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495 lines
20 KiB
Python
495 lines
20 KiB
Python
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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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):
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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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# print(img_folder+'/'+n[i])
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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.
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train_mask = cv2.imread(mask_folder + '/' + filename + '.png')
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# print(mask_folder+'/'+filename+'.png')
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# print(train_mask.shape)
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train_mask = get_one_hot(resize_image(train_mask, input_height, input_width), input_height, input_width,
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n_classes)
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# train_mask = train_mask.reshape(224, 224, 1) # Add extra dimension for parity with train_img size [512 * 512 * 3]
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mask[i - c] = train_mask
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except:
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img[i - c] = np.ones((input_height, input_width, 3)).astype('float')
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mask[i - c] = np.zeros((input_height, input_width, n_classes)).astype('float')
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c += batch_size
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if c + batch_size >= len(os.listdir(img_folder)):
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c = 0
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random.shuffle(n)
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yield img, mask
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def otsu_copy(img):
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img_r = np.zeros(img.shape)
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img1 = img[:, :, 0]
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img2 = img[:, :, 1]
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img3 = img[:, :, 2]
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_, threshold1 = cv2.threshold(img1, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
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_, 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)
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img_r[:, :, 0] = threshold1
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img_r[:, :, 1] = threshold1
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img_r[:, :, 2] = threshold1
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return img_r
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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:
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img, label = do_padding(img, label, height, width)
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img_h = img.shape[0]
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img_w = img.shape[1]
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nxf = img_w / float(width)
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nyf = img_h / float(height)
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if nxf > int(nxf):
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nxf = int(nxf) + 1
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if nyf > int(nyf):
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nyf = int(nyf) + 1
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nxf = int(nxf)
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nyf = int(nyf)
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for i in range(nxf):
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for j in range(nyf):
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index_x_d = i * width
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index_x_u = (i + 1) * width
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index_y_d = j * height
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index_y_u = (j + 1) * height
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if index_x_u > img_w:
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index_x_u = img_w
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index_x_d = img_w - width
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if index_y_u > img_h:
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index_y_u = img_h
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index_y_d = img_h - height
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img_patch = img[index_y_d:index_y_u, index_x_d:index_x_u, :]
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label_patch = label[index_y_d:index_y_u, index_x_d:index_x_u, :]
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cv2.imwrite(dir_img_f + '/img_' + str(indexer) + '.png', img_patch)
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cv2.imwrite(dir_seg_f + '/img_' + str(indexer) + '.png', label_patch)
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indexer += 1
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return indexer
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def do_padding(img, label, height, width):
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height_new = img.shape[0]
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width_new = img.shape[1]
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h_start = 0
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w_start = 0
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if img.shape[0] < height:
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h_start = int(abs(height - img.shape[0]) / 2.)
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height_new = height
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if img.shape[1] < width:
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w_start = int(abs(width - img.shape[1]) / 2.)
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width_new = width
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img_new = np.ones((height_new, width_new, img.shape[2])).astype(float) * 255
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label_new = np.zeros((height_new, width_new, label.shape[2])).astype(float)
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img_new[h_start:h_start + img.shape[0], w_start:w_start + img.shape[1], :] = np.copy(img[:, :, :])
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label_new[h_start:h_start + label.shape[0], w_start:w_start + label.shape[1], :] = np.copy(label[:, :, :])
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return img_new, label_new
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def get_patches_num_scale(dir_img_f, dir_seg_f, img, label, height, width, indexer, n_patches, scaler):
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if img.shape[0] < height or img.shape[1] < width:
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img, label = do_padding(img, label, height, width)
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img_h = img.shape[0]
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img_w = img.shape[1]
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height_scale = int(height * scaler)
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width_scale = int(width * scaler)
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nxf = img_w / float(width_scale)
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nyf = img_h / float(height_scale)
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if nxf > int(nxf):
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nxf = int(nxf) + 1
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if nyf > int(nyf):
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nyf = int(nyf) + 1
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nxf = int(nxf)
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nyf = int(nyf)
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for i in range(nxf):
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for j in range(nyf):
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index_x_d = i * width_scale
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index_x_u = (i + 1) * width_scale
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index_y_d = j * height_scale
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index_y_u = (j + 1) * height_scale
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if index_x_u > img_w:
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index_x_u = img_w
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index_x_d = img_w - width_scale
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if index_y_u > img_h:
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index_y_u = img_h
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index_y_d = img_h - height_scale
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img_patch = img[index_y_d:index_y_u, index_x_d:index_x_u, :]
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label_patch = label[index_y_d:index_y_u, index_x_d:index_x_u, :]
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img_patch = resize_image(img_patch, height, width)
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label_patch = resize_image(label_patch, height, width)
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cv2.imwrite(dir_img_f + '/img_' + str(indexer) + '.png', img_patch)
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cv2.imwrite(dir_seg_f + '/img_' + str(indexer) + '.png', label_patch)
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indexer += 1
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return indexer
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def get_patches_num_scale_new(dir_img_f, dir_seg_f, img, label, height, width, indexer, scaler):
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img = resize_image(img, int(img.shape[0] * scaler), int(img.shape[1] * scaler))
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label = resize_image(label, int(label.shape[0] * scaler), int(label.shape[1] * scaler))
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if img.shape[0] < height or img.shape[1] < width:
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img, label = do_padding(img, label, height, width)
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img_h = img.shape[0]
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img_w = img.shape[1]
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height_scale = int(height * 1)
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width_scale = int(width * 1)
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nxf = img_w / float(width_scale)
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nyf = img_h / float(height_scale)
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if nxf > int(nxf):
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nxf = int(nxf) + 1
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if nyf > int(nyf):
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nyf = int(nyf) + 1
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nxf = int(nxf)
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nyf = int(nyf)
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for i in range(nxf):
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for j in range(nyf):
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index_x_d = i * width_scale
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index_x_u = (i + 1) * width_scale
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index_y_d = j * height_scale
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index_y_u = (j + 1) * height_scale
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if index_x_u > img_w:
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index_x_u = img_w
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index_x_d = img_w - width_scale
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if index_y_u > img_h:
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index_y_u = img_h
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index_y_d = img_h - height_scale
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img_patch = img[index_y_d:index_y_u, index_x_d:index_x_u, :]
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label_patch = label[index_y_d:index_y_u, index_x_d:index_x_u, :]
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# img_patch=resize_image(img_patch,height,width)
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# label_patch=resize_image(label_patch,height,width)
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cv2.imwrite(dir_img_f + '/img_' + str(indexer) + '.png', img_patch)
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cv2.imwrite(dir_seg_f + '/img_' + str(indexer) + '.png', label_patch)
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indexer += 1
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return indexer
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def provide_patches(dir_img, dir_seg, dir_flow_train_imgs,
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dir_flow_train_labels,
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input_height, input_width, blur_k, blur_aug,
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flip_aug, binarization, scaling, scales, flip_index,
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scaling_bluring, scaling_binarization, rotation,
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rotation_not_90, thetha, scaling_flip,
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augmentation=False, patches=False):
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imgs_cv_train = np.array(os.listdir(dir_img))
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segs_cv_train = np.array(os.listdir(dir_seg))
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indexer = 0
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for im, seg_i in tqdm(zip(imgs_cv_train, segs_cv_train)):
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img_name = im.split('.')[0]
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if not patches:
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cv2.imwrite(dir_flow_train_imgs + '/img_' + str(indexer) + '.png',
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resize_image(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_seg + '/' + img_name + '.png'), input_height, input_width))
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indexer += 1
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if augmentation:
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if flip_aug:
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for f_i in flip_index:
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cv2.imwrite(dir_flow_train_imgs + '/img_' + str(indexer) + '.png',
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resize_image(cv2.flip(cv2.imread(dir_img + '/' + im), f_i), input_height,
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input_width))
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cv2.imwrite(dir_flow_train_labels + '/img_' + str(indexer) + '.png',
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resize_image(cv2.flip(cv2.imread(dir_seg + '/' + img_name + '.png'), f_i),
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input_height, input_width))
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indexer += 1
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if blur_aug:
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for blur_i in blur_k:
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cv2.imwrite(dir_flow_train_imgs + '/img_' + str(indexer) + '.png',
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(resize_image(bluring(cv2.imread(dir_img + '/' + im), blur_i), input_height,
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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_seg + '/' + img_name + '.png'), input_height,
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input_width))
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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_seg + '/' + img_name + '.png'), 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_seg + '/' + img_name + '.png'),
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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,
|
|
rotation_90(cv2.imread(dir_img + '/' + im)),
|
|
rotation_90(cv2.imread(dir_seg + '/' + img_name + '.png')),
|
|
input_height, input_width, indexer=indexer)
|
|
|
|
if rotation_not_90:
|
|
|
|
for thetha_i in thetha:
|
|
img_max_rotated, label_max_rotated = rotation_not_90_func(cv2.imread(dir_img + '/' + im),
|
|
cv2.imread(
|
|
dir_seg + '/' + img_name + '.png'),
|
|
thetha_i)
|
|
indexer = get_patches(dir_flow_train_imgs, dir_flow_train_labels,
|
|
img_max_rotated,
|
|
label_max_rotated,
|
|
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_seg + '/' + img_name + '.png'), 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_seg + '/' + img_name + '.png'),
|
|
input_height, input_width, indexer=indexer)
|
|
|
|
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_seg + '/' + img_name + '.png'),
|
|
input_height, input_width, indexer=indexer, scaler=sc_ind)
|
|
if binarization:
|
|
indexer = get_patches(dir_flow_train_imgs, dir_flow_train_labels,
|
|
otsu_copy(cv2.imread(dir_img + '/' + im)),
|
|
cv2.imread(dir_seg + '/' + img_name + '.png'),
|
|
input_height, input_width, indexer=indexer)
|
|
|
|
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_seg + '/' + img_name + '.png'),
|
|
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_seg + '/' + img_name + '.png'),
|
|
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_seg + '/' + img_name + '.png'),
|
|
f_i),
|
|
input_height, input_width, indexer=indexer,
|
|
scaler=sc_ind)
|