYzSMOC3elmpfo9/jbNe//GamTOnG6olbaLfXdBbx4/bQrBNAYw6siKbMTuO/Ct21R5xEKUrQS/wcwGDDcfzaEvoVj+OTb17jruwttD3cmLy4SI6Fms5V197n2vVj+fSh/ix6+jDJ7lq/NdT7cZpSVJrIVpcCtuj3eiWfhqyih+ziF2csmpKwK/lyVDSlvp7qrxgNJM6U799rBDa7619Dt9LNXHwsl9SjprOIH1ZNSEg+SZ0BLuMbO810jkR3p4k0HcTGfOP/cgK/1jkv9tK09HJm3zCGz25sQHGE6TznKpWchAEs7LGAgdkfMuHALEYUZNGq9hkcLo4X/S8biFjnAn0gTAXyjhB/QRgVneUUTHEmurK2Yzr7O/6BP4UDy5Ty7137QWgGkI3u4tHRcJZTKiS+ohfLbZlNQBYt29bgjIihZMdYPrNL165Q4DpgEvrkaXFqXwG70MszuhrOEhBCqXK6cVXGUJp6FV/c3IiDDUxnOlst2Zk6glm3tmPLsCuY8UPj6BL08rANwLLzl67+BHTR7+3i8drd/IvbeOfKrqwZYDqT8BeK5uzuBvAyE/UH4Ve8S7Fe9f7yCpNZTiWBAtff+fXTnVk/qgl7G5rOI/5XHindADqwwU6z/P3RJ86uRKm1psPYlVJ4OPFpnzhHnzDu4Bg+e7iY2OxIyhuOZurdfVjSxZ/2TLqodgxlzsAhfHNrCO7IHFIzJzHphzbo+75vXvb3rj0+AV30e73+CdfUVBBGAoWtG3FANvWIn3QRm5tFUJG4nzS+5Mp84BPTmcRZeROoRrdcSzKc5QSFJLq3kbEYoAMbupnOI06UR0pqORFpLtylt/HWYtN5avm+EDGawj+8DVQCXbDhmR3+6GuGFg5m7sO7abbMiSe0AxuvmMCHNyT7wax/c3Y1vo7372zJ7n6AlUmnaZ3J/NNuWpxqrX4zoB1QArx7PnPWp4Av+pUiJ5+GX3zEz7CAzqzrYjqTsL+2bOkO8Cp34SbkjUDYwBOMlOIg+obNAQw3HOd/vMI9MwFSyesQiJvj/NlemnYFaMreb5I5XGU6j1dr9EFBhcBHhrPYnlIUAB+g9/TYbvz7qzV0K23NjmencNNzlYSWxFPUPJ4jti76O7C+1TC+uj2KY8mlROU9ye8f6ULmGydt2q3tcu/HfytF6Q/8Gb8T8EW/18veJRqks69rNCXSJk/8oDT2JyaRn1GNize5XXF8iYjwT74Z0aHo9dC28S/uyT5I8kYnntCerJDZfpsoIyqyiLiLAEYzdZbpPLWM8X58C6UqfvRPCh/f+B8CyLW/jtTg4hamLBnGnHs/Ytzke/jXzHySygDiOGK7DebzuHRDGVEHM+k07SI23f8Yf/qx9rtRwCDvzwOqgUewFP3zV9Hju/kMxEVNeF+WXvLTf0UEq56sGmCB9Ta3kkfqF0qxx3QmcU6WAmvQG7KuNJzlf3zAhE8AWpHVJ5qSMNN5BGyhbV8PjpAk8lcPYkGe6TxezYE+QAXwD8NZ/MlqYDn6vI5RhrMEnIUMLBrPR3O3k1H+BI9vjqMoagIf/mo8H17bkXWtHdSc985poVQ6e7Kiw3W8d3M45SFTGZ31GE9u6sCGX3Qh841smv7Uk7tr0DeI3yjFpvMQ+byxlAqIvQk/ybK4sgcrp6+kFzU43J8y9oUCEgPmkY2oO26cjcOpvOMJHlc5XNAh0AZ9MLIshqIP7CkD7vB+tAUnbvZzwVONOdh+F82WzGH4fNOZglkRcQ2W0/teheX8Oa/9YiRf7jGdycuBXmfsQKnnDWfxK5bFQGA+cAy4E71OW9SDJfS5rhcrxjrxhABUEFa8lyZr1tIls4j4ej05ujF58Z1Z1yWNnM4huCMA5jNgyqUs+PQM/jMJwGvop8I9lGJ1fWQ1JZiKfgtY/Ba39omhZHspUZ+VE1ltOpewn3/w4NDtZPQE3lGKW03nEefOO/7noh/ZfgL822yiEz3C021+wT9vXkfHlbtpYZeZ5aC0gl6jCknomEruwle5+2+m89SiTwQNlot2HbMsZgPDgC+AtwzHCWjdWRXzNL+7rBcrLo+mrDGAB6smm/RVsxjxTV1/vs5ktmnD9q7xHGlpec9kKSQ+ax6Xzvg1f1t0GjP7td2L/j75RCnG1XVW04Km6AewLPoAS0KoqnicJ15IoFDWRIrvJVIQvZIeoX/n1/eCVQm0UYps07lE3bAsegAr0d087kJvhrSVO3g9vQtrpcuIIYdp2PBbut0F1Pwff7mnH0sOms6ELmLiga9QSm4Iz5Jl0RlYC7iBnwP5ZhMFvlAqref4Teer+XxEOvu6Z9Jp2odMWJTMoaiOrG/WlTWDiojLPUxSXg5peQdoXPRD5yg6qLHSyE1MJScliYLUJfRZXEzcsaPEVt7Af8amkde5Bkf1TlotfptbZ/yVh3bU4DrTyGnAS+gepBcpxbZz+wrYzxl/RfyZUiy1LKZXEzryU8Z2m8hLy6sJtdOx6sIQCw8jmDmhO6vjpzOKHbR5SQr+wKIUqyyLT4GxwARs2PLwY8blXszGhlGUhsp70/m3jYxBCstqzu7ZNin4Qa/jfxB4HHjWcBa/pRSZlsX7wLXow83+aThSwKsiTD3I5LUPMnntGD5N3kd6+Wp6lAB8w+DmuutPUfPm7AG+xYPlUVhuN66Kz7n6jav5/A4HHpf3R4hV647AjWvBIzyztpi4mgYUhzdn99pJTJq3gt7nsnTrBvQyutcDseCHICv6vR4FruzByv438G7PRfR7P4tWuaZDCbO6seaiaMpSimlADmlHgWdMZxL14vfA1ehOPl8Athr7w5iTNJ4PxzqocX3KNXI2xHmUQ1r6UWIznNRU3MnrdmmH6QJuRK8vLjCcJRA8BowDLgU+B/aZjRM8PmPsodq/nsSkeTtpldue71o2Ibt1Evktw6mMAxVag6p+m1s3TODD2Np/p4zIQ4dI3rmXplmzuTyrmLgagHv41w8drnUmWnN8o/wTdfDfs6WgK/qV4jvL4j/N2XNTOJWRfVh25V6avuEmxGM6mzAjluKIDmwYBjCJSRwj6i9KyaPfQKQUWy2Lt9Cbee/EZm/uJcS4k8jPcFET2onMNuvovN10pmDgweHYTpuhAK3Z8UV7NhaZzuQ1AUgBtqEPmhLnQCl2WhavARPR4/8xw5GC1hL6FS+h30r0kkucuGlAsSuFvNB4joSsosfR0XxxWyEJ1YdIrjpIoypfkV8PHOjvB4DJSpFTT5/HuKBa0+9jWVwQRenmdXSKaUUWWbRY9BVDF5rOJcwYw2dXN+LQxQsYwGDm7vbgbK+Ufbq7iLplWaQAm9EtPCejN/jaxiyGjxrOnDuqCCn9iHGvlBAre4/q2Xo69skh7dIwKvOf55f3prO/3HQm9CFcf0UvaeiHUksN5wkIlkUyevwnotdvzzGbSNjAVcBt6Ce/FymFXW7661yw9Ok/gVLsLyP6V7d5N/A3Z3ff5uySzXNBqAtrMxpx6OIyIrmNt/DgvE0K/sCmFHnAL7y/vAPdos02RjN1+iEabg6lOvoy5sopovXsMA0b5pLaH2A4s/9pk4LfhV7H7wD+IQV/3VGKQ+gOLaALvYYG4wjz0tBr+QF+HsgFPwRp0e/15mL6z5nML3CgHP1YMspFdTB/PYJONCVhncm8AuC3/JndtHhJKRYYjiXOj/8CX6JPXrzfcJYTVBGmfsXfJ7txVjXmYPtOZLYxnSlQeXA4NnLxKIXlakL2nDt4c53pTF4/A5oAO9D7UETd+gj4DIjAZuNfnFcO4AH0npkpSjHDcJ56F5TLe3wsi/QoSjfJMp9gpQBGpZLXcQyf7fbg7KAUcmBbkLAsUoFNyDKfoGXTZT2gnz7dADyBUktMhwlElkUj9PiXZT7Bq/aynouV4ojhPPUuqGe2lWKfb5nPFi70rKeDtGgMIjtpHXc/L158FVN9y3qk4A8iSpGLzZf55NF4w3baLC4n4kwOlxGnwabLeny+QakhUvDXH6U4iCzzCWYnL+sJ+IIfgrzo93pzMf3nXMxGx694vr3COvXJECJgpJKTcAH70qZw8+gaXE5AlvUEr/8C09HLfHxrqG2hijDVkqzHerDq1QKS6vX4+mDjxuXayMWjbbispw9wAKUCtnuIzXwEfIpe5mOr8S/qlW/PTNAs6/EJ+m9wpVDAnR6cRXtp1vFdru/Wicw2TtxB/7UJRFGUhg3h6wlD+fqWdmxuAmQBD5vOJczwjv+70T3QOwE3mU10onIiPeVEel7j59vjKIpqxY4005kCwWq6X1FOREo4FQcf5tm3TOfxGgn8FviN6SDBwjv+JwKHgPboGX8R+O4BMoD9wC8NZzmvpLBFL/NBb5zyDOGr4b1ZOf5yZg81nUvULQc11pXMGBNJReI2MhyZdC4BRsuynuDmXeZzDeAGxgCDzCb6X91ZHXc1n982kIUTGnGwgek8/mwDHXofIb6Dk5qKm5nylE2W9XQCbvf+/GOTQYKNt5vPGKAaGAVcZjaRqGdXAkOAcvT1PyiW9fhI0e+lFF8Dv57DcCoJJZ393fuwtLPpXKLuDOWrSxM40iqfRK7iC1VKzPVKscl0LmGed3mXb33/fejTGW1jOiMP55OUFYI7chhzxkdwLMR0Jn+0m+atckgbDDCYuc9fyYw9hiOBPnzrN+jr8ZMoZZfTgIOGUixFz/6CnvlvazCOqD+d0Pu3AG5VirUmw5ggRf+JJq+g99s/5zUALmbjiAvZ2sRwJlEHerKyfXP2XlKNi2v4hN20eFQpppvOJexDKV4B/gWEAI9io4295UR6rmDGX48SkxPFsUZXMPMqCzlE/EwUkJi4nTZjFJbVjs3v3c+Ly01nAiLRLTmjganA42bjBC+leBP4J3q99yNAktlEoo7Vvrl+Wik+NJzHCCn6a/Gu77vn39y8/G/8CgfK0Yel4xpxIM50NnH2WrMjrSPrRwLczwssZOAHwLOGYwl7egBYhC74f4e+AbCFDXQs+wX/fLIaV1lD8i8cwtcDTWfyF+VEhK+j0/ganGGNOLjsSX5vhwu+A/g1kA5sBG5EKbmTM+vX6Na9ceibsVCzcUQdieD4zfV04A9m45gjRf9JlKISGPMwz+6fzTBCqY4cwaybGnI41nQ2cXYsPOHVhDhe4W5e5e41wO3eGzwhTqAUVej1/XuBNuhNXrZ5n5zCLTkvct9zCjwt2d1vIPN7mc5kd25crlX0GFdJWGI0pbuf5PfPh+C2w/h3oQ8LKQRGo1SJ4TxBTyncwHh0g4cWwEPYaPyLs+JCb5BPR5/LcINSwfuYVL6ZT0EpDrgJuWo8H5atpAeFJITWyJfKL1UR6ryFKR16stJ6gMl5wFVKIe0PxQ9SisPAaKAE6Ite62+bVr6/4vnMT7jmBQWeSsLkwK4f4cblXE7va8qIahZKVdH9vPBUYw5Wms7llQsMBPqj1C7DWYSXUhSgx38x0Av4FVIr+SsnuuDvAuSjN+4eNRvJrKA+kfenWBYD4imcFUJ1RBo58+/kdTkoxU+kk90wirLY23mr3W5adEK3ZBugFFtNZxP+wbLogz6lM8r78SWziU50K2+lzWXw4ft5oW0UZbIM4SQ1OB3L6X3NUWIzQqguuZPXH7mc2aYPYLSAYej+8EtQqsZwHvEDLIuewDfoJSFz0ev9pWDyHw70k5q+wBFgkFKsNxvJPCn6f4JlMQS9BiysC9/Of5cbPN9x8Xf5NJRHsTbVjN2NLmPuDUDEIOZbS+lbCAxUiu9MZxP+xbIYBMwEwoHZwCvY7MLfhL2hz/PggFbsbLGUvkHXjeJU3LicK+k5ppgGF7pwl93CO4+OZprp2XQHcD8wGPgUpa4xnEf8BMuiH3rcR6JvAF6E4F0a4kec6Cc0/YCjwGVKsdpsJHuQR1Y/wdvKcwxQ1ZsVgy5k2+CRfHmL9Mq2p5ZkpVzG3JtCcEd+zRBrLV2OAEOk4BdnQynmox/1VwDD0Ut9bPW+GUWZcwSzbuzAxisGMb+36TymuXG5ltN7nK/gv553H7NBwe8rQgYDx4CXzcYRp0MpFqMPTStH9++31R4fcUq+Nfz90Es0L5eC/ziZ6T9NlsXQeAqnzmZ4eA9WU0VI6SL6f7SD1nJcuk10YW1GN7692okn5AtGM4EPDlcSfplSbDCdTfg3y+JS9BO/SHR3n3+gD/Oyhc+5athopk60wMrmglWzGf51Da6gm5GsJCxsJT2vKSW6RQjVJTfx7z9cxVTTBX80ulVgJ7xFCEotNRtJnAnLoj8wA/1vuQz4G/owL2EvYeiCvxtQBAxTilVmI9mLFP1nwLIYGEvxl58xJmow8/Bg1aynw4wV9A76dWImWXgYzNz+rckaAPBvbuQO3sirJnSwUmwxnU8EBsuiL3qpTwywBXgGfWGxhXe5bsB4PnzAicd1hAZ7ZjLik6M0sMNps+fFEeLjMuk8voLw5BCqi+7gjT+MYNZew7HS0a0CU9D7ikailBQhfsiy6IVe6tMA2AY8DcF1mqvNNUSPteZAAXpJzzqzkexHHlOdAaVYcJQGl17OrLx/cj8OlLMz60eNYOYQJ27bdPcINknkxzYl+xIPFv/Hc9zCO5urCe0nBb+oS0qxBN1tZT/6xM6/o9v62cL1vLdwEpMeqSCsKJ7iZmP57PYm7G1oOtf5kE2TZqvocXsF4cmRHMu5nxd+a4OCH+BKdMG/FugmBb//UooVwAAgG8gAnsdmJ3cHsXbo9+PmwE6grxT8pyYz/WfBskgFvriD17u/xL0cIb5gBiNeqyTcNo/7g8lURrdz4r5a4XDMYsR0dB/eoG7LJeqPZdEY+AzoDVQCkwHbdPa6nJmJ/+HGRxMpbLWMXh+up9N205nq0yYu6pZNk2EKy5FE/ppJTPpLU7Lt0Ja3AvgOmAg8i1J2yCTOkWWRDHyCXjNeje7qs9BoqOA2FLgbvZb/a2C8UvIE5odI0X+WLIsI4LU+LLlhL01oxt4Z1/J+poVH2aild8Bqy5am0ZTE38o78Rtp39f78lPAY8F88IY4PyyLMOBfwC3elz4C3sUmnX3S2B/6GH/s/BL3bryJf7eO5FiIjhY4701uXM41dB1WQGJXgNbs+PQZHvlPGFUmx384MA54A1iEUnKOQgCyLELRnXzu9L70KfAfpLPP+eQEbkc/TQP95OU33gPWxA+Qov8cWBYWug/snwGrNdvWLGJAbDENshfSf3kNLvni1rEwKlyDmXtpE/b1rMFJD1aRSZdy4Dal+MB0PhE8vOP/AfSmPgewCj3rZ6unTI3JC3mT24f2YenlCxgwPZe0QtOZzlUxDWLX0enqMqKaOPBU92XJC//HXxcYjtUe3d2pEfBflLrRcB5Rj7zjfyL6SZ8TWOP9uW32+QSwBHQ3rA5AFXC3UrxtNpJ/kKK/DlgWI4D3+7Akdgn9ACghOmchA6btIz3fbLrAcSFbm/RixcgIKhLcOHmGR/gjj+13E3KVUqwxnU8EJ8tiKPAhEIc+xfMVdIcP2zhI8p+TOdzWg+XeSsa8xfRb5cHpl2/+W2jbeS9Nh3pwhIZSVXgt7z91DZ/uMBgpHP3EZ4T315nAjSi1yVgicd54O3t9jC5EjwKvAouNhgpsg4E70IcmHgTGKGWv91s7k6K/jlgWLYG3hzKn3xvcQTr7qcFRs5NWC2TW/9yEUeG6jLmXprOvpwV8x8XcwjuspesnwL1Kcch0RhHcLIvmwJvAIO9LS9DLf2wx69+JzKgPmHBnBtsvBSgmZt8CBk7zp1n/YhrErqfjyFKiWwAkc2jFA0x+uQPfmZxZ7YA+cKsRen33n9Dr96WdYxCxLJqgx/9l3peWoce/zPrXnQTgPnQ7ToAvgbuUItdcJP8jRX8dsiwcwH0NKHr2rzwUcQdvAr5Z//7T99HksNmE/mk0U0elktfRN7v/FI/mVxI+USk+Np1NCB/v+L8beA49C2W7Wf+Xuaf7LbxzbwQVCR4s9zYy5i+m70q7T0rUnt134S7px+JXH2DyIqfZJdQXcPyQrUzgFpSSM0GClHe5z53o5X7RyKx/Xao9u1+EXkb3X6XssYfKn0jRXw8si1bAW8OY3e917iSd/ayjw8LlXLLIdDZ/4cTtqMHl2c8FMUvoc8XTPNr6fl4gky4yuy9szTvr/xa6vSfoWf83AFvMqnfl2+j3uO7ONuwYVEnov39fKAAADKZJREFU0Xe57iW7dh47Qnz8d7QfYaPZfSdQg94VvQ+9pns/MrsvvCyLpujxXnvW/3V073hxZhoBdyGz+3VGiv56UnvW/xGeiXiGhyvbsWXe1Xy+vhurU3JIO1RCrHR2OElDDsdewrIBEZQ3GsS87d/RoZ8Hp8PCk69wyOy+8AunmPWvRF+wPgHKDEb73svc0z2citC3uG3jcGanp5Md15TsxptpZ7y/fSnR0Ztp16+AxC4Ky2GD2X0Hukf79cAT6I26R7EsC7mIipOcYta/Gj3+PwZKDUbzFw2A8cBwdCtOmd2vI1L01zPvrP9fgKsAGpFXsouWYaFUebJosWwpfVbadZbtfIqlOKIPS/s0YV8PB8pZjYserGIdnT3A28DvZHZf+BvvrP9fgLHel0rRhf+X6K4TtpFJpzs7sX5kIfFZq+g+bzctDpzvDBWEh22mXe9DJPfy4AixUJ40cubfwytTDM7udwduApp6fy2decRp8c76Pwf8zPtSGbq953T0RIA4UQS6VrrK+3OFboX8W5ndrxtS9J8nlkVv4Nmm7On/BndwGXMBqCKkdDfNV66hy7pi4oLu8JZkDjboTGaXJuzr7qImDOADxvN7niSLVp8BjyrFVsMxhTgnlkUP4FmOb/QtBN4DvsEmvb2/YshVA1lwbQjuCIDDJG3ZyEWrtnJhdn3393fjcm2hbbdcUvvU4IwESObQynF8/J/hzMmu109+ak6gJzAaffoy6JNYH0MX/TUGMgk/ZVl0BZ4BhnhfOgK8jz5MSr6X9Gz+5ejZ/Vjva1+ir/+yT6YOSdF/Hnkf+Q0HnhnMNx2f5WG6eTtNerBqDpG8eQYjplcRFhRvAhYebmHKL8OpjAaYw1B+x9OspesC4GGlWGk2oRB1xzv+h6CL/87el/PQF7d52GDZTy+Wx7zIfeM6se5KJx4XQBkRh9fS5auNtN9V15+vlOioLFp2OkRyt2pCYgHiKNp0BTOmTOBDkzf7Izl+8FIB8CTwCkrJ7Kw4a5bFYPT4961RPwDMQN/8Gx//BsSg3xOvABp6X1uGvv7LBuh6IEW/Ad71vhMc1Dw5jDnNJ/IyI5jJLlqUvMI977RiZxFAKJXOQLoBiOFoeFfWdFpD13VL6Bc7n0GdxvNh9wy2OV5mIsvokwk8Anwl6/ZEoPKO/3HoQrKV9+VKYCEwE6jz4vpMXc7MxN/z5LAurB0eTmXcF4x6KYcLCh146uR9KYe09L007VZMg7YKywkQRdmeQcyfcgdvrDGwbr8DugBZiN6YexT4HL0h+98oZYvWq8L/eW/+x6JPkG/jfbkSWIQe/1mGop1PGehzLfqhZ/kBNqGv/1/K9b/+SNFvkGURgn58PLEZuwclUMhautKYvO2388a2STwxNJ+k7dmkb9tC26xjRNlqDfDpaEBRZFu2tL6AnDYJFLZy4nE9yaNFf+DJOO8f8QBT0a0N5yplj6UOQtQ3y8KFnlGeyPFOHwDb0Bf/JegNgMYkUOB6ike73MeLqxpyOGQYc5L/ykMPhlIZfYCUbdtpvX0f6YdPZ/lPJWGhWbRsf4DGXSsIbwRgoTxJ5K++hGUzb2bKuhDc5+uC5EAXHj2BXkAqcAhoilLSYEHUO+/4vwI9/ofW+q3tHB//fnfN/xFhQH/0/3ML72sK/f/6MjBHKVnqVN+k6LcJy6ItcA9wMxD7EH/hL/zm+9/3YHmKabAnh9Tt28jYfohGxaayno7+LOyRzv52MZSk1y4HZjGc5/gNCxh0EHgNeF0p9hmKKYQtWBYZ6G4/t6I7V4B+3L8aWAWsBYzv+bmIjZGZdH4zBHeU77UKwooOkbxtN822byMjuwbX9zfux4iM2Ed663yS2pQQ09KDIxQghOriFuyaM5ZP5/Rmxfk8v6QpcDV6c25Mrddz0T3V/4ZSwbjMQhhkWbTm+PiP975cBqwBVqLHvz9+X0ajlzL1ALqiN+cC5KMPM3tVKXYbyhaUpOi3GcsiGrgOmNiGbR1HMp1RTKMPS/E98q4ktPxtbvmrwgFAGvsT80gp9OA87/+YLqodTchulMKBlPV02FRKTGU2TWKv473x7dnYuJJQ5nEp0xjFdEaSwwWLgZeAz5UKqFkMIc6ZZREFTADu5fi6fwA3sBF9A7AKzHWyas6usId5tlM/Fvdszu7u4VT6blJYT4fp0xm5L4e0Ng5q2haSkFJKjMP3+w0o3tKZzBm38+ayOIrru2tZAnr5VBWwDv1UMRU92QB6GcVUdCeVJSgV9F3UhFmWRSR6M+u96CLZp4YTx//B85/utKWgn6D1ANoBjlq/twJ9/f9EKeSJmgFS9NuYd/ZvFDAqicOXXM4sx0imc4BG/JHHs1PJ3d2ZtQXvcNuYGhzVZUQdKKJBXgFJeYdoeLiIuJIi4srq6mbAidtqwe7UZA41SqQgJZbilCiOJTtQToAHeX7TO9ySWExc4/4sJIl8vmZIVQmx84Fp6LV6JjpxCOF3LIs26OU/o4C+nHjx3AtsAXZ6f2SjbwzOq3DKw6/n3X6Dmdu3JytbD+ab0irCGsdRxDM8wiimU0BCVRlR+6MpXZPAkQ3oG5ZC6rZlYRJ6yUAroCXQGvAtIVwOjEHPLirgAWAWsFV67Au78s7+j/T+6IfuKOWTjR7/WcAO9PuBiZvWEKAZety1Qhf5abV+343eJzMNmC6z+uZJ0e8nLIsk9MaXUegOQFEA7dnAVEbTnD2n/HuzGDZlD82zAfqwtHMiBakVhJfW4HR7cHg8WN8/inegHA48ThfukHDKo8OpjCkn/Og0Rn9ZQXhoKVHRf+Cpe0/+HFvJYA1dmcwDrKYH6G4XM9AD/SulKKnTL4YQQcaySESP/5Ho1nbRJ/0RN7AHfQOQhT4tttD7oy6eqIWjZ84TgCYcL6ybcOLNCOh2hDM2ctEF7djcy9J/92TL0S0MQbfo+7k3ayl6VtN36q0HXew40Rv+GngzxKP3Ae1B73u4C+9ZKLUUo5dHzEepJ8/mf1oIO7AsEtDj3nf9jz3pj7jRhb9v/Gejx2Fd3VyHo8dcIpDO8Zvrppx4MwL6IK2Z6Ov/bKWw9VLkYCNFvx+yLMLRm8+6otfLdU0kv3UX1tKVNXRlDa3YSSq5dGM1h0kui6D8yMeMix3MvJPfLH5UFi3cF7GpupKwCLBYSH/2ke79LF1ZRyfPURpsBr5FX2C/Bb5VysisgxABz7II4/j4970HtPmRv1LG8QKg0PvzKnRh7fF+dKKLdyd6w52vwI/3foz4n/+q5kHPOH4/9oHV349/ywpB97n35eyAngn8Avit93P1RLcsPFPjgU9RqgbL+jm6I1JmrSy7ZCZfBBrLIhQ9Zrpx/D0ggx/eTX+ME8e+byLgp8a/b+z/1Pjfih5zvnG3SimzDQjED5OiP0BYFg3Qa4B9bwLN0etXU9CP4OjLYi5mI405QChVhFCNy3ttVli4ceHGRTkR5JFCHinsI50NdASoQG92y0XPJvgG+XqlzG8wFCKYWRax/PD4D62DT1GBPlMgFz2T6LvJX6/UOW4wtKyG6NnLFPSSHJf3hwNdyLhr/civleM7lDpyTp9biABgWcRw4vhvgR5PqdTN+K9Ej7k89Pj3Ffjrznn8i/NKiv4A5+0JnMjxN4AUoDH6jcB3cXVy4oXVd4H3XVzzgGLpnSuEf/GO/wROPf5DOPX4r+TE8Z+LjH8h/I53/MdzfOyncuL1P4RTj/8DHL/25wJFMv4DgxT9QgghhBBCBLiTN2AJIYQQQgghAowU/UIIIYQQQgQ4KfqFEEIIIYQIcFL0CyGEEEIIEeCk6BdCCCGEECLASdEvhBBCCCFEgJOiXwghhBBCiAAnRb8QQgghhBABTop+IYQQQgghApwU/UIIIYQQQgQ4KfqFEEIIIYQIcFL0CyGEEEIIEeCk6BdCCCGEECLASdEvhBBCCCFEgJOiXwghhBBCiAAnRb8QQgghhBABTop+IYQQQgghApwU/UIIIYQQQgQ4KfqFEEIIIYQIcFL0CyGEEEIIEeCk6BdCCCGEECLASdEvhBBCCCFEgJOiXwghhBBCiAAnRb8QQgghhBABTop+IYQQQgghApwU/UIIIYQQQgQ4KfqFEEIIIYQIcFL0CyGEEEIIEeCk6BdCCCGEECLASdEvhBBCCCFEgJOiXwghhBBCiAAnRb8QQgghhBABTop+IYQQQgghApwU/UIIIYQQQgQ4KfqFEEIIIYQIcFL0CyGEEEIIEeCk6BdCCCGEECLASdEvhBBCCCFEgJOiXwghhBBCiAAnRb8QQgghhBABTop+IYQQQgghApwU/UIIIYQQQgQ4KfqFEEIIIYQIcP8PcfOJW8v4IYcAAAAASUVORK5CYII=\n",
"text/plain": [
"<Figure size 936x288 with 3 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fig, axes = plt.subplots(1,3, figsize = (13,4))\n",
"params = [((0,0), 4), ((2,0), 4, ), ((2,0), 2) ]\n",
"y_true = fuzzy_circle(fuzz_factor=0.01)\n",
"\n",
"print('{:<10s} {:<10s} {:<10s}'.format('','explicit', 'np function'))\n",
"\n",
"for i in range(len(axes)):\n",
" axes[i].scatter(0,0, c='b')\n",
" axes[i].add_artist(plt.Circle((0, 0), 4.05, lw=2, edgecolor='b', facecolor=(0,0,1,0.3), zorder=1))\n",
" xy, r = params[i]\n",
" axes[i].scatter(*xy, c='r')\n",
" axes[i].add_artist(plt.Circle(xy, r, lw=2, ls='--', edgecolor='r', facecolor=(1,0,0,0.3), zorder=1))\n",
" \n",
" smooth = 0.001\n",
" y_pred = fuzzy_circle(xy, r, 0.01)\n",
" intersection = np.sum(np.logical_and(y_true, y_pred))\n",
" union = np.sum(np.logical_or(y_pred, y_true))\n",
" iou = np.mean((intersection)/union)\n",
" dice = 2*np.mean(intersection/(np.sum(y_pred)+np.sum(y_true)))\n",
" \n",
" print('{:<10s} {:<10.2f} {:<10.2f}'.format('IoU', iou, metrics_np(np.reshape(y_true, (1,)+y_true.shape+(1,)), np.reshape(y_pred, (1,)+y_pred.shape+(1,)), metric_name = 'iou')))\n",
" print('{:<10s} {:<10.2f} {:<10.2f}'.format('Dice', dice, metrics_np(np.reshape(y_true, (1,)+y_true.shape+(1,)), np.reshape(y_pred, (1,)+y_pred.shape+(1,)), metric_name = 'dice')))\n",
" \n",
" axes[i].text(0,5, f'IoU={iou:1.2f}\\nDice={dice:1.2f}', ha='center')\n",
" \n",
" axes[i].set_axis_off()\n",
" axes[i].set(aspect=1, xlim=(-5,6.1), ylim=(-5,6))\n",
"fig.savefig('metrics_iou_dice.png',bbox_inches='tight')"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" explicit np function\n",
"IoU 1.00 1.00 \n",
"soft IoU 0.60 0.60 \n",
"IoU 1.00 1.00 \n",
"soft IoU 0.50 0.50 \n",
"IoU 0.52 0.52 \n",
"soft IoU 0.42 0.42 \n",
"IoU 0.25 0.25 \n",
"soft IoU 0.15 0.15 \n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1152x288 with 4 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"x,y = fine_grid\n",
"fig, axes = plt.subplots(1,4, figsize = (16,4))\n",
"params = [((0,0), 4, 0.8), ((0,0), 4, 1), ((2,0), 4, 0.8), ((2,0), 2, 0.8) ]\n",
"y_true = fuzzy_circle(fuzz_factor=0.01)\n",
"\n",
"print('{:<10s} {:<10s} {:<10s}'.format('','explicit', 'np function'))\n",
"\n",
"for i in range(len(axes)):\n",
" # axes[i].contour(x, y, y_true, levels = [0.99], colors='b')\n",
" axes[i].add_artist(plt.Circle((0, 0), 4, lw=2, edgecolor='b', facecolor=(0,0,0,0), zorder=1))\n",
" xy, r, fuzz_factor = params[i]\n",
" y_pred = fuzzy_circle(xy, r, fuzz_factor)\n",
" # axes[i].contourf(x, y, y_pred, alpha=0.5, levels=[0.01,0.5,0.99,1.25], cmap = 'gray_r')\n",
" axes[i].pcolormesh(x, y, y_pred, alpha=0.3, shading='gouraud', cmap = 'gray_r')\n",
" cs = axes[i].contour(x, y, y_pred, levels=[0.01,0.5,0.99,1.25], colors = 'k')\n",
" axes[i].clabel(cs, fmt='%1.1f')\n",
" \n",
" intersection = np.sum(np.logical_and(y_true, y_pred))\n",
" union = np.sum(np.logical_or(y_pred, y_true))\n",
" iou = np.mean(intersection/union)\n",
" \n",
" intersection_soft = np.sum(np.abs(y_true * y_pred))\n",
" union_soft = np.sum(np.abs(y_pred)) + np.sum(np.abs(y_true)) - intersection_soft\n",
" iou_soft = np.mean(intersection_soft/union_soft)\n",
"\n",
" print('{:<10s} {:<10.2f} {:<10.2f}'.format('IoU',iou, metrics_np(np.reshape(y_true, (1,)+y_true.shape+(1,)), np.reshape(y_pred, (1,)+y_pred.shape+(1,)), metric_name='iou')))\n",
" print('{:<10s} {:<10.2f} {:<10.2f}'.format('soft IoU',iou_soft, metrics_np(np.reshape(y_true, (1,)+y_true.shape+(1,)), np.reshape(y_pred, (1,)+y_pred.shape+(1,)),metric_name='iou', metric_type='soft')))\n",
" \n",
" axes[i].text(0,5, f'IoU={iou:1.2f}\\nsoft IoU={iou_soft:1.2f}', ha='center')\n",
" \n",
" axes[i].set_axis_off()\n",
" axes[i].set(aspect=1)\n",
"fig.savefig('metrics_iou_dice_soft.png',bbox_inches='tight')"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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I/fBDAIz4eHyXXELxuHGU9OmD1+vF6/Xi8/nw+XwEAoFSB8SPzxdg+fJkpk1rzQ8/pBMIWLDbA1x44V6uueYP0tO9FZwO8wh2Oux2O1arFbvdjsPhwOlwkPDjjyRMmoT1yy+xmMlRN94o7azf+UdttCMkNxf+9S+pvFxSIs7FOefIoOrcc8O0aXl5YqhfeEHuBsA3wCvATMBfrYBewC3ANYAk2o0YAc88U8sISDAbNkjE5d13pVYBSEcnTJCQZP2hdThCfD545x148klxoAH69hUdHj1aNm4NS+h778Hjj8POnQAEevXCfd11FF10EW6ns4IdNm2x3+/H7/eTnW1lxowWfPFFa/btk4TnPn1E3c47LwwnHsSbevttCUduL80l6dUL/vlPGR2EJVQZ2vGINsXFcs996SWZrouLgzvukLorzZuHKdTlEiX/73/B58NwOvFefz0Fd9yBu1kzPB4PHo/niI6HqfiGYbBrVxxTphzDggXtMAwLCQk+rr56ExdcsB2brRaOh9OJw+EgLi6O+H37SHruOWwffIAlEJASvE88IVs514/XrY12mBiGGOv77hPnA2RU+PTTkrcRttD33oO//U3mbJDIxiNA1hH/sSpSgL+VHslYLDLb99xzEST+FxSIQ/T88+V7Xlx7LfznP7LXRd2jdTgCfvhBdGLjRnl+wglycz/rrAjuwz/+KHuclCaFBI47jqJHHqH4tNNwl9pg0xb7fD68Xm8F+2s+DgQCeL0G8+e35f33jyE7OwGQQNtbb0kkJCz8fhmYPvqoLC8DieK99ZbMH9UP2vGIJosXS/j5t99kim3cOLn3duwYgdBly0TQxo0YFgveUaMovO8+Slq1wu12lzkdNXE8gg/DMNi+PZWPP+7L6tXtAejRI5tbbllB+/bFNXY8go+4uDgSt24l6ZlnsJnTQCefLIkBxx4b4dWtNdpoh8Eff4ix/uYbeX766fDss1LYMGx275Yo2MyZACwCHgCWRtpYAFoi7svNgJOOHcXGnnVWBCKzs+UO9fLLEr5u3x7eeEOSAOoWrcNhUFwMDz8sgz/DkJv400/DZZdFkPrgcsFjj4kTGggQ6NyZovvvp+j883GXOhuVbXFVEY/KdtjttjB3bjc+/7w3hYVxJCSI+t1xRwTtdbtFZ//xD9Hn+Hi5CHffXR8DQe14RAOvFx56SHTSMGTJ9eTJkmgXNj6feK3//jcEAvi7dyfvhRco7NMHv9+P2+0uO4KdjmDnI1jZgxU+eGtigLVrOzFlyqnk5yfidPq49NIVDBv2K3Z7iBwPqxWHw1F2BDsd5hEfH0/Cd9+R/Ne/YtmzR8I+EyaI0tddyE8b7Vry3nti7PLzJcn5//4Prrwywq/sww8lrn3oEIeAu4D3FLW3Ir2AycBAQPycF1+UKc6w2bhRRhI//ijPx4+Xi1J3y3C1DteSFStkCuW33+T++sADYkYjKuGyapUI3bgRw2rFddtt5N59N26LBZ/PV8EOV7bFpuNhGMZhtrjyNvFFRXF89NGfWL78GEDyTqZMCTMP0CQ3V6LOkyfL88GDYepUKfxUd2jHQzUHDognvWCBKPr994tjHJGiHzwoiZpz52JYrXjvuov8e+7BBbhcrpDKXlXEoyaOB0BRURyffXYaK1ZIOG7w4F8ZM2YJ8fEcFvGoieNht9tJcLtJfvRRbObuhlddJcPRiO4GNUYb7Rri88k+UC+9JM8vuECmiiNK0fH74cEHZe4D+Aq4CSotklWNDbgPp3MCHo84/l98IQGLsPH7ZfrlkUdkFNmvH8yYEeHdoMZoHa4F778v0Tq3W3ImJk2KcPAHst52/HhwuQj06EHR//5HUZ8+uFwuXC5XhUHgkRyPI0U8Kt9/1607mo8+Gkp+fiItW8L06WEmUAczc6Z447t3y5z/tGkwdGiEQmuMdjxUsm6dLCPctk2M9PTp4lBGxPr1InTzZoxWrSiaPJnCE06gpKSkTNmj4XiArGxZt64nn356Ll6vk06ddnPddbPIyHCH5XjEx8eTkJBA8pw5xN14I5aiIkV3gxqhjXYNCPJxcThkduH66yOMcuTlSajk66/xIlGOVxW1t2Ych2SQHE2bNqJugwZFKHL9evHIfv8ddXeDatE6XAMq+bjcfLNEuyIa/AUCEip55hkAvGPHkvf005QYRpkdjpbjYbFYKC5O4KOPzue337rgcEju83XXRdAfkOjH1VdLxUq7XbLGb7klQqE1Qjseqpg3Dy68UAqAKbuXLlwoa2wLCgj060f+u+9SmJFRwemoreNxpByPypirWvbvb8fHH19JXl4aqan5XHfdx7Rrl1+jHI/4+PjDHI+EhASSt24l8corsWzbJkVzvv1W5qSihzba1bBjh9QR+O03Kfs8fbrshB0Ru3ZJVdANGzgAXAosVNDW2tMc+BQ4HadTZnwuvjhCkbm54qV9+614ae+/L8+jh9bhanC55CvIzFR4L/V4ZGpl2jQMmw33hAnkjRlDictVpS2OJMejKlsMdubOPZtly2Q0e//9MmMd0aDA75e8gH//W57ffbcsWojuFLh2PFTw9deyw7DbLQO7t99WMHswd65EOkpK8F10EYdefJFioLi4uM4dD6vVSklJCpmZY9m1qwtJSQVcc837tG+fG7bjkZiYSFJJCcnjx2NdtAhatBADHr3litpoH4EtW+CMM2Tl3fHHi+GOePZgxw7JRt2yhV+AkcC2yJsaAXZgInALNpvMl48aFaFIn09W5kycKJl/kydLUZ7ooHX4CJSUyOBvzhzJSfrsMwWzBy4XXHopzJyJkZpK0bvvUnDSSWV2uC4dDzPBPyvrJObOvQyfz8Idd8iUaMR+wpQpEkLxeOCmm6T4WfSKjmnHI1LmzJGghMcjnvX//qfg+1qwQAojuFx4x44ld8IEit3uCspeV1MtwQrv98cxe/bN/PHHsSQmFjB69Ju0bJlT66kW0/FITEwk0WIh9brrsM6eLXONCxbIhKx6tNGugu3bpVLujh2y6OjrrxWsFt21S4Ru2cJK4CwgV0Fb1fAP4BGsVpmyv+yyCMUZhqwWePxxuQNMmSL5S+rROlwFbrfMfH3zjcx8zZsXQR0XE48HLrkEvvoKo3lzCj77jIJu3SguLq7S8YjWVEtlW7xjx/F8++2NeDwW7rpL0o4idj5mz5YRtMslN7OXX45W5EM7HpGwfLl41CUlcNttkuAe8fe0apUY7KIivOPGkTthAkUlJYcpe304HlarFcOIY968u9i1qxdJSYcYNeoF0tOLw3c8EhNJsttJvfZarF9/Da1by5LhTp0ivJCHoY12CHJyxNn4/Xf5O2dOiF00a8uhQ5LctHEjK4FhQJ6CtqrlSeAx7HaZ4h4+XIHIf/5Tkk5tNgkZqV9uq3U4BIEAXH65RDhatpSxS69eEQo1DJle+fBDjIwMCmbMIL9z5zI7XN+Oh81mY/fu45k//068XgtPPimLGCLm229lJO12yxTMP/+pQOhhaMcjXHbvllyOPXskyfmttxREOvbtE6E7d+K+7DLyJk6kqKSEoqKimHE8bDYbhpHAggUPsH9/D1q02MZFF71AQgLhOx5JSSTZbKSOHo31u+9kuuWHH1QvU9RGuxJerxTknD9fLvl33ymIdPj9Ujp09mzWIQXNYyfSUZnngHtJTxdf95hjFIh8+GFJQGzWTJbdhl1hLSRah0Pw+ONSwj81VbZG6dtXgdBnnoGHH8ZISaEgM5P8bt0q2OFYcDzE+TiJ77+/m0BAFqZccomCvs+cKeEjvx8++EDyB9SiHY9wKCmRinIrVsjfOXPCqOtfGbdbJtmXLME3aBD7PvgAj8VymLLXV45HZYX3+9OZP38CRUWt6dJlCaef/g5xcbXL8ajgeCQlkezzkXLmmbLp3KWXSn15daE+bbQrceedEqVr3Vp0OaLCdib33QfPP082UkFDwcbfUcQCTAcupGdPWLpUwaa0hiHD72nTpFLV8uWyDa8atA5X4tNP5XJbrRK5OvtsBUJnzIALL8SwWMh/7z0KTz+doqKiGjkedZHjEWyL7XY7mzdfyNq1Y0hMhCVLFO1r+L//SRGf+Hj4/nsF65AroB2PcBg/XnLIOnUSg92ypQKhN90Eb7xBoH17smfNIj8hAY/HE7OOh91up7i4K99//y/8/gT69ZvK8cd/G5njkZxMwrZtpJ59NpaCAgnzPfSQgosr3VElqJbEpB5PmiTVv51OiXT86U8KhH7wAYwejReZXllU3ftjgmSkZmofRo6Ue07Evm5RkSwHWrtW7oSzZqlK1NM6HMS6dTI9WFwsxRrvuUeB0I0bZTvawkKKHnmEA9dfX2aHY9XxsNnsrF17Dzt3ns5RR8mmy7Xa1TwUhiF1Pt56S5Znrl6t6EYHaMej9nz+uSzDS0yUEZKSsN7MmTBiBEZ8PAdnzOBQ164UFhbi9XoPU/hYmGoJLiB24MAprFnzBBaLl+HDH6Bly33hTbWUOh5Op5O0JUtIGTVK1sOtWKFqpYs22qVs2yaJd4WFYlcirgcAkkzauzfk5XErdV2nI1K6ACuBDN58U+qWRMz27TJKPHBARo+33aZAqNZhE69XarGsXi2brk6erMBh9PnEYVy+HM9FF7HvxRcpLCqqleNR11Mtpi02jDhWrfoveXnHcvXVsrI7YjweicIvXiwZ2OYOp5ET8puK2hqahk5OjhSjAdmlU4nTkZsrniXgevhhSnr1qrChUE2OYE/6SOdqc9RUZnr6Ijp0mIlhOFi+/Bbcbn/EMktOPx3fLbeIIRg7tnx7Z03EGIbcWAsLZT742msVCb3hBsjLI5OG5nQAbAVuB2TUvGOHApGdOkm5V5CCC1u2KBCqMXn2WXE6OndWuPjiv/+F5csJtG9P3r//jbfUJtXUbtWnzQ4Eiund+1lsNjdTpkhuc8Q4neLBJCXJnNannyoQWjU64lEFV10lhYeGDJGEPCXR03Hj4N138Z98MtnTplFQXExhYSFFVXjaoSIeqvdqgYpetulpB3vZwXu1WCyprFr1Lm53a4499n16954RumR6abSjQi2P0miHeTidTpKTk0mxWkkfOhTLli2Srv3kk5FeaT1aBF5/XZznFi1kQ81WrRQILZ23yQV6A3sUiKwfpgMXcfbZsqRYyc3siitkpDh0qKzxjMxoaB0Gfv5Zgkler9jh009XIHTDBtmu1u2m4NNPOThwIIWFhYdFnk1bHGyHTVvsdruV7tViUpOIh1lBeu/eUWzefDtt2sjvO+IpF5AyqbfeKlMtWVkqplz0VEtNmTdPqjomJsrc4tFHKxD6/fdw2mkY8fEc+u47DrVuXabshYWFMZ/jEVy5tKhoMBs2TMRi8TB06E2kpeWFleNhOh7JycmkrV1L0nnnyZTLhg0R7A0t3YnknyMgZvQ4J0f0Nj8fPv5YkvIi5tAhEZqbyxhgigKR9UdrIAtoru76ZGfLFFR2tuy6F1lxsSavw4Yhm6UtXiz3wpdfViT0zDPhu+/wjBlD9oQJIe1wrOZ4BNthu93Jxo2vkZ9/vLrrEwjIevP582Ve9q23IpWoHY+aYBiyDfjKlbLK6sEHFQn9859hyRJc99/Pgdtvp6CgoErHo6ioiJLSeh6mspeUlFTpZdfE8fD7/RiGUcHxMAyjtDxv7RwPh8PBjh0TyMk5m7ZtZ9Ov38QyxyMuLg6Hw1Ejx8PhcJCSkiJRj5QUMu69F/v778uSrg8+iOSKN3mjfe+9koR31llSK0jJiP6hh2DCBL4DzlAgrv65EXidY46RwZ3DoUDk5MmSkd65syQvhr9pSJPX4S+/lILOLVrA5s2yajli5syBs8/GSEvjwLJl5NtsFBYWUlBQUGXkuSaOh9vtxuv1VnA8vF5vBftbW8fDYrFgs9kqJZeW22SHw4HP15316z/CZrOycSN07argGm3aJMVRDEN+GMceG4k07XjUhGnTJLembVsptJSYqEBoZiZccAFGixZk//gj+YZxmOMRKrk0ViMeDocDwziaX3/9AsOwcNJJN5CRsSeiiEdKSgqpeXk0GzAAi8cjk7rhJ5o2aaO9Ywd07y6rtletkqhyxOzZI1atpIRBwHIFIusfOxL16M5rr8lis4jx+yUhbP16qW99553hSmrSOuxIjaxHAAAgAElEQVT3y1LRrCzZ9O2uuxQIDQRk3mb1akqeeIID115LQUFBBVvckCIeZiHHPXueJifnAgXjtSBuvlnmai++WKq1hY9OLq0On09qAoGkGihxOsyNeYCSe+7B5XCUOQ6mAh/pcfDz4HOh3lfVe2t6rjYyDeN30tOnATZ++228EpklLVviK02+VRNqapo8+aQ4HaNGKXI6QMqEl5QwncbidAD4APnBP/mkLNWMGJutbFdTnn4aCgoUCG16TJ0qTkenTuVJ/hHz6aewejWBdu3IHzv2MJsbbJ+qs1vh2Nxo2eyMjP/DYnHz4YewZo2ia/XYY7IJ2fTpUnFPMdrxCOKrryTKdPTRipYdgmwokJVFoEMH8q+66jDlruoww3eqjuCwX2Uv25yC8fv9+Hy+Gn92Wtr/YbGUkJt7KocOtVLSzqK77sJITpb5gawsRV9C02HvXklOt1rFV1BCTg688w4BzNt0Y2Ia8BN79kgyuRLOP1+KTmRnK1rr2LQwDHj+eXn8xBMRbnEfTKnQknvvxW21KrWvtbHrwZHoI9lir9dbI5mBwDbS0z8CZLGOEtq1k6JiSoWWox2PIF55Rf7ecYei+d4goe7rrsNjsRymhOZNvvLj4HPBR1X/X/mcefj9/rCaHQgEyhQ/1Od7PB4Cgd2kpMwEYNeukWG3s4KzlZKC3yzb+2rDW6xZ37z9tqwAOP/8SPNzg5g0CdxuZgMbFYmMLV4EJDlPycyzxSJbjoP8/mNjOrvBsHixJPW3aqWwgveKFbByJUZGBoUXXVSlza2p3aqJLQs+J8tgA2E13e/3V3sfSE6eBAT4+GMpJ6OEO+6QCN706TLVqhDteJSyaZPsmZOQIOUklLB1K8yaheF0Unj55SEdieoUtioH5EiHGd1QgWEYZUlTodqclPQuAAcOjKSkxFKr9oZ6r9vtpmTcOPnw997Toepa4PPJtCzIKgAlBAJlDuArikTGHp8CB1i9WqqeK+Gii6Q+fVaWrGjT1BhzAHj99QqjHaVCPaNH47HZIrKvtbFlZlK/CsxSCqE+3zC2kJi4AI8H3nlHycdBhw4ygvH5VKxuqYB2PEoxDfZVVyncbuGNN8Aw8F58Me5mzap1Firf4EOdq+5Q5XBUxgz9VW6fxbIKp3M1gUAzsrPPqFV7Q73X6/XiOuYYAqecIk7H1KlR6U9jZNYs+OMP2QDtzDMVCf32W9iyhW3A14pExh5u4G2g/KYXMU5nWbFAHbmrOdnZkuBvtSpK9gUp3PiRTEUUXn11RPa1KvscyjZGi1AREI/HQ2KiDAJffVXGC0owq/C+/rrkKypCOx5IJNQs1KakhHIloe6rr65QQCZaR12sUDIzsoM/NzlZftS5uWeG1e5QMn1m1GPatKj3qbFg6vB116naLqRc6DuAKlsWm8iIbsYMhcVzzUSxL78El0uR0MZNZqZMFZ59Nhx1lCKhX30FLhf+oUPxHnVUVG2wacuiTeWiZH6/H4djPomJB9m2TVazKeGMM6BLF9kmQWGSqXY8kP2d/vgD2rSRGh5K2LgRNm/GaN6ckv79wyplXpsjWpGOyphTL8GH3f41EKCoaBAul01Jf1zDhsn84sKFkJdXJ31ryPh8sg0QwIUXKhIaCIjRBr5QJDJ2+R3IIi9P4cxIp06yrKioSHbn01SLWf5bmQ4HCfX85S9Rt8Ner7dOBoBQHoUuL6XgxmL5KrjLkWOxwAUXoFaodjwAGZAAjBypcKRYKtR31ll4SxM1G7rTYVLZ+QgE9uBwrMYw4jh0aKCafqWkYJxyitxRZ8+u0/41RJYskYhy9+7Qo4cioStWwL59bAPWKRIZ24hhNe2BEkaORL3QxklJiczsAYwYoUio2y0rCwHXsGGNxukwqex8OBwyIRoVHdaOh1rML+n889UL9ZxzToViMlUd5V7rkc+FOuoDwzAqtNPpFOcgP39ojfpbk777/vIX+TCFCt9YiaYON52rLz1Vqm7mF5KZqVe3VMP8+eJ8DBggqzmVsGgRFBQQOO443G3bhmVfa2q36qsYZ3CbrNaFxMV5WLNGovhKOPVUSE2VrSx+/12JyCbveJSUyHyY1apoAyKQSeIVKwBwnXJK2Ipdk6M+Met++Hw+bLb5AJSUHK+sb54hQ+SDli6tx142DJYskb/DhqkXOlehyNhmOZDP1q2wb58ikf37y+5du3bBzp2KhDZOoqnDviFDomqH6zrqXJnydhRjGAsBhWbT4Si/OSoS2uQdj7VrJVm3Vy/ZEVgJWVngduPv2hVvUlKNFTf4Rl7VueCjLpKYqqP8R7cO8OD1Ho3HE1frH22ovnu7dcNISJBlyTk59d3VmMXvL69YOGCAIqGBAPz0EwArFYmMfQLAaqCs65FjscCJJ8rjlU3nSoaDeXkGDlQv1NuvX63ta23sVn1jbpEhUQ/RYaXqZhoWRT+MJu94mNfRtA0qhfr69o1qBnUsYGZXBwIl2GwbACslJceq6aPFIhs2gMI07cbHxo1S7rtTJ2jeXJHQzZshP5/dgNrSQbGO/HaVOR5QblyUCm1cGEZ0bbHnuOOiZodjZL+zsvaYjkcs67B2PKKp7FF0PGJF2aFc4W02UXiXq7eyfgbMzUa00a6SaOpw07vq2vGoD3bskKBm8+YKl9Hu2QN79mA0axbVZbSxgjkIDNZhZbcJU4dXr1ZSz6PJOx5btsjfnj3VC/V27XrYzrBVHWU32mrO1dU68dpgttVikYLaPl/HGve7ur77zSUa5helOYxo6vAGhSIbBusBxerWqxfqhTYugnXYompf3lKh/m7d8JdFZmtmX2tqt2LRFhvGLpzOEvLyZKWbElq2hBYtZGl4dnbE4pq847F7t/xVlkUdJNTXunXYSl3dEWtIm0r77VPY79at5QPML0pzGOalad9evdCmd9VL+62y46Zx0TpcJdHU4UCbNk3KDhtGAJ9PlrTEqh43ecfD3PtGqeNRKtTfqhWGYdToMKnuXOXXYgXDMLBY9gIQCNS839UdgTZt5AMUb1LUmDDtQNu2CoWWXu+md6vMBnzk5EgJCCWkpkJ8PBQWyqE5DPPnHQ0d9puDF2puX2tjs2MJs22mLVZqNs0vR4HQJu14FBSIHYiPF9ugBLcbcnIwbDZ86enlN9BSD7nCTTXoXOXHVZ2LbYXfBYDf36rKflY+F3w+1Dl/q1byAdrxqJJoOs9N76obgBjtvXsVibRYyr8crcchiaYOB4IizzWxr7Wx2bFI1BwPhTrcpB0PsxJ3RobCecX8fACMtDQMi0XZyL8heNpwsPRxmrqIR1qafICyycrGx6FD8jcjQ73QgwpFNhyk10pVzvxytB6HJJo6HEhTZ48aii22WGJbh5u04+Et3UDQ4Yie0COF9YLPVX5c1blYVnbwlj62V9nPUH044jnzy4mBtfKxSjT1OHp7bMYy0mulKqf1+IhEU4cNu73sVE3sa21tdqwhbRM9i4oOeyO3Ck3a8TB1R1m0I1gotVPOmt6QYxuzrTW7oKH6dtg5c/OcGA1rxgLR1OOGpH3qEF1TqnLml6P1OCRRMXNBP4xo2NfYts1R0GGFtrhJOx6mI6x0KXaQUEst7gQWi+Ww94c6F9tI3y2WmrnZofp22DnTZVc6FGpcRFOPbQpFNhyk70pVTuvxEakLW6zavsa2bZZfbqzqcJN2POLj5W9RkXqhluJi+VtJuc3nlc9VflzVuVhVdmlXYukzV5X9rKkBKHtv6XUs+7I0hxFNPVa1i0DDQnqtVOW0Hh+RqNrikpKyUzWxr7W12bGGxWLBMKKgw+aXk5AQsagm7Xi0aAE2Gxw8qHDpXLNmEB+PpagIa3HxYcqs4ohFpF2y3MpqzVbWV6u5W5e5rFZzGApXuZVTer1Vrm5sOEivo7G0U+txaMzLomwlUZBQ2759UbHDsWyLDUP6rlTdFOpwk3Y8rNYoKHzQ0jl7dvkN2Gq1YrVaK95Ug85VflzVuVhWdtNg22z7quxn5XPB50Ods+3fLx+gdJ1d4yIqjkfp9W56jkcykBKVJfbYbFIBUnMYUVltXCrUun9/rexrbWx2LBLseERjebIKobF55eqQqBjtUqH2IIWv7qjpDdk8Yg1pkyikzVazfofq22HnzIiH0uFn4yIqhTFLr3fTu+rS43btFCbrmqOaNm3KE/Q0FTB/3tHQ4eCIR03ta03tV6xR3rYoRO0UVips8r+CDh3k79at6oU6du6ssRLbbDZsNlu152Lb8egEgN2+t8Z9rvbcH1L6V20t5cZFNHW4i0KRDYPOgGJ127YN9UIbF9HUYeuOHbW2rzW1X7HmfIjTkYRhtMJmg6CirZHhdsMuKRCpHQ8FRGXX9VKhjnXrmsTcovlD9Pv7AeBwrK9dHscRQprW1bLjLf361WMPY5u+feWveamUUKrD/RWKbBhIj027oARzV1qtw1XSs6cslti0SSpKK6F7d4iPx7Z9O9a8vCZhi2WapS+BgIVevcDpVCT4l19kVUv37pCYWP37q6HJOx5R2bG6VKhj3boyr1r1EUsKL6MGW5njkZCwXlk/yxwPpXu+Ny76l3oHP/+spLaP0K8fWK30BprWOgzRM6XqZhoXrcNVEhcHffpI6Y01axQJtdvLPEhnVlbUbHGsYLFYsNlsGIYYhFjWYe14lF7HVasUFrEpFWr/5Rds0KgV3lR2q7UThtECqzUXp3OPkv7Zc3Kw7NoFKSlwzDH13dWYJTUVunWTaGhWliKhiYnQsyd2oK8ikQ0D7XjUF9EcBDp//rnRDwLN9gQCJwCxrcNN3vFo106OvDyJJimheXPo0gVLcTHxGzZgt9urPcputtWcCz5iIdfDbGcgMBiAuLh1OBzV97cmfXeuWCEf0r+/TsqrhoED5e8PP6gX+meFImObtsDRps+lhuxsmT9wOmVIr6mSaOqwY9myWtvX2tit+sZqtZa1y+8fBMCAAQo/YPFiVArV1hw4+2z5+9VX6oXGz50btmLX5KhPgn+0Pt+5ACQn/6Csb47Zs+WDzjmnHnvZMIimDo9QKDK2kZ4OG1Ze9DJiZs6UUOoZZ8h8gqZKzjpL/s6Zo7Cu0vDhANgWLMDu9UbNDtd3BNpsh8VyLH5/VzIyFDoeW7ZIKDUlBQYPViJSOx7A+efL38xM9UIds2fX7CbrcOBwOKo9FyvOh8ViCWpDAh7PmQCkpi6qvYMRqu9WKzbT8TC/IE2V/OUvEhSaP79sg+TIOeccsNs5FUhXJDK2ET1Tqm6mUdE6XC2dO0uidEEBLFyoSGj79jBgAJaSEhKXLg3LvtbUbtXXlEtwm3y+vwBw3nkKnecvv5S/556rLFtVOx6IUxwXB8uWKSwkdvrpkJSEbe1a4rKzy5Q0Gkdde9sWi6XC5xvGKRhGKk7nbyQl7VXSp/g1a7AcOABduyqMezdeWraEP/1JkkvnzFEkNC0NhgzBDpyrSGTskggMw2KBEapCPC5X+ZcxcqQioY0b0z8z73UqhcbPmRNVO+xwOOrc+bDZbBU+3+M5J7jLajC/DIVCteMBJCVJeNUw4KOPFAmNjy8LVSfOmBF1ha8r56Oy0+FwOHC7LwWgWbOFyvoT99ln8oHnn69429XGi2kXpk5VL3S0QpGxyUVAPIMGKax9kJkp+1v0719eqEJzREwd/uQT8HjUCnV8+SUOn6/ROB+VnQ6LpQsezwCczvJpq4jZuRMWLJDwybnqhh/a8Sjluuvk76uvKlzdUio0fvJknDYbTqezysNUnurOHemI9rSL1Wo9rE12e0uKiy8AoFWrmTVu6xH76XZj++AD+dBrr41qnxoTY8aIfcjMFHuhhKuugrg4zqGxFxO7FSi3A0p45RXUC23cDBgAxx0H+/fD9OmKhPbtCyeeiOXgQZK//jps+1obmx3txH+73X5Ye9zusYCVyy+XLcOU8OabsmXwxRdDRoYiodrxKGPkSJkO3LRJ5smVcPbZ0KUL1m3bSPrhh7AVvLaHaqW3Wq1VfpbLdQWGkUBKylKaNdurpP2Jn3+OpbAQTjtNrwSoBW3awCWXQCAAb7yhSGiLFnDFFViBmxSJjD36AX8iNRWuvFKRyKwsSVRIToarr1YktPFjscCt4gOW+W0qhSZMmlRndjgaA8FQDoc4PMkUFo4Cyq9fxHi95YZEmVBBOx6l2O1wU6ll/d//FAm12eDmmwFIePvtI9esCMqOPtK5uq7zYdbpCHVYrXby8yUI36rVNDXr4q1WnG+9JR+uWNmbAuYle+MNhSsDSoVeR2MtJib9GzdOpl2VYN41x4xROPxsGoweLQsovv9eiuIpYdQoSEvDtnIlcaWFHSO1rzW12aqmXo7UlpKSEfj9GfTrByefrOTj4PPPJemxVy8ZBCpEOx5BXH+9JO1+8YXC6nnXXgsJCTjmziUpK4u4uLiyw/RWQz2u6lxV/1+VzHAV35xWCZYZ/FkS2rsYr7cLTuceWrVaEfJ91bWzssyk2bOxZmXJfgAXXaToS2g6nHqqRJb37YPXX1ck9KSTYMAAWgC3KRIZO3QBxmKxwC23KBK5Ywe8/bY81s5zrUlJEScQ4MknFQlNTCyb8kr573+V2dea2uxwp1/MgV8o+eWyEzh06HYAbr9dUUqc3w9PPSWPb7tNeZ6ddjyCaNtWrjHAgw8qEtqiBdx5JwDNJkwgLoSC18XhcMiyr+A9UYAKe6OYHnuoH+HhP8pkcnLuBqBjx0nEx9sjb6fNRuIzz8h1e+wxhRsNNB0slnJ78fTTiva9sFjgH/8A4CFA1W7xscFTgJMxY6BHD0Uin3hCwk1XXKGnCsPkgQcgIUHyPJYtUyT073+H5GScc+aQvHp1vdhhcwpGIsYVd7gNtsXmct2ayCwpGYXH04Vu3eCaaxRdqylTZLqwU6eo5CjZnnjiCeVCw+CJ+m6AyYknykgxKwuGDpW15aqE2jZtInDyyXg6dsTv9xMIBMoOq9WKYRgEAgEMw8AIynANfhzsNR9pw7WKUyLWkKHAI61NDz7K5xHLHxcWXs2hQ+eRkLCVnj0nEh/vJD4+nvj4eJzO8sfx8fEkJCQcdiQmJhIXF0diYmLZkT59Os4PPpD632+/LVNV4aFqnFRbnqinz61Ajx7w7bfw66+yTHzoUAVCu3aFhQtJ2LYNP6AqDap+6Qu8jNNpYfp0WT0cMevXy5ytzSZ3zfAT8pq0DqekQGGhVDH9/XcYO1bBoDspSZbKLFyIY8sWXFdeSSAQqGCLLRZL2WPTDgcfwQRHk0NteBlqx+0jTdlUrg1Slf2tmNyfws6dLxEIJPHKK+UbRkaE2y3JpHl5MHFipGXSQ+qxjnhUokULuO8+efy3v8mGfBGTnl4WQmn21FPEW60Vbszx8fFl3mt152pzRENmfHw8dnsb9u2ThJhjjplEYqKzgvzatK/MOXG5SHj2WbleTz8tW1VqwsJiAfNSPv+8RP6VCJ0wAYC/0lhWuPwXsHLrrTKwixjDEKMRCMANN4gDrQmb++8X07lgAcyYoUjo3/4GLVpgX76cZrNnx5x9rU5m5dcOHboBr7cV/fvDZZcpukYvvCBGo08fSbiJAjriEYL+/aUWwsaNUo7j1FMVCf3wQ6ybNmF3OnEPHhwy4lGVd21Sefv4w7aRP4JnfaR9CY4U6ajscf/xxxMUFfUhPX0tvXtPJT4+yIFISCiLeFQV6TCP+Pj4sscZjzyCfelSKcn7n/9EOrxp0qNFkBvpunWwdq1E766+WsGIsUMH+PVXHL/8Ql/gPRUNrTduAu6kRQv4+GMlO33D5Mmiu2lp8OmnsqIlfJq8DsfHy3TL7NmyQKg0XS4y4uJkV8WvvsK5dCneq67CHx9fIeJRXaTDxIx4HMkO19YWV2eHzWlzp9OJ39+bLVueBGx88AF0UTEaWL9eEnH9fnj/fRWbc4bUY0tVF7WOiYlGBDN3rlQ0dThk51olU7ULF8LQoRh2OzmzZpHbuTOFhYUUFRXh8XgoKiqiuLiYoqIiSkpKKC4upqSkBJfLRUlJCW63G5/Ph9vtLjs8Hg9er7fC4fP5MAwDn89XFkb0+/0VpnJApnAqhwqD8z0q/0isVit5eWeyYcPT2Gwuhgy5g/T03ArzjRIRsYd0OJKSkiocTqeT5ORkMpYuJeXKK8XSrFmjYrK9viqOxZQe790LvXvDwYOyHP/66xUIzc4WodnZ3AaoWvEYTKdOUsehpCQKwuUTgHVACh9/DJdfrkDkzp1yXfLz4b33ZDVLZGgdRoJHQ4bIlMvVV8u9UInQ4cNh/nzcF1zAvokTKSgoqGCHzcO0wcGHy+WqYIs9Hg9utxuv11tmj53OYhwOF/v2Ocvsr3mEmsYBKthic4AZPFUenBtis8Wzbt2bFBZ259Zb4eWXFVwXnw9OOQWWL5e8DnN1YWSE1GPteByBm2+WfI/+/WHJEkV7PN15J/zf/+Hv3Zs9X3xBoc/XYBwPny+DFSvexuNJp2/fNzj22HmHJaLW1vFI8XhoccYZWPbskXmBv/1NwUXWRtvkww+lBlhKiixNVJKz9NlncOmlFCEVMH5XINKkXTsJHMyYociYHoYVmAOcyaWXSmAiYgIB2Sznm2+kINCMGSpWAWgdLuW33+D448URnT5d0WK3bdtkNFlURP7rr3Ng2DBljodhuLjxxh9wuWxMnNgvKo7Hjh3XsnXrNXTuLJHNyIJrpUyYAA89JJHNX36RyFDkaMejthQUSBW97dslzPfWWwrsSVGR/Io2b8Z9+eXs+/e/KSwqwuv1VlD2ygrvcrlwuVwVHA+Px1N2BDsdPp8vpLJXVvhgQoUKK0Y74lm+fAK5ub1p0SKL4cMnEBfnqBACLM8BsVeYagmeXjGdjuTkZJwWCy2uvhr7okWy0ciiRZEklFbojgohYRBzemwYUlTs888l8WzxYkVG6sor4aOPyAIGAyoWz6SlSQ2dNm0k4mjmqajlWeB+WrSQKahWrRSIfPRRyUtKTxeD3a6dAqFah4N56SW4+25xoJctU7R902uvwS23YCQlkZOZyaGjjqrgeJh2ONgWm3bY5XLh9/sPs8U+n4fhwxfTsWM2Ho+Nl176cwVbHDy9fiQ7XNVCAbvdTnb2yaxa9RhgZd482fg4Yr75RpznQAC+/lrljuAh9bh+91WPcVJSZHD35z/DO++Iv1C6MjZ8kpLEbf/Tn4j75BOa9+pFYOxYPB5PhWhEMMFK6PP5Qs4RhnI8giMelRU+1GdU5XjYbHZ+/vk2cnN7k5CQw9Chr5OUlFChzkdw8pPD4ajS8Qg+Uh56SJyO1q1lol1R0TNNORaLLBD65ReJeIwbJ/tgRFzc9vXX4eef6b1+PVOAC4nsjhUfLwOuDRvEN+/aNcL2hWQ0cD82m1wDJU7HJ5+I02G1ykZPapwOTSXuvFOc5k8/la1Xli8XPy8ibroJFi/GMmUKGePH45s5E1dyco0HZj6fr0I0wm63ceKJK0lM9GOxGMTF+UhPt1NSYq+x42F+Vqg8EbvdTnFxZ37++e+AlaefVuR0bNokS78DASljoM7pqBK9qqUaTjwRJk2Sx/fcI8sUI6ZvX5kHBhKfeorkRYuivnFRJMeOHSPZtm04NpuH00+fSGpqcYWlXZUToWoiM2nKFOLefFNqdXz+ud5EK4qkp8v+Lc2aiSNt1vmIiGbNZEohPZ3zgX9GIMpmk9IXW7ZAbq5Ma3booLqMy0BA5qwnTpTNoyNm1arySlf/+Y/Cnbk0lbFYxA6fcIIsr73iCqnoHbHQN96AgQOx7thBxk034TCMsO1k377raNXqAJs2dWf//pbk5KTTrl2RMjscCKSzbNlD+HyJXHGFzIpEzKFD4snl5ckc1uOPKxBaPXpVSw3o00eUfNEiCVYMHQodO0YotGdPsFqxfPcdCd98A6eeSqCS0FCrV4LXhB8pc7o2q1eq2igpLi6O3bvPYOXKGwALQ4e+Q7dumw9bAlY5nyMuLq7KSEdSUhJp33xD4h13YDEMGY4r24e8jCa/IqAyLVpAv37wwQeyPLF5cxg0KEKhGRmyq9fUqZxqGBQAS8MQc8458nt65hnJq/rkE8nT/PVXOHAgwjYCcBzwLdCMm24SJyfiKdP162VL67w8GD9eGq+2uqPW4Uo4nbJB6gcfSF7Dpk1yr4woeudwyBTDhx9i27CB+M2b8YwYcZjQULbYTLi3Wq0kJR2iV69FLF58MV26/M7Bgy3x+ezExxscPNgu7JWEph22WNL5/vtHycvryAkniM8fsWNeUCA/vrVrJafgq68UJTJWQK9qiYRAQIrYTJkig73Zs2XlZ8RCb7gB3nkHIzmZQ1Oncui442Imx2PbtsEsWnQDhmFl8OBMTjppfsgfSajk0qqmWtLmziXphhuw+HxyB4iOh63nx6vg9dfLtg/i5ZcVVfSePFluvsDdwEu1/HerVX4KzZuLH3rxxRJd3LRJIjWR0QeYC7RmxAiJ+ERssDdskBj33r1iuL/4IhoGW+twFaxYIT5ffr6UmZg8WfbaiojVq+U7PXQIzwUXsP+FFyj2emuZ4+HC6/Xzpz+9xvLl55KcvJ/WrbeyePEZEeV4+HzJzJ17D/v3d+Xoo2UA3L59hP3Nz4fzzpPlQp06idCjjopQaEh0jkckWK0S6vN4ZEQ2fLgYxYjm2KxWCfV5PFimTCHtyiuxTJqE5aSTqsy38Pl8IT1m1Y7Hxo2nsHDhVYCVwYPnMGTIUqzWpIgcj7TMTBJvvx1LICA1kR97LKLvRFN7brpJChPedZdsD1BUBPfeG+Fgfdw4cLngllt4EUgCnqnFvwcC8nfAALH/gYCsxjHPh88AYDbQnLPOkvyAiJ2O1atl1+nsbPnxT58eDadDcwQGDoRZs8TnmzpVVG/KFMkTCpsTTpDR5Fln4Zwxg1YeDwcmTsSSlFRloqff7z/MFlut+3A6Pfj9nTh0qCUeT3OSkpLCdjxcrmZ8++3tZGcfRadOsnN6xE7HgQMSZV62TITNm+fg7xYAABs1SURBVBctp6NKdMSjlvh8YmenThUve+JEBZtL+f1SZGHyZAybjaKnnuLg6NEUh/CyaxPxCCe5FOx8//0IfvppCABDhszl9NMXl4UVzR9ZqHBglata4uJI/89/iH/xRfmwxx5TFO+uEj1arIZXXinfl+i66yT6EfH98623CNxwA1akuNiNQG02yH34YSnjMnNmhO0A4ErgbSCBESPE6YjoxgQiZNw4KC4W5+PzzxVUtKoSrcPVsHSpOB/5+TJtOH26gtzelSslVyc3F1+/fuS89RaFaWkhIx7By2nNIyVlAYmJv7Np02UhB4G1SS49cKADM2aMJz8/na5dxemI2D9Ytw4uuAC2bpW19fPnK6o8ViV6Oa0q/H4ZsD//vDy/+WZxQByRVPkOBOCRR8rKUruuvpoDTzxBSSBQZ46H253A559fzubNx2C1+hk5cg6DB/9c5tWH43gk+nxk3H479m++kSzCF1+ULRSjizbaNeCjj2SGxOWSukGffSYLjCLis89kp6riYpYBFwF7avBvFov86y23yM664WMFngZki4LrrxenKqJIRyAg26SaWbljx8qcVXQjHVqHa8DatZIbuWOHOB1ffCERkYjIyhKhW7YQaNWKQ++8Q37v3jVyPDIyXiI/vw85OQMicjw2bjyOzMyL8HqdnHyyOFVt20bYry++kCpsRUWyamLGDAXhk2rRjodq3n9fUjTcbilDMXmyggqzH34oRUNcLnwnnkjuCy9Q2KFD1HM8tm/vwLRp55GTk0FSUjHjxn1F9+57KySqWq3WWk21pKxfT7M77sD622+SiPjpp4rWf1WLNto15Kef4MILpfBm+/aybDzixRlr18qoavt2dgPXA19X8y9du0ogbOzYSD64AxLlOKvMx414R+/du+VHPmuWTI0+/7wUlIhetM5E63AN2b8fLr0Uvv9efMF//QvuuCPCpNOcHClpO38+htNJ8UMPcXDsWFxe7xFyPEpISxvP3r3P4nYnheV4eL0O5swZwuLF4j2NGwevvhphtM7lkh/Xc8/J86uukqJU0YvWBaMdj2iwfLlkVu/eLd/jM8/ImvOIlP6nn0ToH39gxMVR/OCD5I4bh6vUsVDpeHg8dr76ahALFvTDMCx06HCAW26ZQ6tWxYetkKmx4+HzkfbCC8T973+Sz9Gnj3jXRx+t5qJXjzbatWDvXikytmSJPL/hBrm/NmsWgdDsbNm1auFCACYhm8vlVfH2UaOgZUv4v/8L9wOvRTZ9S6V5c8nDisjHNQxZ8n733bLkMC1NQkRnnx2B0FqhdbgWeDxid19/XZ6feqo40RHt0+f1SiXlUqX0DRxI7n//S1HpQLCy4+H1rsVqfYGCghertMVHcjw2b27L1Klnkp2dhs0G//43/PWvEfq4P/4oYc2NG+Wm9Mwz8Pe/14XjbKIdj2hx8KAk602ZIs9POUUq7UW0m/ChQ5LaX1pExDdwIHlPPomrb18ljkcgYLBuXTs++OAk9u5NxWoNcN55WVx88S/ExXFY5dIaOR5OJ8k//kjC/fdj3bRJFP3eeyVMHfEEe63QRruW+HzibDz+uBjxjh1lk8qLL47ARvn9IuSRR8DtZifwN+BTDr9Qzz8v4WTT+ak5PYEXAHEILrxQRoht2oTZZpB1vPfcI1EOkOz/N96o6+JgWofDIDNTEqj37pWB4BNPSPQjosH9rFnije/ejREfT8k993Bo/Hh8TmcFW1xc/Bp+fx5+/021cjwKCuKYMeN45s07FsOw0KePmP0BAyJo88GDUtjupZdkqrBnTxEa8Rr6WqMdj2gTrPQgkbqnn45w+mXWLLjxRti1CwDvBRdQ+MADFHfsGHaOx8aNaUyZ0pusrJYAdOyYz513ruKYY/KqLJleXY5HYlYWSU8/jW3BAmn3sceKop98cgSdDxtttMMkK0sGSCtWyPOBA6V0eUTRg40bReiPPwLwE/AAssgVJDw+fboESIqLayq0I1Ii4BrARkaGlFsfNSoCR2nnTnGSJ00SpyktTQz3mDF1OUI00TocJpUHgh06iAMydmwEy24PHZLww+TJAATatKHk73+n4NJL8RgGHo+HffuuwOm8FjipzA4fyfEoLraSmdmVGTOOoaTEgc0muYOPPhpB+lBRkSQc/utfUmPGaoX77pMLULeDPxPteNQFubmSHzpxouR+2O2Sa3fbbbLZXFjk5cGzz2K89BKWkhIMmw33JZdQMG4cRT171sjx8PkCrFqVxowZR7FsmWQQpqR4uPzyzYwcuR2n0zjiXi2hHA+H3U7yypUkT5qE01yKkJYGDz4occ/6UXTQRjsifD7Zzfapp8qd6GHDZNbhnHPCrGzv90vs+4knZF4S+A6JVewbCFeNFvnV0xO4DbgOiMduF7/8scciSIzduFGW+bz5psyH22ySlfr44woy+sJG63CEzJkjN/LVq+V5jx4SyLrqqgj2K5o/H+6/X1a/AP6uXSm68UYOnXc6Ww+cTsuWi/D57Ed0PA4etPP11+2YMaMTubniYZx7rtw3jj8+zHZlZ8vv68UXy3+0Z50lQsO+8ShBOx51yc6dYmMnTSqvR3DyyZK1f+mlkJgYhtDdu+Vu8NZbYsgB3wknUDR2LAXnnIPH4ThM2Q8ehC+/TOezz1qyY4fEG+Pj/Vx++S6uvHIXyckip7pN4oIdD2dRESmZmSROnoxt0yZKhcow4/77FWyiEDHaaCugqEgG/P/6lyxZBKk1dPPNEsAI60ZfXCxz5s8+K6NIYNUt8FMBPDYF9ob8JydwAXArMLTs7JVXwj/+Eea+Lm63VGp85RW5mZhcfrkI7d49DKFK0TqsgEBA8n0eeQQ2b5ZzzZpJ9OOmm6RCbq0xDJg2TdZ///YbANlnJvDHtel0aPUuJd264a3keHi9flavTmDatBbMm5eBzydJgIMGye9ryJAw27F0qcwtfvKJzJGCojClMrTjUR/89pvYtkmTJHABco8ePlxWbI0YEcZ89JYtIvSddyTEAhhxcXhPO42S4cPZ2G0Ys9Z0Zs6ceFasiMfvl+++TRsvl156kEsuOUjz5j4Mw6iwFXOw81G5BHvcrl0kzJ1L3Jw52JcskcqjIPPeN94oR/2NDiujjbZCDh6UiqKvvirL/0FmHgYPFh0+/3yZWavVbMShQ6K/r77Kigd/p8dzkLwRlgGZQCaprGck4nCcDaQAssfimDESQezTJ4yOzJolc6KzZ0vJaJBRwNVXi9C+fWspNGpoHVaI1yv35ldflc3mTHr0EP0dOVJWJtYqmuf1yhrwV15h0wnfE78PjvpYoiDu4cPJPXUYc0v+xDfzkpk3L4H9+2Wex2oVu3/rrRKUqNXvxuWC774THf7yy7IpeCwWyUW69VYJS9b91GBVaMejPikqkqT4N9+UgnHB9Oghiajm0bWrjCarqwviyi2h4K2PsU96g/QNFXfJ2MQxrGQAq6398R7Xl5NHt2XwRSlY42xlmdQhHQ+3G0dODs5t23CsW4fj55+xr1mDdceOcuE2m+yydfPN8quNqIBJVNBGOwoEArJ79quvyl9zgAXif5r6O2CAOCJt21Yf2Ssq3Mmq5T3p+eSZpC2Zjd1XXnJsN235iRP5iRPJ7XIiA67pxQU3t6VZm2qE+nyyxnLbNlkhZh7r11csh3rccTKlMnYspKbW/oJEF63DUWLtWhm3ffpp2bgNkEhIsB3u21f0OjX1yPdxw4ClCzvT4eOTaPX+POKLDpa9lk8Kq+jPT5zI1owBHHNJXy66vT1HHVcDoTk5Ejpfs6Zch1evFufDpF078cRvuinahcDCRTsescLu3VKdMTMT5s6tqEcmFots7NW2LaSklCdFeb0yWNyzp+KPpjV7GcFXjORLhvMtiZSE/OxAixYYbdpgJCeXu/c+H5b8fKx79mApDX8fRrNmMhF5/vnyt/6nU46ENtpRpqBAdmrOzJQZi5yc0O9LTRUdzsgQHbZaRYeLikSH+/V7l5NP/oonn/yUJAoZxlzOJ5MRfEUrskMLbdasXKjDUS60uFiE7t8fut663S47PJpD3M6dVV2OaKB1OMr4fBL9yMyU4/ffQ78vIUHUrWVLKURns8n/ut1mOsVWXnzxZC69dA9WI8BglnI+mZxPJj3YFFpofLwIbdWqXKjfLzeDfftEcLBnH8wJJ4j+nn++5G/ETnQjFNrxiEXcbvjll4oO7R9/VG07g7HbZZqmc+eKnnqPLh5sG7MkASpY6L59NRfaqZMo9YABIvTYY8PMKqwXtNGuQwIBMdqmDq9cKVMye/ZUv3X5ww+PZvPmoWzadAP9+pVHTPr2CRC/a3PFiMWWLSK0KoMcTKtWUhHNFHriiZK5VzdFk1SgdbiO2b27orpt3CjqVlR05P8bMeINBgxYxCefTKFPn3J1698f0t17K/4wTKGFhdU3KDVVIhq9e5f/MPr3F4e74aAdj4aEGS3es0cGcl6vDOzsdomAtGsnu3nWqlCZ318utKiootDkZBHaokWE1c9iAm20YwDDkLSK3bslv8nnEyfF4ZD7f5s2AbZvb0v//stISOhcO6F79kjoL1ioOYqsyTxl7KN1OEYoKBB1y84WdfP7xWQ6neLfFhZeRqtWI2nT5praCz1wQOywKdThEP1t0ybMFQgxh3Y8NE0GbbQbAAUFa1i//goGDfq1vpsSi2gdbgAYhp/Fi1sycGAWcXExk1wfS4TU4wY/tNVoNA2T3Nw5ZGREujGMRlN/FBSsJC6ug3Y6aol2PDQaTb1w8OAc0tOH13czNJqw0TocHtrx0Gg0dY7fX0xBwTLS0obWd1M0mrDRUbvw0I6HRqOpc/Lyvic5+QTs9ki2wNVo6g+fL5/CwjWkpp5a301pcGjHQ6PR1Dl+fwlt2oyv72ZoNGHj9R6kTZtrsdkaxeqTOkWvatE0RvSKAE1DR+uwpjGgV7VoNBqNRqOpX7TjodFoNBqNps7QjodGE2N07tyZKVOmRP1zrr/+esaNG1fl67179+bjjz+Oejvqm6bSz4bIypUr6du3LykpKdx99901/r/k5GSWLl1a/RsbOA21n9rx0Gg0IcnKyuKKK66o8fvrymEKl23btmGxWNi5c2eF87Xtp6bueOihhzjnnHMoKCjgxRdfZPLkyXTr1q3a/yssLGTw4ME1/hyLxcIPP/wQSVOjyoIFC7CbO4UGUdt+xgra8dBoGjne6nZqiyEaUls10WfLli307du3vptRI7Tu1hzteGg0MciOHTs488wzSU5Opk+fPixZsqTstXnz5jFo0CDS09Np2bIlo0aNYv/+/WWvDx06lLvvvpsLL7yQZs2a8Z///AeAd955h65du9KsWTPGjBmDy+U6YhuCIxjmiOvjjz+ma9eupKamcvnll1NQUADAyJEj2bFjB9dffz3JycmcdZYUVfL5fDzzzDN0796dtLQ0TjnlFH766aeyzxg3bhyjR49m/PjxZGRkcOedd5Kbm8tll11G8+bNSU1Npc//t3f/MVHXfxzAn4AGjDvuh0CQIhwY1KRY2rSxjLIFETKaM8e58iIxS2thO5xSMxFpaf80ahOUaQSsrNAWhUgaIpnFZGkji5BDI43UFfHLi7vj1R9++eTJKdgXP0k+H9v9cZ/358f78+Z1d08+9/ncJy4ODQ0NyjIfffQRZs6cCb1ej9tvvx0VFRVu/a6vr8ecOXNgNBoRFBSEzMwLl+3Gx8cDAGJjY6HRaJCfnz9sP4eWnz17NnQ6HW677TYUFxcrbSONAw1XWFgIk8kErVaLyZMnIzc3V2n79ttvMXfuXBgMBkRFRWHDhg1wuVwAAL1eD5vNptRUfn4+nnnmGdhsNmg0Gmg0Guzfv9/jNi8+gjF0lKSwsBBTpkyBwWDAsmXLlO0M1UVSUhI0Gg2ysrIAAP39/bBarTCZTDAajXj44Ydx/PhxZRueXmcnTpxAcnIy9Ho9DAYDZs6ciZaWv+9FtHXrVsTFxUGn0+Guu+5CbW2tW7937tyJu+++GzqdDqGhoXjppZdw+vRppKSkwOVyKftdWlo6bD8BoLKyEvHx8dDpdIiPj8euXbuUtpHGQVUicj08iMbSuK7jiIgIiY6OlubmZnE6nZKdnS3Tpk1T2hsaGqSxsVEcDof88ssvMmfOHMnIyFDaExMTRavVyr59+2RwcFD6+vrkwIED4ufnJ7W1teJwOKS0tFQmTJggFovliv0oKysTEZG6ujoBIE899ZT09PRIZ2enTJs2TTZs2OBx/iFr1qyRWbNmSVtbmzidTikpKZFJkybJb7/9JiIiFotFJk6cKO+99544nU7p6+uTNWvWyCOPPCI9PT0yODgoLS0tYrPZRESktrZWjEajHDhwQFwul3z99dei1+ulvr5eRESOHj0qvr6+sn37drHb7dLf3y+ff/65iIi0t7cLAOno6LjsftpsNvHz85Nt27aJw+GQQ4cOicFgkPfff3/U4zBGxnUND2lpaRF/f39pbm4WEZHff/9dDh06JCIiXV1dEhISIuvXrxe73S7Hjh0Tk8kkmzZtUpa/tKa2b98u0dHRI24XgDQ0NCjLTJgwQXJzc8Vut0tra6sYDAYpLy/3OP8Qs9ksqamp0tnZKX/++aesXbtWYmNjZWBgQEQ8v87MZrNkZWWJ3W4Xp9MpR48elc7OThERKS4ulujoaDly5Ii4XC759NNPJSAgQFpbW0VEpLq6WjQajVRVVYnD4ZA//vhD6VNdXZ34+PhccT+//PJL8fX1lerqanE4HPLJJ5+Ir6+vfPXVV6Meh2vAY53924GDwYOuhXFdxxEREW5vvs3NzQJAurq6PM5fVVUlwcHByvPExETJzMx0mycrK0sef/xxt2kJCQlXHTzOnDmjtFutVnn00Uc9zi8iMjg4KBqNRgkFQ+Li4pT5LBaLPPDAA27tr7zyisyePVsOHz4sLpfLrS01NVXy8vLcpj333HOyZMkSERF59tlnZcGCBR73ZzTBo6CgQBISEtzaV69eLUlJSaMehzEyrmt4SFtbm/j5+cmOHTukp6fHra2iokKmTJkig4ODyrSioiKJiYlRno9V8NBqteJ0OpX2BQsWSHZ2tsf5RUTOnj0rAOTkyZPKNJfLJYGBgcp8nl5nFotF5s2bJ8eOHRvWp+nTp0tpaanbtHnz5kl+fr6IiKSkpIjVavW4P6MJHkuXLpVFixa5tWdkZMjTTz896nG4BjzWGb9qIboOhYX9fbfLgIAAAFAO5zc1NSE5ORmhoaEIDAyE2WzG2bNn3ZaPjIx0e/7zzz8Pm2Yyma6qTz4+PggODnbr15W+Yjh37hx6e3uRlpYGvV6vPGw2m9sJnpf2KycnBw8++CAsFguCg4NhsVjw66+/AgDa29uxceNGt/W9/fbbOH36NIALJ5DGxMRc1X5drKOjA1FRUW7ToqOj0dHR8Y/H4UYWFRWFiooKbN26Fbfccgvuvfde5euFjo4OREZGwsvr79+YunSsx0pISAh8fHyU5yP9zdrb2wEAd955p1JnRqMRDofDrX+X1u7rr78Ok8mEtLQ0hIWF4fnnn0dvb6+yzhUrVrjVbl1dHU6dOgVAndq92nG4Vhg8iMaZjIwMzJgxAz/++CO6u7vx7rvvDpvH29v9pT158mScOHHCbdrQm+tYuXSbQUFBCAgIwN69e9HV1aU8+vr6sHr16ssuFxAQgIKCAjQ3N+O7777DqVOnkJOTAwCIiIjAunXr3NbX09OD6upqABc+CFpbW0fVP0/Cw8OHjYvNZkN4ePjIA0AezZ8/H5999hnOnTuHhQsXIj09Hf39/QgPD8fJkycvHHr/n5HGejR/w3/i4vADXKgzAGhtbXWrtf7+fpjN5sv2Jzg4GIWFhTh+/DgOHjyI/fv3Y9OmTco6t23b5ra+3t5ebN68GcCNVbsMHkTjTHd3N3Q6HbRaLX766Se89tprIy6zePFifPjhh9i3bx+cTifKy8vR2Ng4pv0KDQ11e+P08vLCCy+8AKvVqkzv7e3Fnj17lCMUnlRVVeH7779XTqbz8/NTLiXMzs7GG2+8gYaGBrhcLgwMDKCpqQmHDx8GACxbtgwff/wxysrKMDAwgPPnzysnIAYHB8Pb2/uyb+4AYDab0dTUhHfeeQdOpxONjY0oLi7GkiVL/t/huSG1tLSgpqYG/f39mDhxInQ6Hby8vODt7Y3U1FTY7Xa8+uqrGBgYQEtLCzZu3HjFsQ4NDcWZM2fQ3d09pv28tHZDQkKwaNEiLF++XDki0dXVhV27dilHMDzZsWMH2tvbISLQ6XS46aablNpduXIl1q1bhyNHjkBEcP78eXzxxRf44YcfAAArVqxAUVERdu/eDafTie7ubhw8eFDpn8vluuI/C08++SQqKyuxZ88euFwu7N69Gzt37lROrr6eMHgQjTNbtmxBSUkJtFot5s+fj8cee2zEZe677z68+eabyMrKgtFoRE1NzZj/dsXLL7+M8vJyGAwGpKSkAADy8vKQnp6O9PR0BAYG4tZbb0VRUREGBwcvu562tjakpaUhMDAQkZGR8Pf3V8JVUlIStmzZgpycHAQFBSEsLAwrV65UPgzi4+NRXV2NzZs3IyQkBFOnTkVZWRkAwN/fH/n5+TCbzdDr9SgoKBi2bZPJhOrqarz11luYNGkSnnjiCaxfvx4LFy4c07G6UQwMDCAvLw9hYWHQ6/UoLCxEZWUl/Pz8oNPpUFtbi7179+Lmm29GcnIyFi9ejBdffPGy65s7dy4eeughmEwm6PV61NfXj0k/CwoKsHbtWuVKD+DCFSixsbG4//77odVqcccdd+CDDz4YdnTkYt988w0SExOh0Wgwffp0zJgxA1arFQCwdOlSrFq1CpmZmTAYDJg6dSry8/OVy3BTU1NRUlKC3NxcGI1GxMbGoqamBgAQExOD5cuXY9asWdDr9UpNXywhIQGlpaWwWq0wGAxYtWoVysvLcc8994zJGI0l3iSO/ot4gy0a71jD9F/Am8QRERHRv4vBg4iIiFTD4EFERESqYfAgIiIi1TB4EBERkWoYPIiIiEg1DB5ERESkGgYPIiIiUg2DBxEREamGwYOIiIhUc738ZDoRERHdAHjEg4iIiFTD4EFERESqYfAgIiIi1TB4EBERkWoYPIiIiEg1DB5ERESkGgYPIiIiUg2DBxEREamGwYOIiIhUw+BBREREqmHwICIiItUweBAREZFqGDyIiIhINQweREREpBoGDyIiIlINgwcRERGphsGDiIiIVMPgQURERKph8CAiIiLVMHgQERGRahg8iIiISDUMHkRERKQaBg8iIiJSDYMHERERqYbBg4iIiFTD4EFERESq+QuieQKkihBXngAAAABJRU5ErkJggg==\n",
"text/plain": [
"<Figure size 648x216 with 3 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"y_true = fuzzy_circle(fuzz_factor=0.01)\n",
"y_pred = fuzzy_circle((2,0), 4, 0.8)\n",
"\n",
"fig, axes = plt.subplots(1,3, figsize=(9,3))\n",
"for ax in axes:\n",
" ax.set_axis_off(); ax.set(aspect=1)\n",
" ax.add_artist(plt.Circle((0, 0), 4, lw=2, edgecolor='b', facecolor=(0,0,0,0), zorder=1))\n",
" ax.text(-2,4,'True\\n mask', ha='center', va='bottom', color='b')\n",
" ax.add_artist(plt.Circle((2, 0), 4, lw=2, edgecolor='r', facecolor=(0,0,0,0), zorder=1))\n",
" ax.text(4,4,'Predicted\\n mask', ha='center', va='bottom', color='r')\n",
" iax=list(axes).index(ax)\n",
" if iax>0:\n",
" axes[iax].annotate(['hard ','soft '][iax-1]+'intersection', (1,-2), xytext=(0,-6), ha='center', arrowprops={'arrowstyle': '->', 'color':'y'}, zorder=2)\n",
" \n",
"axes[0].pcolormesh(x,y, y_pred, cmap='gray_r')\n",
"axes[1].pcolormesh(x,y, np.logical_and(y_true, y_pred), cmap='gray_r')\n",
"axes[2].pcolormesh(x,y, y_true * y_pred, cmap='gray_r');\n",
"fig.savefig('metrics_intersection_soft.png',bbox_inches='tight')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To test the non-naive mean_IoU, I need multiple classes, the masks of which overlap for only a small subset. I will arbitrarily take a circle and a diamond as examples of two classes, offset them a little and then find the IoU's"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"IoU of first class: 0.726\n",
"IoU of second class: 0.286\n",
"IoU of background: 0.775\n",
"IoU of each class (explicit list): [0.72645972 0.28643223 1. 1. 0.7748001 ]\n",
"mean IoU of all classes (no background, naive mean): 0.753\n",
"mean IoU of all classes (with background, naive mean): 0.758\n",
"mean IoU of all non-absent classes (dropping background): 0.506\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"x,y = fine_grid\n",
"true1 = fuzzy_circle(xy=(2,0), fuzz_factor=0.01)\n",
"pred1 = fuzzy_circle(xy=(3,0))\n",
"# two instances of Diamond class: first has IoU=0.33 (half overlap), second one has IoU=0.24\n",
"true2 = fuzzy_diamond(xy=(-4,-2),r=1,fuzz_factor=0.01) + fuzzy_diamond(xy=(-3.5,3),r=1,fuzz_factor=0.01)\n",
"pred2 = fuzzy_diamond(xy=(-5,-3),r=1) + fuzzy_diamond(xy=(-5,3),r=1)\n",
"empty = np.zeros_like(true1)\n",
"\n",
"plt.contour(x,y,true1, colors='r')\n",
"plt.contour(x,y,true2, colors='b')\n",
"\n",
"plt.pcolormesh(x,y,pred1, cmap=mpl.colors.ListedColormap([(0,0,0,0)]+list(map(plt.get_cmap('Oranges'), range(256)))[1:]))\n",
"plt.pcolormesh(x,y,pred2, cmap=mpl.colors.ListedColormap([(0,0,0,0)]+list(map(plt.get_cmap('Purples'), range(256)))[1:]))\n",
"plt.gca().set_axis_off()\n",
"plt.gca().set(aspect=1)\n",
"\n",
"y_true = np.expand_dims(np.stack([true1, true2, empty, empty, (true1==0) & (true2==0).astype(int)], axis=-1), axis=0)\n",
"y_pred = np.expand_dims(np.stack([pred1, pred2, empty, empty, (pred1==0) & (pred2==0).astype(int)], axis=-1), axis=0)\n",
"\n",
"print('{:<60s} {:.3f}'.format('IoU of first class:', metrics_np(y_true[:,:,:,:1], y_pred[:,:,:,:1], metric_name='iou')))\n",
"print('{:<60s} {:.3f}'.format('IoU of second class:', metrics_np(y_true[:,:,:,1:2], y_pred[:,:,:,1:2], metric_name='iou')))\n",
"print('{:<60s} {:.3f}'.format('IoU of background:', metrics_np(y_true[:,:,:,-1:], y_pred[:,:,:,-1:], metric_name='iou')))\n",
"print('{:<60s} {}'.format('IoU of each class (explicit list):', metrics_np(y_true, y_pred, metric_name='iou', metric_type='naive', drop_last=False, mean_per_class=True)))\n",
"print('{:<60s} {:.3f}'.format('mean IoU of all classes (no background, naive mean):', metrics_np(y_true, y_pred, metric_name='iou', metric_type='naive')))\n",
"print('{:<60s} {:.3f}'.format('mean IoU of all classes (with background, naive mean):', metrics_np(y_true, y_pred, metric_name='iou', metric_type='naive', drop_last = False)))\n",
"print('{:<60s} {:.3f}'.format('mean IoU of all non-absent classes (dropping background):', metrics_np(y_true, y_pred, metric_name='iou')))\n",
"\n",
"plt.text(5,6,'Circle\\nIoU={:1.2f}'.format(metrics_np(y_true[:,:,:,:1], y_pred[:,:,:,:1], metric_name='iou')), color='r', ha='center', va='center')\n",
"plt.text(-5,6,'Diamond\\nIoU={:1.2f}'.format(metrics_np(y_true[:,:,:,1:2], y_pred[:,:,:,1:2], metric_name='iou')), color='b', ha='center', va='center')\n",
"plt.text(0,-5,'mean IoU={:1.2f}'.format(metrics_np(y_true, y_pred, metric_name='iou')), ha='center', va='bottom');\n",
"\n",
"plt.savefig('metrics_mean_iou_multiclass.png', bbox_inches='tight')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"So far I have used `batch_size=1`. Test the difference between naive and standard ways to take the mean, for multiple examples. Here I will take two images, the first with two classes as above and the second one with only the circle."
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Naive per-class mean: [0.72645972 0.64321612 1. 1. ] -- Overall mean: 0.84\n",
"Standard per-class mean: [0.72659643 0.28714509 1. 1. ] -- Overall mean: 0.51\n",
"Standard per-class mean, with background [0.72659643 0.28714509 1. 1. 0.83668973]\n",
"Soft per-class mean [0.54782264 0.17182951 1. 1. ]\n"
]
}
],
"source": [
"y_true = np.stack([np.stack([true1, true2, empty, empty, (true1==0) & (true2==0).astype(int)], axis=-1),\n",
" np.stack([true1, empty, empty, empty, (true1==0)], axis=-1)])\n",
"y_pred = np.stack([np.stack([pred1, pred2, empty, empty, (pred1==0) & (pred2==0).astype(int)], axis=-1),\n",
" np.stack([pred1, empty, empty, empty, (pred1==0)], axis=-1)])\n",
"\n",
"print('Naive per-class mean: {} -- Overall mean: {:1.2f}'.format(\n",
" metrics_np(y_true, y_pred, metric_name='iou', metric_type='naive', mean_per_class=True), \n",
" metrics_np(y_true, y_pred, metric_name='iou', metric_type='naive')))\n",
"print('Standard per-class mean: {} -- Overall mean: {:1.2f}'.format(\n",
" metrics_np(y_true, y_pred, metric_name='iou', mean_per_class=True), \n",
" metrics_np(y_true, y_pred, metric_name='iou')))\n",
"print('Standard per-class mean, with background', metrics_np(y_true, y_pred, metric_name='iou', mean_per_class=True, drop_last=False))\n",
"# metrics_np(y_true, y_pred, metric_name='iou', mean_per_class=True),\\\n",
"# metrics_np(y_true, y_pred, metric_name='iou'),\\\n",
"print('Soft per-class mean ', metrics_np(y_true, y_pred, metric_name='iou', metric_type='soft', mean_per_class=True))\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Test Keras version and verify it gives same result as Numpy"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"hard IoU 0.506446 0.506446\n",
"soft IoU 0.359299 0.359298\n",
"hard IoU, naive mean 0.842419 0.842419\n",
"hard Dice 0.643436 0.643436\n"
]
}
],
"source": [
"print('hard IoU {:1.6f} {:1.6f}'.format(metrics_np(y_true, y_pred, metric_name='iou'), \n",
" K.eval(seg_metrics(y_true, y_pred, metric_name='iou'))))\n",
"print('soft IoU {:1.6f} {:1.6f}'.format(metrics_np(y_true, y_pred, metric_name='iou', metric_type='soft'), \n",
" K.eval(seg_metrics(y_true, y_pred, metric_name='iou', metric_type='soft'))))\n",
"print('hard IoU, naive mean {:1.6f} {:1.6f}'.format(metrics_np(y_true, y_pred, metric_name='iou', metric_type='naive'), \n",
" K.eval(seg_metrics(y_true, y_pred, metric_name='iou', metric_type='naive'))))\n",
"print('hard Dice {:1.6f} {:1.6f}'.format(metrics_np(y_true, y_pred, metric_name='dice'), \n",
" K.eval(seg_metrics(y_true, y_pred, metric_name='dice'))))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Print verbose info for metrics: look at number of pixels in intersection, union for each class and each input (`batch * classes` axes)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"intersection (pred*true), intersection (pred&true), union (pred+true-inters), union (pred|true)\n",
"[[16896 2850 0 0 46316]\n",
" [16896 0 0 0 56266]] [[16896 2850 0 0 46316]\n",
" [16896 0 0 0 56266]] [[23258 9950 0 0 59778]\n",
" [23258 0 0 0 62628]] [[23258 9950 0 0 59778]\n",
" [23258 0 0 0 62628]]\n",
"intersection, union\n",
"[[16896. 2850. 0. 0. 46316.]\n",
" [16896. 0. 0. 0. 56266.]] [[23258. 9950. 0. 0. 59778.]\n",
" [23258. 0. 0. 0. 62628.]]\n",
"[[0.72645974 0.28643215 nan nan 0.7748001 ]\n",
" [0.72645974 nan nan nan 0.89841604]]\n",
"Counts of inputs with class present, metrics for non-absent classes\n",
"[2. 1. 0. 0.] [0.72645974 0.28643224]\n"
]
},
{
"data": {
"text/plain": [
"(0.506445978533749,\n",
" array([0.72659643, 0.28714509, 1. , 1. ]),\n",
" 0.506446)"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"metrics_np(y_true, y_pred, metric_name='iou', verbose=True),\\\n",
"metrics_np(y_true, y_pred, metric_name='iou', metric_type='standard', mean_per_class=True),\\\n",
"K.eval(seg_metrics(y_true, y_pred, metric_name='iou', verbose=True))\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Coarse-grained example\n",
"\n",
"Image with few pixels to explicitly check what is going on at the pixel level"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"x,y = np.meshgrid(np.arange(-7,7.1,1), np.arange(-7,7.1,1))\n",
"\n",
"true1 = fuzzy_circle(xy=(2,0), fuzz_factor=0.01)\n",
"pred1 = fuzzy_circle(xy=(3,0), fuzz_factor=1)\n",
"# two instances of Diamond class: first has IoU=0.33 (half overlap), second one has IoU=0.24\n",
"true2 = fuzzy_diamond(xy=(-4,-2),r=1,fuzz_factor=0.01) + fuzzy_diamond(xy=(-3,3),r=1,fuzz_factor=0.01)\n",
"pred2 = fuzzy_diamond(xy=(-5,-3),r=1) + fuzzy_diamond(xy=(-5,3),r=1)\n",
"empty = np.zeros_like(true1)\n",
"\n",
"# build N*W*H*C ground truth and predicted masks\n",
"y_true = np.stack([np.stack([true1, true2, empty, empty, (true1==0) & (true2==0).astype(int)], axis=-1),\n",
" np.stack([true1, empty, empty, empty, (true1==0)], axis=-1)])\n",
"y_pred = np.stack([np.stack([pred1, pred2, empty, empty, (pred1==0) & (pred2==0).astype(int)], axis=-1),\n",
" np.stack([pred1, empty, empty, empty, (pred1==0)], axis=-1)])\n",
"\n",
"# plot predicted masks\n",
"plt.pcolormesh(x,y,pred1, cmap=mpl.colors.ListedColormap([(0,0,0,0)]+list(map(plt.get_cmap('Oranges'), range(256)))[1:]))\n",
"plt.pcolormesh(x,y,pred2, cmap=mpl.colors.ListedColormap([(0,0,0,0)]+list(map(plt.get_cmap('Purples'), range(256)))[1:]))\n",
"\n",
"# plot true masks\n",
"plt.pcolormesh(x,y,true1, cmap=mpl.colors.ListedColormap([(0,0,0,0), (1,0,0,0.2)]))\n",
"plt.pcolormesh(x,y,true2, cmap=mpl.colors.ListedColormap([(0,0,0,0), (0,0,1,0.2)]))\n",
"\n",
"for i in range(len(x)):\n",
" for j in range(len(y)):\n",
" if pred1[i][j]!=0:\n",
" fmt = '%d' if pred1[i][j] %1 ==0 else '%1.1f'\n",
" plt.text(x[i][j]+0.5, y[i][j]+0.5, fmt % pred1[i][j] , ha='center', va='center')\n",
" if pred2[i][j]!=0:\n",
" fmt = '%d' if pred2[i][j] %1 ==0 else '%1.1f'\n",
" plt.text(x[i][j]+0.5, y[i][j]+0.5, fmt % pred2[i][j] , ha='center', va='center')\n",
"\n",
"plt.text(5,6,'Circles\\n(I,U)=({:},{:})\\nIoU={:1.2f}'.format(np.logical_and(pred1, true1).sum(), np.logical_or(pred1, true1).sum(),\n",
" metrics_np(y_true[:1,:,:,:1], y_pred[:1,:,:,:1], metric_name='iou')), color='r', ha='center', va='center')\n",
"plt.text(-5.5,0.5,'Diamonds\\n(I,U)=({:},{:})\\nIoU={:1.2f}'.format(np.logical_and(pred2, true2).sum(), np.logical_or(pred2, true2).sum(),\n",
" metrics_np(y_true[:1,:,:,1:2], y_pred[:1,:,:,1:2], metric_name='iou')), color='b', ha='center', va='center')\n",
"plt.text(0,-5,'mean IoU={:1.2f}'.format(metrics_np(y_true[:1], y_pred[:1], metric_name='iou')), ha='center', va='bottom');\n",
"\n",
"plt.gca().set_axis_off()\n",
"# plt.gca().set(aspect=1)\n",
"plt.savefig('metrics_mean_iou_coarse_example.png', bbox_inches='tight')"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"intersection (pred*true), intersection (pred&true), union (pred+true-inters), union (pred|true)\n",
"[[ 38 3 0 0 156]\n",
" [ 38 0 0 0 173]] [[ 38 3 0 0 156]\n",
" [ 38 0 0 0 173]] [[ 52 17 0 0 184]\n",
" [ 52 0 0 0 187]] [[ 52 17 0 0 184]\n",
" [ 52 0 0 0 187]]\n",
"intersection, union\n",
"[[ 38. 3. 0. 0. 156.]\n",
" [ 38. 0. 0. 0. 173.]] [[ 52. 17. 0. 0. 184.]\n",
" [ 52. 0. 0. 0. 187.]]\n",
"[[0.7307692 0.1764706 nan nan 0.84782606]\n",
" [0.7307692 nan nan nan 0.9251337 ]]\n",
"Counts of inputs with class present, metrics for non-absent classes\n",
"[2. 1. 0. 0.] [0.7307744 0.17651904]\n"
]
},
{
"data": {
"text/plain": [
"(0.45364671823845565,\n",
" array([0.73090895, 0.17734169, 1. , 1. ]),\n",
" 0.45364672)"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"metrics_np(y_true, y_pred, metric_name='iou',verbose=True),\\\n",
"metrics_np(y_true, y_pred, metric_name='iou', mean_per_class=True),\\\n",
"K.eval(seg_metrics(y_true, y_pred, metric_name='iou', verbose=True))\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.7"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
@rose-jinyang
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Hello
Thanks for contributing to this script.
I have a question.
Did u test these metrics in binary segmentation?
On my side(binary segmentation), all the metrics are "nan".

@farhanone
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Hello
Thanks for contributing to this script.
I have a question.
Did u test these metrics in binary segmentation?
On my side(binary segmentation), all the metrics are "nan".

For me too

@ilmonteux
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Author

ilmonteux commented Apr 27, 2021

@rose-jinyang @farhanone given you're mentioning binary segmentation, is it possible that the inputs in your cases are not 4d (B*W*H*N) but 3d instead (B*W*H)? In that case reshaping the data (labels) should work (something like y.reshape(y.shape+(1,))). Though would be nice to validate the input and throw an error if the passed inputs are the wrong shape.

@farhanone
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@ilmonteux the input label shape is 4D(B*W*H*N). Dice always give nan values however IOU gives 1.

@fabiankueppers
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Hi and thank you very much for the article and your code. I would like to use this snippet within my current work. Do you have any (Bibtex) reference that I can refer to? Thank you!

@explorer359
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Hi, I have color maps available , can I use your code directly? without one hot encoding? just reading through cv2?

@ilmonteux
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hi, you'll have to one-hot encode

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