iandewancker

# NOTE : run within DATASET folder
# project 2D mask, 3D bounding box, and vertices from the mesh into the dataset

import glob
import json
import os

import cv2
import matplotlib.pyplot as plt
import numpy as np

iandewancker

# NOTE : run within DATASET folder
# project 2D mask, 3D bounding box, and vertices from the mesh into the dataset

import glob
import json
import os

import cv2
import matplotlib.pyplot as plt
import numpy as np

iandewancker

import glob
import skimage
import numpy as np
import matplotlib.pyplot as plt
from PIL import Image
import glob

import matplotlib.pyplot as plt
import numpy as np

iandewancker

def generate_interactive_plot(y_pred, y_true, gids, img_pattern):
    colors=["red", "gold", "limegreen"]
    projected_points = projectSimplex(y_pred)
    labels = ['0', '1', '2+']

    from bokeh.plotting import figure, output_file, show, ColumnDataSource
    from bokeh.models import HoverTool

    URLs = []
    web_colors = []

iandewancker

import glob
images = glob.glob("*.jpg")
import re
import os
import matplotlib.pyplot as plt

import numpy as np
import cv2
from PIL import Image

iandewancker

import glob
import skimage
import numpy as np
import matplotlib.pyplot as plt
from PIL import Image
import glob

import matplotlib.pyplot as plt
import numpy as np

iandewancker

import glob
images = glob.glob("*.jpg")
import re
import os
import matplotlib.pyplot as plt

def natural_sort(l): 
    convert = lambda text: int(text) if text.isdigit() else text.lower() 
    alphanum_key = lambda key: [ convert(c) for c in re.split('([0-9]+)', key) ] 
    return sorted(l, key = alphanum_key)

iandewancker

import scipy.stats
import numpy as np
import scipy.optimize

obs_data = [
0.08982035928143713
,0.06818181818181818
,0.012987012987012988
,0.05357142857142857
,0.045454545454545456

iandewancker

def custom_cat_crossentropy(y_true, y_pred):
    # negate 3x3 sub matrix of income matrix to get cost matrix
    # check https://github.com/tensorflow/tensorflow/blob/r1.10/tensorflow/python/keras/backend.py#L3461
    cost_m = tf.constant([[ 0.00222222,  0.01111111,  0.00222222],
       [ 0.00222222, -0.05888889,  0.00222222],
       [ 0.00222222,  1.51111111,  0.00222222]])
    y_true = tf.matmul(y_true, cost_m)
    return tf.keras.losses.categorical_crossentropy(y_true, y_pred)

iandewancker

# load keras model from disk
model_name = "GVC_IncepvtionV3_epoch_6_vanilla_vgg_chute_date_2018_07_27"                                  
json_file = open(model_name+'.json', 'r')                          
model_json = json_file.read()                                      
json_file.close()                                                  
model = tensorflow.keras.models.model_from_json(model_json)        
# load weights into new model                                      
model.load_weights(model_name+".h5")                               
print("Loaded model from disk")