Skip to content

Instantly share code, notes, and snippets.

@luke14free
Created April 10, 2017 09:09
Show Gist options
  • Save luke14free/e4cd679099835a7599ba122863747580 to your computer and use it in GitHub Desktop.
Save luke14free/e4cd679099835a7599ba122863747580 to your computer and use it in GitHub Desktop.
Deep matting with FCN/VGG16
Display the source blob
Display the rendered blob
Raw
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Fully convolutional network for image matting (from VGG16) \n",
"\n",
"In this notebook I re-implemented part of the FCN algorithm applied to image matting from http://xiaoyongshen.me/webpage_portrait/papers/portrait_eg16.pdf\n",
"\n",
"Note that I only had time to implement FCN and not FCN plus, which is more complex and would have required more time. Also no preprocessing is applied to input images (again - more time would have been needed), so this is just a proof of concept, but the performances are not good enough to be applied in real life."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Imports"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Using TensorFlow backend.\n"
]
}
],
"source": [
"import numpy as np\n",
"from keras import applications\n",
"from keras.models import Model\n",
"from PIL import Image\n",
"from PIL import Image\n",
"import numpy as np\n",
"import urllib.request\n",
"import io\n",
"from matplotlib.pyplot import imshow\n",
"import matplotlib.pyplot as plt\n",
"from keras.layers import (\n",
" Input,\n",
" Dropout,\n",
" Lambda,\n",
" Reshape\n",
")\n",
"from keras.layers.convolutional import (\n",
" Conv2D,\n",
" Conv2DTranspose,\n",
" MaxPooling2D,\n",
" ZeroPadding2D,\n",
" Cropping2D\n",
")\n",
"from keras.layers.merge import add\n",
"from keras import backend as K\n",
"\n",
"%matplotlib inline"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Image preprocessing\n",
"At the moment this simply takes some image from the web, converts it to our desired output size (800x600) and shows it. In theory a lot of work is still needed here (average masking etc)."
]
},
{
"cell_type": "code",
"execution_count": 66,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"def preprocess(url):\n",
" with urllib.request.urlopen(url) as _url:\n",
" f = io.BytesIO(_url.read())\n",
" im = np.asarray(Image.open(f).resize((IMAGE_WIDTH, IMAGE_HEIGHT))).astype(np.float32)\n",
" #RGB -> BGR \n",
" #im.transpose(2, 1, 0)\n",
" #im[:,:,0] -= 103.939\n",
" #im[:,:,1] -= 116.779\n",
" #im[:,:,2] -= 123.68\n",
" imshow(im)\n",
" return np.array([im])\n",
"#test = preprocess(\"https://www.oleg-ti.com/gallery/beauty27.jpg\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Load a pre-trained VGG16 network from Keras.\n",
"\n",
"VGG16 is an award winning (from 2014) classifier network, we load it and exclude the head of the network (the dense part) by only keeping the convolutional part"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"# build the VGG16 network\n",
"IMAGE_HEIGHT, IMAGE_WIDTH = 800, 600\n",
"inp = Input((IMAGE_HEIGHT, IMAGE_WIDTH, 3))\n",
"model = applications.VGG16(include_top=False,\n",
" weights='imagenet',\n",
" input_tensor=inp,\n",
" input_shape=(IMAGE_HEIGHT, IMAGE_WIDTH, 3))\n",
"layer_dict = dict([(layer.name, layer) for layer in model.layers])\n",
"pool5 = layer_dict[\"block5_pool\"].output"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Adding more convolutions (fc6/fc7)\n",
"\n",
"These are needed for further tuning and network surgerying. More info: https://people.eecs.berkeley.edu/~jonlong/long_shelhamer_fcn.pdf"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Tensor(\"dropout_2/cond/Merge:0\", shape=(?, 19, 12, 4096), dtype=float32)\n"
]
}
],
"source": [
"# Add fc6 and fc7 to the model\n",
"fc6 = Conv2D(filters=4096, kernel_size=(7, 7),\n",
" activation='relu', padding='valid',\n",
" name='fc6', input_shape=(None, 1, 1, pool5.get_shape()[-1]))(pool5)\n",
"drop6 = Dropout(0.5)(fc6)\n",
"fc7 = Conv2D(filters=4096, kernel_size=(1, 1),\n",
" activation='relu', padding='valid',\n",
" name='fc7')(drop6)\n",
"\n",
"drop7 = Dropout(0.5)(fc7)\n",
"pool4 = layer_dict[\"block4_pool\"].output\n",
"pool3 = layer_dict[\"block3_pool\"].output\n",
"\n",
"print(drop7)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Additional help layers\n",
"We create additional cropping layers to fix issues with inverse convolution sizes."
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"def _crop(target_layer, offset=(None, None), name=None):\n",
" \"\"\"Crop the bottom such that it has the same shape as target_layer.\"\"\"\n",
"\n",
" def f(input):\n",
" width = input._keras_shape[ROW_AXIS]\n",
" height = input._keras_shape[COL_AXIS]\n",
" target_width = target_layer._keras_shape[ROW_AXIS]\n",
" target_height = target_layer._keras_shape[COL_AXIS]\n",
" cropped = Cropping2D(cropping=((offset[0],\n",
" width - offset[0] - target_width),\n",
" (offset[1],\n",
" height - offset[1] - target_height)),\n",
" name='{}'.format(name))(input)\n",
" return cropped\n",
" return f"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"ROW_AXIS = 1\n",
"COL_AXIS = 2\n",
"CHANNEL_AXIS = 3"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Let's actually build a FCN on top of the VGG\n",
"This is the magical part, we extend the existing network following the FCN architecture"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"import tensorflow as tf\n",
"num_output=2\n",
"from keras.losses import sparse_categorical_crossentropy\n",
"from keras.optimizers import Adam\n",
"\n",
"score_fr = Conv2D(filters=num_output, kernel_size=(1, 1),\n",
" padding='valid',\n",
" name='score_fr')(drop7)\n",
"\n",
"upscore2 = Conv2DTranspose(filters=num_output, kernel_size=(4, 4),\n",
" strides=(2, 2), padding='valid', use_bias=False,\n",
" data_format=K.image_data_format(),\n",
" name='upscore2')(score_fr)\n",
"\n",
"scale_pool4 = Lambda(lambda x: x * 0.01, name='scale_pool4')(pool4)\n",
"score_pool4 = Conv2D(filters=num_output, kernel_size=(1, 1),\n",
" padding='valid', name='score_pool4')(scale_pool4)\n",
"\n",
"score_pool4c = _crop(upscore2, offset=(5, 5),\n",
" name='score_pool4c')(score_pool4)\n",
"fuse_pool4 = add([upscore2, score_pool4c])\n",
"upscore_pool4 = Conv2DTranspose(filters=num_output, kernel_size=(4, 4),\n",
" strides=(2, 2), padding='valid',\n",
" use_bias=False,\n",
" data_format=K.image_data_format(),\n",
" name='upscore_pool4')(fuse_pool4)\n",
"\n",
"scale_pool3 = Lambda(lambda x: x * 0.0001, name='scale_pool3')(pool3)\n",
"score_pool3 = Conv2D(filters=num_output, kernel_size=(1, 1),\n",
" padding='valid', name='score_pool3')(scale_pool3)\n",
"score_pool3c = _crop(upscore_pool4, offset=(9, 9),\n",
" name='score_pool3c')(score_pool3)\n",
"fuse_pool3 = add([upscore_pool4, score_pool3c])\n",
"\n",
"upscore8 = Conv2DTranspose(filters=num_output, kernel_size=(152, 176), #(168, 164)),\n",
" strides=8, padding='valid',\n",
" use_bias=False,\n",
" data_format=K.image_data_format(),\n",
" name='upscore8')(fuse_pool3)\n",
"\n",
"def custom_loss(annotation, logits):\n",
" logits = tf.reshape(logits, [-1, IMAGE_HEIGHT, IMAGE_WIDTH, 2])\n",
" logits = tf.cast(logits, tf.float32)\n",
" annotation = tf.reshape(annotation, [-1, IMAGE_HEIGHT, IMAGE_WIDTH])\n",
" annotation = tf.cast(annotation, tf.int32)\n",
" return tf.nn.sparse_softmax_cross_entropy_with_logits(logits=logits, labels=annotation)\n",
"\n",
"model = Model(inp, upscore8, name='fcn_vgg16')\n",
"model.compile(optimizer=Adam(lr=1e-4), loss=custom_loss)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### And now it's training time :-)"
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"from keras.callbacks import EarlyStopping\n",
"from itertools import repeat\n",
"import matplotlib.pyplot as plt\n",
"from tqdm import tnrange, tqdm_notebook\n",
"import pickle\n",
"\n",
"test_images, test_annotations = pickle.load(open(\"data/test_batch_100.pkl\", \"rb\"))\n",
"for epoch in tnrange(100, desc=\"epoch\"):\n",
" for idx, batch in tqdm_notebook(list(enumerate(range(1, 63)))):\n",
" images, annotations = pickle.load(open(\"data/batch_%d00.pkl\" % batch, \"rb\"))\n",
" images = images.reshape(200, IMAGE_HEIGHT, IMAGE_WIDTH, 3)\n",
" annotations = annotations.reshape(200, IMAGE_HEIGHT, IMAGE_WIDTH, 1)\n",
" if idx % 61 == 0 and idx:\n",
" _=model.fit(\n",
" images,\n",
" annotations,\n",
" batch_size=3,\n",
" epochs=1,\n",
" verbose=2,\n",
" validation_data=(test_images, test_annotations)\n",
" )\n",
" else:\n",
" _=model.fit(\n",
" images,\n",
" annotations,\n",
" batch_size=3,\n",
" epochs=1,\n",
" verbose=0,\n",
" )"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Let's see how we did!\n",
"\n",
"We will now try our magical algorithm on some random picture coming from the web. Did we really succed at telling our algo how to recognize people?\n",
"\n",
"Color map:\n",
"\n",
"* Yellow = Background\n",
"* Green = Border area, indecision\n",
"* Purple = Foreground"
]
},
{
"cell_type": "code",
"execution_count": 67,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAANQAAAD8CAYAAAAPIYpDAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1140ef278>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"test = preprocess(\"https://www.oleg-ti.com/gallery/beauty27.jpg\")"
]
},
{
"cell_type": "code",
"execution_count": 74,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.image.AxesImage at 0x114f92d30>"
]
},
"execution_count": 74,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAANQAAAD8CAYAAAAPIYpDAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nCpRIiRQokRIpUCIlUqBESqRAiZTo/wGBXEK/B0ZqrgAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11763cdd8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from sklearn.preprocessing import normalize\n",
"ma = normalize(out[:,:,0])\n",
"mo = normalize(out[:,:,1])\n",
"\n",
"out = model.predict(test)[0]\n",
"imshow(out)\n",
"\n",
"#np.min(ma-mo)\n",
"mm = ma - mo\n",
"lower_bound = -.01\n",
"upper_bound = .01\n",
"mm[ma - mo < lower_bound] = -1\n",
"mm[ma - mo > upper_bound] = 1\n",
"mm[(ma - mo > lower_bound) & (ma - mo < upper_bound)] = 0\n",
"imshow(mm)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Misc utils\n",
"(mostly stuff you shouldn't care about)"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"import pickle\n",
"def dump_model(model, path):\n",
" out = {}\n",
" for layer in model.layers:\n",
" out[layer.name] = layer.get_weights()\n",
" pickle.dump(out, open(path, \"wb\"))\n",
" \n",
"def load_model(model, path):\n",
" inp = pickle.load(open(path, \"rb\"))\n",
" for idx in range(len(model.layers)):\n",
" if inp[model.layers[idx].name]:\n",
" model.layers[idx].set_weights(inp[model.layers[idx].name])\n",
" return model\n",
"\n",
"model = load_model(model, \"/Users/luca/Downloads/model.pkl\")"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"from portrait import BatchDatset, TestDataset\n",
"import pickle\n",
"from tqdm import tqdm_notebook\n",
"\n",
"def reshape_array_img(images, height, width):\n",
" out = []\n",
" for img in images:\n",
" dtype = np.float32\n",
" if img.shape[2] == 1:\n",
" img = img.reshape(img.shape[0], img.shape[1])\n",
" dtype = np.uint8\n",
" resize = Image.fromarray((img*255).astype(np.uint8)).resize((width, height))\n",
" out.append((np.asarray(resize) / 255).astype(dtype))\n",
" return out\n",
"\n",
"class Train(BatchDatset):\n",
" def __init__(self, path, width=IMAGE_WIDTH, height=IMAGE_HEIGHT):\n",
" super().__init__(path)\n",
" self.width=width\n",
" self.height=height\n",
" \n",
" def __iter__(self):\n",
" return self\n",
" \n",
" def __next__(self):\n",
" images, annotations = self.next_batch()\n",
" if (IMAGE_WIDTH, IMAGE_HEIGHT) == (600, 800): # Original size\n",
" return images.reshape(len(images), self.height, self.width, 3), \\\n",
" annotations.reshape(len(annotations), self.height, self.width, 1)\n",
" else:\n",
" return reshape_array_img(images, self.height, self.width).reshape(len(images), self.height, self.width, 3), \\\n",
" reshape_array_img(annotations, self.height, self.width).reshape(len(annotations), self.height, self.width, 1)\n",
" \n",
"class Test(TestDataset):\n",
" def __init__(self, path, width=IMAGE_WIDTH, height=IMAGE_HEIGHT):\n",
" super().__init__(path)\n",
" self.width=width\n",
" self.height=height\n",
" \n",
" def __iter__(self):\n",
" return self\n",
" \n",
" def __next__(self):\n",
" images, annotations = self.next_batch()\n",
" if (IMAGE_WIDTH, IMAGE_HEIGHT) == (600, 800): # Original size\n",
" return images.reshape(len(images), self.height, self.width, 3), \\\n",
" annotations.reshape(len(annotations), self.height, self.width, 1)\n",
" else:\n",
" return reshape_array_img(images, self.height, self.width).reshape(len(images), self.height, self.width, 3), \\\n",
" reshape_array_img(annotations, self.height, self.width).reshape(len(annotations), self.height, self.width, 1)\n",
" \n",
"test_dataset = Test('data/testlist.mat')\n",
"\n",
"it = 0\n",
"images, annotations = [], []\n",
"for _images, _annotations in tqdm_notebook(test_dataset, desc=\"iterations\"):\n",
" images.extend(_images)\n",
" annotations.extend(_annotations)\n",
" it += 1\n",
" if it % 100 == 0:\n",
" # Save to disk and clear arrays\n",
" pickle.dump(\n",
" [np.array(images, dtype=np.float32), np.array(annotations, dtype=np.uint8)], \n",
" open(\"data/test_batch_%s.pkl\" % it, \"wb\")\n",
" )\n",
" images, annotations = [], []"
]
}
],
"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": "3.6.0"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment