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import os
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import cv2
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import numpy as np
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import seaborn as sns
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from scipy.ndimage.interpolation import map_coordinates
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from scipy.ndimage.filters import gaussian_filter
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import random
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from tqdm import tqdm
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import imutils
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import math
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from tensorflow.keras.utils import to_categorical
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from PIL import Image, ImageEnhance
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def return_number_of_total_training_data(path_classes):
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sub_classes = os.listdir(path_classes)
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n_tot = 0
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for sub_c in sub_classes:
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sub_files = os.listdir(os.path.join(path_classes,sub_c))
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n_tot = n_tot + len(sub_files)
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return n_tot
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def generate_data_from_folder_evaluation(path_classes, height, width, n_classes, list_classes):
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#sub_classes = os.listdir(path_classes)
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#n_classes = len(sub_classes)
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all_imgs = []
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labels = []
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#dicts =dict()
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#indexer= 0
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for indexer, sub_c in enumerate(list_classes):
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sub_files = os.listdir(os.path.join(path_classes,sub_c ))
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sub_files = [os.path.join(path_classes,sub_c )+'/' + x for x in sub_files]
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#print( os.listdir(os.path.join(path_classes,sub_c )) )
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all_imgs = all_imgs + sub_files
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sub_labels = list( np.zeros( len(sub_files) ) +indexer )
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#print( len(sub_labels) )
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labels = labels + sub_labels
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#dicts[sub_c] = indexer
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#indexer +=1
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categories = to_categorical(range(n_classes)).astype(np.int16)#[ [1 , 0, 0 , 0 , 0 , 0] , [0 , 1, 0 , 0 , 0 , 0] , [0 , 0, 1 , 0 , 0 , 0] , [0 , 0, 0 , 1 , 0 , 0] , [0 , 0, 0 , 0 , 1 , 0] , [0 , 0, 0 , 0 , 0 , 1] ]
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ret_x= np.zeros((len(labels), height,width, 3)).astype(np.int16)
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ret_y= np.zeros((len(labels), n_classes)).astype(np.int16)
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#print(all_imgs)
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for i in range(len(all_imgs)):
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row = all_imgs[i]
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#####img = cv2.imread(row, 0)
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#####img= resize_image (img, height, width)
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#####img = img.astype(np.uint16)
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#####ret_x[i, :,:,0] = img[:,:]
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#####ret_x[i, :,:,1] = img[:,:]
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#####ret_x[i, :,:,2] = img[:,:]
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img = cv2.imread(row)
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img= resize_image (img, height, width)
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img = img.astype(np.uint16)
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ret_x[i, :,:] = img[:,:,:]
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ret_y[i, :] = categories[ int( labels[i] ) ][:]
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return ret_x/255., ret_y
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def generate_data_from_folder_training(path_classes, batchsize, height, width, n_classes, list_classes):
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#sub_classes = os.listdir(path_classes)
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#n_classes = len(sub_classes)
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all_imgs = []
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labels = []
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#dicts =dict()
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#indexer= 0
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for indexer, sub_c in enumerate(list_classes):
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sub_files = os.listdir(os.path.join(path_classes,sub_c ))
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sub_files = [os.path.join(path_classes,sub_c )+'/' + x for x in sub_files]
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#print( os.listdir(os.path.join(path_classes,sub_c )) )
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all_imgs = all_imgs + sub_files
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sub_labels = list( np.zeros( len(sub_files) ) +indexer )
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#print( len(sub_labels) )
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labels = labels + sub_labels
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#dicts[sub_c] = indexer
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#indexer +=1
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ids = np.array(range(len(labels)))
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random.shuffle(ids)
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shuffled_labels = np.array(labels)[ids]
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shuffled_files = np.array(all_imgs)[ids]
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categories = to_categorical(range(n_classes)).astype(np.int16)#[ [1 , 0, 0 , 0 , 0 , 0] , [0 , 1, 0 , 0 , 0 , 0] , [0 , 0, 1 , 0 , 0 , 0] , [0 , 0, 0 , 1 , 0 , 0] , [0 , 0, 0 , 0 , 1 , 0] , [0 , 0, 0 , 0 , 0 , 1] ]
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ret_x= np.zeros((batchsize, height,width, 3)).astype(np.int16)
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ret_y= np.zeros((batchsize, n_classes)).astype(np.int16)
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batchcount = 0
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while True:
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for i in range(len(shuffled_files)):
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row = shuffled_files[i]
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#print(row)
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###img = cv2.imread(row, 0)
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###img= resize_image (img, height, width)
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###img = img.astype(np.uint16)
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###ret_x[batchcount, :,:,0] = img[:,:]
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###ret_x[batchcount, :,:,1] = img[:,:]
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###ret_x[batchcount, :,:,2] = img[:,:]
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img = cv2.imread(row)
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img= resize_image (img, height, width)
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img = img.astype(np.uint16)
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ret_x[batchcount, :,:,:] = img[:,:,:]
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#print(int(shuffled_labels[i]) )
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#print( categories[int(shuffled_labels[i])] )
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ret_y[batchcount, :] = categories[ int( shuffled_labels[i] ) ][:]
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batchcount+=1
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if batchcount>=batchsize:
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ret_x = ret_x/255.
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yield (ret_x, ret_y)
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ret_x= np.zeros((batchsize, height,width, 3)).astype(np.int16)
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ret_y= np.zeros((batchsize, n_classes)).astype(np.int16)
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batchcount = 0
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def do_brightening(img_in_dir, factor):
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im = Image.open(img_in_dir)
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enhancer = ImageEnhance.Brightness(im)
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out_img = enhancer.enhance(factor)
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out_img = out_img.convert('RGB')
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opencv_img = np.array(out_img)
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opencv_img = opencv_img[:,:,::-1].copy()
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return opencv_img
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def bluring(img_in, kind):
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if kind == 'gauss':
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img_blur = cv2.GaussianBlur(img_in, (5, 5), 0)
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elif kind == "median":
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img_blur = cv2.medianBlur(img_in, 5)
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elif kind == 'blur':
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img_blur = cv2.blur(img_in, (5, 5))
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return img_blur
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def elastic_transform(image, alpha, sigma, seedj, random_state=None):
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"""Elastic deformation of images as described in [Simard2003]_.
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.. [Simard2003] Simard, Steinkraus and Platt, "Best Practices for
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Convolutional Neural Networks applied to Visual Document Analysis", in
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Proc. of the International Conference on Document Analysis and
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Recognition, 2003.
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"""
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if random_state is None:
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random_state = np.random.RandomState(seedj)
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shape = image.shape
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dx = gaussian_filter((random_state.rand(*shape) * 2 - 1), sigma, mode="constant", cval=0) * alpha
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dy = gaussian_filter((random_state.rand(*shape) * 2 - 1), sigma, mode="constant", cval=0) * alpha
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dz = np.zeros_like(dx)
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x, y, z = np.meshgrid(np.arange(shape[1]), np.arange(shape[0]), np.arange(shape[2]))
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indices = np.reshape(y + dy, (-1, 1)), np.reshape(x + dx, (-1, 1)), np.reshape(z, (-1, 1))
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distored_image = map_coordinates(image, indices, order=1, mode='reflect')
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return distored_image.reshape(image.shape)
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def rotation_90(img):
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img_rot = np.zeros((img.shape[1], img.shape[0], img.shape[2]))
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img_rot[:, :, 0] = img[:, :, 0].T
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img_rot[:, :, 1] = img[:, :, 1].T
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img_rot[:, :, 2] = img[:, :, 2].T
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return img_rot
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def rotatedRectWithMaxArea(w, h, angle):
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"""
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Given a rectangle of size wxh that has been rotated by 'angle' (in
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radians), computes the width and height of the largest possible
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axis-aligned rectangle (maximal area) within the rotated rectangle.
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"""
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if w <= 0 or h <= 0:
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return 0, 0
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width_is_longer = w >= h
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side_long, side_short = (w, h) if width_is_longer else (h, w)
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# since the solutions for angle, -angle and 180-angle are all the same,
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# if suffices to look at the first quadrant and the absolute values of sin,cos:
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sin_a, cos_a = abs(math.sin(angle)), abs(math.cos(angle))
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if side_short <= 2. * sin_a * cos_a * side_long or abs(sin_a - cos_a) < 1e-10:
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# half constrained case: two crop corners touch the longer side,
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# the other two corners are on the mid-line parallel to the longer line
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x = 0.5 * side_short
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wr, hr = (x / sin_a, x / cos_a) if width_is_longer else (x / cos_a, x / sin_a)
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else:
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# fully constrained case: crop touches all 4 sides
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cos_2a = cos_a * cos_a - sin_a * sin_a
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wr, hr = (w * cos_a - h * sin_a) / cos_2a, (h * cos_a - w * sin_a) / cos_2a
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return wr, hr
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def rotate_max_area(image, rotated, rotated_label, angle):
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""" image: cv2 image matrix object
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angle: in degree
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"""
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wr, hr = rotatedRectWithMaxArea(image.shape[1], image.shape[0],
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math.radians(angle))
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h, w, _ = rotated.shape
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y1 = h // 2 - int(hr / 2)
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y2 = y1 + int(hr)
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x1 = w // 2 - int(wr / 2)
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x2 = x1 + int(wr)
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return rotated[y1:y2, x1:x2], rotated_label[y1:y2, x1:x2]
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def rotation_not_90_func(img, label, thetha):
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rotated = imutils.rotate(img, thetha)
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rotated_label = imutils.rotate(label, thetha)
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return rotate_max_area(img, rotated, rotated_label, thetha)
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def color_images(seg, n_classes):
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ann_u = range(n_classes)
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if len(np.shape(seg)) == 3:
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seg = seg[:, :, 0]
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seg_img = np.zeros((np.shape(seg)[0], np.shape(seg)[1], 3)).astype(float)
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colors = sns.color_palette("hls", n_classes)
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for c in ann_u:
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c = int(c)
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segl = (seg == c)
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seg_img[:, :, 0] += segl * (colors[c][0])
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seg_img[:, :, 1] += segl * (colors[c][1])
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seg_img[:, :, 2] += segl * (colors[c][2])
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return seg_img
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def resize_image(seg_in, input_height, input_width):
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return cv2.resize(seg_in, (input_width, input_height), interpolation=cv2.INTER_NEAREST)
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def get_one_hot(seg, input_height, input_width, n_classes):
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seg = seg[:, :, 0]
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seg_f = np.zeros((input_height, input_width, n_classes))
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for j in range(n_classes):
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seg_f[:, :, j] = (seg == j).astype(int)
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return seg_f
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def IoU(Yi, y_predi):
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## mean Intersection over Union
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## Mean IoU = TP/(FN + TP + FP)
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IoUs = []
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classes_true = np.unique(Yi)
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for c in classes_true:
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TP = np.sum((Yi == c) & (y_predi == c))
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FP = np.sum((Yi != c) & (y_predi == c))
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FN = np.sum((Yi == c) & (y_predi != c))
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IoU = TP / float(TP + FP + FN)
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#print("class {:02.0f}: #TP={:6.0f}, #FP={:6.0f}, #FN={:5.0f}, IoU={:4.3f}".format(c, TP, FP, FN, IoU))
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IoUs.append(IoU)
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mIoU = np.mean(IoUs)
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#print("_________________")
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#print("Mean IoU: {:4.3f}".format(mIoU))
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return mIoU
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def data_gen(img_folder, mask_folder, batch_size, input_height, input_width, n_classes, task='segmentation'):
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c = 0
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n = [f for f in os.listdir(img_folder) if not f.startswith('.')] # os.listdir(img_folder) #List of training images
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random.shuffle(n)
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while True:
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img = np.zeros((batch_size, input_height, input_width, 3)).astype('float')
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mask = np.zeros((batch_size, input_height, input_width, n_classes)).astype('float')
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for i in range(c, c + batch_size): # initially from 0 to 16, c = 0.
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try:
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filename = n[i].split('.')[0]
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train_img = cv2.imread(img_folder + '/' + n[i]) / 255.
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train_img = cv2.resize(train_img, (input_width, input_height),
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|
interpolation=cv2.INTER_NEAREST) # Read an image from folder and resize
|
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|
|
|
|
|
|
|
|
img[i - c] = train_img # add to array - img[0], img[1], and so on.
|
|
|
|
|
if task == "segmentation":
|
|
|
|
|
train_mask = cv2.imread(mask_folder + '/' + filename + '.png')
|
|
|
|
|
train_mask = get_one_hot(resize_image(train_mask, input_height, input_width), input_height, input_width,
|
|
|
|
|
n_classes)
|
|
|
|
|
elif task == "enhancement":
|
|
|
|
|
train_mask = cv2.imread(mask_folder + '/' + filename + '.png')/255.
|
|
|
|
|
train_mask = resize_image(train_mask, input_height, input_width)
|
|
|
|
|
|
|
|
|
|
# train_mask = train_mask.reshape(224, 224, 1) # Add extra dimension for parity with train_img size [512 * 512 * 3]
|
|
|
|
|
|
|
|
|
|
mask[i - c] = train_mask
|
|
|
|
|
except:
|
|
|
|
|
img[i - c] = np.ones((input_height, input_width, 3)).astype('float')
|
|
|
|
|
mask[i - c] = np.zeros((input_height, input_width, n_classes)).astype('float')
|
|
|
|
|
|
|
|
|
|
c += batch_size
|
|
|
|
|
if c + batch_size >= len(os.listdir(img_folder)):
|
|
|
|
|
c = 0
|
|
|
|
|
random.shuffle(n)
|
|
|
|
|
yield img, mask
|
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|
|
|
|
def otsu_copy(img):
|
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|
|
img_r = np.zeros(img.shape)
|
|
|
|
|
img1 = img[:, :, 0]
|
|
|
|
|
img2 = img[:, :, 1]
|
|
|
|
|
img3 = img[:, :, 2]
|
|
|
|
|
_, threshold1 = cv2.threshold(img1, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
|
|
|
|
|
_, threshold2 = cv2.threshold(img2, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
|
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|
|
|
_, threshold3 = cv2.threshold(img3, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
|
|
|
|
|
img_r[:, :, 0] = threshold1
|
|
|
|
|
img_r[:, :, 1] = threshold1
|
|
|
|
|
img_r[:, :, 2] = threshold1
|
|
|
|
|
return img_r
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
def get_patches(dir_img_f, dir_seg_f, img, label, height, width, indexer):
|
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|
|
|
if img.shape[0] < height or img.shape[1] < width:
|
|
|
|
|
img, label = do_padding(img, label, height, width)
|
|
|
|
|
|
|
|
|
|
img_h = img.shape[0]
|
|
|
|
|
img_w = img.shape[1]
|
|
|
|
|
|
|
|
|
|
nxf = img_w / float(width)
|
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|
|
|
nyf = img_h / float(height)
|
|
|
|
|
|
|
|
|
|
if nxf > int(nxf):
|
|
|
|
|
nxf = int(nxf) + 1
|
|
|
|
|
if nyf > int(nyf):
|
|
|
|
|
nyf = int(nyf) + 1
|
|
|
|
|
|
|
|
|
|
nxf = int(nxf)
|
|
|
|
|
nyf = int(nyf)
|
|
|
|
|
|
|
|
|
|
for i in range(nxf):
|
|
|
|
|
for j in range(nyf):
|
|
|
|
|
index_x_d = i * width
|
|
|
|
|
index_x_u = (i + 1) * width
|
|
|
|
|
|
|
|
|
|
index_y_d = j * height
|
|
|
|
|
index_y_u = (j + 1) * height
|
|
|
|
|
|
|
|
|
|
if index_x_u > img_w:
|
|
|
|
|
index_x_u = img_w
|
|
|
|
|
index_x_d = img_w - width
|
|
|
|
|
if index_y_u > img_h:
|
|
|
|
|
index_y_u = img_h
|
|
|
|
|
index_y_d = img_h - height
|
|
|
|
|
|
|
|
|
|
img_patch = img[index_y_d:index_y_u, index_x_d:index_x_u, :]
|
|
|
|
|
label_patch = label[index_y_d:index_y_u, index_x_d:index_x_u, :]
|
|
|
|
|
|
|
|
|
|
cv2.imwrite(dir_img_f + '/img_' + str(indexer) + '.png', img_patch)
|
|
|
|
|
cv2.imwrite(dir_seg_f + '/img_' + str(indexer) + '.png', label_patch)
|
|
|
|
|
indexer += 1
|
|
|
|
|
|
|
|
|
|
return indexer
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
def do_padding_white(img):
|
|
|
|
|
img_org_h = img.shape[0]
|
|
|
|
|
img_org_w = img.shape[1]
|
|
|
|
|
|
|
|
|
|
index_start_h = 4
|
|
|
|
|
index_start_w = 4
|
|
|
|
|
|
|
|
|
|
img_padded = np.zeros((img.shape[0] + 2*index_start_h, img.shape[1]+ 2*index_start_w, img.shape[2])) + 255
|
|
|
|
|
img_padded[index_start_h: index_start_h + img.shape[0], index_start_w: index_start_w + img.shape[1], :] = img[:, :, :]
|
|
|
|
|
|
|
|
|
|
return img_padded.astype(float)
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
def do_degrading(img, scale):
|
|
|
|
|
img_org_h = img.shape[0]
|
|
|
|
|
img_org_w = img.shape[1]
|
|
|
|
|
|
|
|
|
|
img_res = resize_image(img, int(img_org_h * scale), int(img_org_w * scale))
|
|
|
|
|
|
|
|
|
|
return resize_image(img_res, img_org_h, img_org_w)
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
def do_padding_black(img):
|
|
|
|
|
img_org_h = img.shape[0]
|
|
|
|
|
img_org_w = img.shape[1]
|
|
|
|
|
|
|
|
|
|
index_start_h = 4
|
|
|
|
|
index_start_w = 4
|
|
|
|
|
|
|
|
|
|
img_padded = np.zeros((img.shape[0] + 2*index_start_h, img.shape[1] + 2*index_start_w, img.shape[2]))
|
|
|
|
|
img_padded[index_start_h: index_start_h + img.shape[0], index_start_w: index_start_w + img.shape[1], :] = img[:, :, :]
|
|
|
|
|
|
|
|
|
|
return img_padded.astype(float)
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
def do_padding_label(img):
|
|
|
|
|
img_org_h = img.shape[0]
|
|
|
|
|
img_org_w = img.shape[1]
|
|
|
|
|
|
|
|
|
|
index_start_h = 4
|
|
|
|
|
index_start_w = 4
|
|
|
|
|
|
|
|
|
|
img_padded = np.zeros((img.shape[0] + 2*index_start_h, img.shape[1] + 2*index_start_w, img.shape[2]))
|
|
|
|
|
img_padded[index_start_h: index_start_h + img.shape[0], index_start_w: index_start_w + img.shape[1], :] = img[:, :, :]
|
|
|
|
|
|
|
|
|
|
return img_padded.astype(np.int16)
|
|
|
|
|
|
|
|
|
|
def do_padding(img, label, height, width):
|
|
|
|
|
height_new=img.shape[0]
|
|
|
|
|
width_new=img.shape[1]
|
|
|
|
|
|
|
|
|
|
h_start = 0
|
|
|
|
|
w_start = 0
|
|
|
|
|
|
|
|
|
|
if img.shape[0] < height:
|
|
|
|
|
h_start = int(abs(height - img.shape[0]) / 2.)
|
|
|
|
|
height_new = height
|
|
|
|
|
|
|
|
|
|
if img.shape[1] < width:
|
|
|
|
|
w_start = int(abs(width - img.shape[1]) / 2.)
|
|
|
|
|
width_new = width
|
|
|
|
|
|
|
|
|
|
img_new = np.ones((height_new, width_new, img.shape[2])).astype(float) * 255
|
|
|
|
|
label_new = np.zeros((height_new, width_new, label.shape[2])).astype(float)
|
|
|
|
|
|
|
|
|
|
img_new[h_start:h_start + img.shape[0], w_start:w_start + img.shape[1], :] = np.copy(img[:, :, :])
|
|
|
|
|
label_new[h_start:h_start + label.shape[0], w_start:w_start + label.shape[1], :] = np.copy(label[:, :, :])
|
|
|
|
|
|
|
|
|
|
return img_new,label_new
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
def get_patches_num_scale(dir_img_f, dir_seg_f, img, label, height, width, indexer, n_patches, scaler):
|
|
|
|
|
if img.shape[0] < height or img.shape[1] < width:
|
|
|
|
|
img, label = do_padding(img, label, height, width)
|
|
|
|
|
|
|
|
|
|
img_h = img.shape[0]
|
|
|
|
|
img_w = img.shape[1]
|
|
|
|
|
|
|
|
|
|
height_scale = int(height * scaler)
|
|
|
|
|
width_scale = int(width * scaler)
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
nxf = img_w / float(width_scale)
|
|
|
|
|
nyf = img_h / float(height_scale)
|
|
|
|
|
|
|
|
|
|
if nxf > int(nxf):
|
|
|
|
|
nxf = int(nxf) + 1
|
|
|
|
|
if nyf > int(nyf):
|
|
|
|
|
nyf = int(nyf) + 1
|
|
|
|
|
|
|
|
|
|
nxf = int(nxf)
|
|
|
|
|
nyf = int(nyf)
|
|
|
|
|
|
|
|
|
|
for i in range(nxf):
|
|
|
|
|
for j in range(nyf):
|
|
|
|
|
index_x_d = i * width_scale
|
|
|
|
|
index_x_u = (i + 1) * width_scale
|
|
|
|
|
|
|
|
|
|
index_y_d = j * height_scale
|
|
|
|
|
index_y_u = (j + 1) * height_scale
|
|
|
|
|
|
|
|
|
|
if index_x_u > img_w:
|
|
|
|
|
index_x_u = img_w
|
|
|
|
|
index_x_d = img_w - width_scale
|
|
|
|
|
if index_y_u > img_h:
|
|
|
|
|
index_y_u = img_h
|
|
|
|
|
index_y_d = img_h - height_scale
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
img_patch = img[index_y_d:index_y_u, index_x_d:index_x_u, :]
|
|
|
|
|
label_patch = label[index_y_d:index_y_u, index_x_d:index_x_u, :]
|
|
|
|
|
|
|
|
|
|
img_patch = resize_image(img_patch, height, width)
|
|
|
|
|
label_patch = resize_image(label_patch, height, width)
|
|
|
|
|
|
|
|
|
|
cv2.imwrite(dir_img_f + '/img_' + str(indexer) + '.png', img_patch)
|
|
|
|
|
cv2.imwrite(dir_seg_f + '/img_' + str(indexer) + '.png', label_patch)
|
|
|
|
|
indexer += 1
|
|
|
|
|
|
|
|
|
|
return indexer
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
def get_patches_num_scale_new(dir_img_f, dir_seg_f, img, label, height, width, indexer, scaler):
|
|
|
|
|
img = resize_image(img, int(img.shape[0] * scaler), int(img.shape[1] * scaler))
|
|
|
|
|
label = resize_image(label, int(label.shape[0] * scaler), int(label.shape[1] * scaler))
|
|
|
|
|
|
|
|
|
|
if img.shape[0] < height or img.shape[1] < width:
|
|
|
|
|
img, label = do_padding(img, label, height, width)
|
|
|
|
|
|
|
|
|
|
img_h = img.shape[0]
|
|
|
|
|
img_w = img.shape[1]
|
|
|
|
|
|
|
|
|
|
height_scale = int(height * 1)
|
|
|
|
|
width_scale = int(width * 1)
|
|
|
|
|
|
|
|
|
|
nxf = img_w / float(width_scale)
|
|
|
|
|
nyf = img_h / float(height_scale)
|
|
|
|
|
|
|
|
|
|
if nxf > int(nxf):
|
|
|
|
|
nxf = int(nxf) + 1
|
|
|
|
|
if nyf > int(nyf):
|
|
|
|
|
nyf = int(nyf) + 1
|
|
|
|
|
|
|
|
|
|
nxf = int(nxf)
|
|
|
|
|
nyf = int(nyf)
|
|
|
|
|
|
|
|
|
|
for i in range(nxf):
|
|
|
|
|
for j in range(nyf):
|
|
|
|
|
index_x_d = i * width_scale
|
|
|
|
|
index_x_u = (i + 1) * width_scale
|
|
|
|
|
|
|
|
|
|
index_y_d = j * height_scale
|
|
|
|
|
index_y_u = (j + 1) * height_scale
|
|
|
|
|
|
|
|
|
|
if index_x_u > img_w:
|
|
|
|
|
index_x_u = img_w
|
|
|
|
|
index_x_d = img_w - width_scale
|
|
|
|
|
if index_y_u > img_h:
|
|
|
|
|
index_y_u = img_h
|
|
|
|
|
index_y_d = img_h - height_scale
|
|
|
|
|
|
|
|
|
|
img_patch = img[index_y_d:index_y_u, index_x_d:index_x_u, :]
|
|
|
|
|
label_patch = label[index_y_d:index_y_u, index_x_d:index_x_u, :]
|
|
|
|
|
|
|
|
|
|
cv2.imwrite(dir_img_f + '/img_' + str(indexer) + '.png', img_patch)
|
|
|
|
|
cv2.imwrite(dir_seg_f + '/img_' + str(indexer) + '.png', label_patch)
|
|
|
|
|
indexer += 1
|
|
|
|
|
|
|
|
|
|
return indexer
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
def provide_patches(imgs_list_train, segs_list_train, dir_img, dir_seg, dir_flow_train_imgs,
|
|
|
|
|
dir_flow_train_labels, input_height, input_width, blur_k, blur_aug,
|
|
|
|
|
padding_white, padding_black, flip_aug, binarization, scaling, degrading,
|
|
|
|
|
brightening, scales, degrade_scales, brightness, flip_index,
|
|
|
|
|
scaling_bluring, scaling_brightness, scaling_binarization, rotation,
|
|
|
|
|
rotation_not_90, thetha, scaling_flip, task, augmentation=False, patches=False):
|
|
|
|
|
|
|
|
|
|
indexer = 0
|
|
|
|
|
for im, seg_i in tqdm(zip(imgs_list_train, segs_list_train)):
|
|
|
|
|
img_name = im.split('.')[0]
|
|
|
|
|
if task == "segmentation":
|
|
|
|
|
dir_of_label_file = os.path.join(dir_seg, img_name + '.png')
|
|
|
|
|
elif task=="enhancement":
|
|
|
|
|
dir_of_label_file = os.path.join(dir_seg, im)
|
|
|
|
|
|
|
|
|
|
if not patches:
|
|
|
|
|
cv2.imwrite(dir_flow_train_imgs + '/img_' + str(indexer) + '.png', resize_image(cv2.imread(dir_img + '/' + im), input_height, input_width))
|
|
|
|
|
cv2.imwrite(dir_flow_train_labels + '/img_' + str(indexer) + '.png', resize_image(cv2.imread(dir_of_label_file), input_height, input_width))
|
|
|
|
|
indexer += 1
|
|
|
|
|
|
|
|
|
|
if augmentation:
|
|
|
|
|
if flip_aug:
|
|
|
|
|
for f_i in flip_index:
|
|
|
|
|
cv2.imwrite(dir_flow_train_imgs + '/img_' + str(indexer) + '.png',
|
|
|
|
|
resize_image(cv2.flip(cv2.imread(dir_img+'/'+im),f_i),input_height,input_width) )
|
|
|
|
|
|
|
|
|
|
cv2.imwrite(dir_flow_train_labels + '/img_' + str(indexer) + '.png',
|
|
|
|
|
resize_image(cv2.flip(cv2.imread(dir_of_label_file), f_i), input_height, input_width))
|
|
|
|
|
indexer += 1
|
|
|
|
|
|
|
|
|
|
if blur_aug:
|
|
|
|
|
for blur_i in blur_k:
|
|
|
|
|
cv2.imwrite(dir_flow_train_imgs + '/img_' + str(indexer) + '.png',
|
|
|
|
|
(resize_image(bluring(cv2.imread(dir_img + '/' + im), blur_i), input_height, input_width)))
|
|
|
|
|
|
|
|
|
|
cv2.imwrite(dir_flow_train_labels + '/img_' + str(indexer) + '.png',
|
|
|
|
|
resize_image(cv2.imread(dir_of_label_file), input_height, input_width))
|
|
|
|
|
indexer += 1
|
|
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if binarization:
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cv2.imwrite(dir_flow_train_imgs + '/img_' + str(indexer) + '.png',
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resize_image(otsu_copy(cv2.imread(dir_img + '/' + im)), input_height, input_width))
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cv2.imwrite(dir_flow_train_labels + '/img_' + str(indexer) + '.png',
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resize_image(cv2.imread(dir_of_label_file), input_height, input_width))
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indexer += 1
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if patches:
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indexer = get_patches(dir_flow_train_imgs, dir_flow_train_labels,
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cv2.imread(dir_img + '/' + im), cv2.imread(dir_of_label_file),
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input_height, input_width, indexer=indexer)
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if augmentation:
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if rotation:
|
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indexer = get_patches(dir_flow_train_imgs, dir_flow_train_labels,
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rotation_90(cv2.imread(dir_img + '/' + im)),
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|
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rotation_90(cv2.imread(dir_of_label_file)),
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input_height, input_width, indexer=indexer)
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if rotation_not_90:
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for thetha_i in thetha:
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img_max_rotated, label_max_rotated = rotation_not_90_func(cv2.imread(dir_img + '/'+im),
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cv2.imread(dir_of_label_file), thetha_i)
|
|
|
|
|
indexer = get_patches(dir_flow_train_imgs, dir_flow_train_labels,
|
|
|
|
|
img_max_rotated,
|
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|
|
|
label_max_rotated,
|
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|
|
input_height, input_width, indexer=indexer)
|
|
|
|
|
if flip_aug:
|
|
|
|
|
for f_i in flip_index:
|
|
|
|
|
indexer = get_patches(dir_flow_train_imgs, dir_flow_train_labels,
|
|
|
|
|
cv2.flip(cv2.imread(dir_img + '/' + im), f_i),
|
|
|
|
|
cv2.flip(cv2.imread(dir_of_label_file), f_i),
|
|
|
|
|
input_height, input_width, indexer=indexer)
|
|
|
|
|
if blur_aug:
|
|
|
|
|
for blur_i in blur_k:
|
|
|
|
|
indexer = get_patches(dir_flow_train_imgs, dir_flow_train_labels,
|
|
|
|
|
bluring(cv2.imread(dir_img + '/' + im), blur_i),
|
|
|
|
|
cv2.imread(dir_of_label_file),
|
|
|
|
|
input_height, input_width, indexer=indexer)
|
|
|
|
|
if padding_black:
|
|
|
|
|
indexer = get_patches(dir_flow_train_imgs, dir_flow_train_labels,
|
|
|
|
|
do_padding_black(cv2.imread(dir_img + '/' + im)),
|
|
|
|
|
do_padding_label(cv2.imread(dir_of_label_file)),
|
|
|
|
|
input_height, input_width, indexer=indexer)
|
|
|
|
|
|
|
|
|
|
if padding_white:
|
|
|
|
|
indexer = get_patches(dir_flow_train_imgs, dir_flow_train_labels,
|
|
|
|
|
do_padding_white(cv2.imread(dir_img + '/'+im)),
|
|
|
|
|
do_padding_label(cv2.imread(dir_of_label_file)),
|
|
|
|
|
input_height, input_width, indexer=indexer)
|
|
|
|
|
|
|
|
|
|
if brightening:
|
|
|
|
|
for factor in brightness:
|
|
|
|
|
try:
|
|
|
|
|
indexer = get_patches(dir_flow_train_imgs, dir_flow_train_labels,
|
|
|
|
|
do_brightening(dir_img + '/' +im, factor),
|
|
|
|
|
cv2.imread(dir_of_label_file),
|
|
|
|
|
input_height, input_width, indexer=indexer)
|
|
|
|
|
except:
|
|
|
|
|
pass
|
|
|
|
|
if scaling:
|
|
|
|
|
for sc_ind in scales:
|
|
|
|
|
indexer = get_patches_num_scale_new(dir_flow_train_imgs, dir_flow_train_labels,
|
|
|
|
|
cv2.imread(dir_img + '/' + im) ,
|
|
|
|
|
cv2.imread(dir_of_label_file),
|
|
|
|
|
input_height, input_width, indexer=indexer, scaler=sc_ind)
|
|
|
|
|
|
|
|
|
|
if degrading:
|
|
|
|
|
for degrade_scale_ind in degrade_scales:
|
|
|
|
|
indexer = get_patches(dir_flow_train_imgs, dir_flow_train_labels,
|
|
|
|
|
do_degrading(cv2.imread(dir_img + '/' + im), degrade_scale_ind),
|
|
|
|
|
cv2.imread(dir_of_label_file),
|
|
|
|
|
input_height, input_width, indexer=indexer)
|
|
|
|
|
|
|
|
|
|
if binarization:
|
|
|
|
|
indexer = get_patches(dir_flow_train_imgs, dir_flow_train_labels,
|
|
|
|
|
otsu_copy(cv2.imread(dir_img + '/' + im)),
|
|
|
|
|
cv2.imread(dir_of_label_file),
|
|
|
|
|
input_height, input_width, indexer=indexer)
|
|
|
|
|
|
|
|
|
|
if scaling_brightness:
|
|
|
|
|
for sc_ind in scales:
|
|
|
|
|
for factor in brightness:
|
|
|
|
|
try:
|
|
|
|
|
indexer = get_patches_num_scale_new(dir_flow_train_imgs,
|
|
|
|
|
dir_flow_train_labels,
|
|
|
|
|
do_brightening(dir_img + '/' + im, factor)
|
|
|
|
|
,cv2.imread(dir_of_label_file)
|
|
|
|
|
,input_height, input_width, indexer=indexer, scaler=sc_ind)
|
|
|
|
|
except:
|
|
|
|
|
pass
|
|
|
|
|
|
|
|
|
|
if scaling_bluring:
|
|
|
|
|
for sc_ind in scales:
|
|
|
|
|
for blur_i in blur_k:
|
|
|
|
|
indexer = get_patches_num_scale_new(dir_flow_train_imgs, dir_flow_train_labels,
|
|
|
|
|
bluring(cv2.imread(dir_img + '/' + im), blur_i),
|
|
|
|
|
cv2.imread(dir_of_label_file),
|
|
|
|
|
input_height, input_width, indexer=indexer, scaler=sc_ind)
|
|
|
|
|
|
|
|
|
|
if scaling_binarization:
|
|
|
|
|
for sc_ind in scales:
|
|
|
|
|
indexer = get_patches_num_scale_new(dir_flow_train_imgs, dir_flow_train_labels,
|
|
|
|
|
otsu_copy(cv2.imread(dir_img + '/' + im)),
|
|
|
|
|
cv2.imread(dir_of_label_file),
|
|
|
|
|
input_height, input_width, indexer=indexer, scaler=sc_ind)
|
|
|
|
|
|
|
|
|
|
if scaling_flip:
|
|
|
|
|
for sc_ind in scales:
|
|
|
|
|
for f_i in flip_index:
|
|
|
|
|
indexer = get_patches_num_scale_new(dir_flow_train_imgs, dir_flow_train_labels,
|
|
|
|
|
cv2.flip( cv2.imread(dir_img + '/' + im), f_i),
|
|
|
|
|
cv2.flip(cv2.imread(dir_of_label_file), f_i),
|
|
|
|
|
input_height, input_width, indexer=indexer, scaler=sc_ind)
|
