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https://github.com/qurator-spk/sbb_pixelwise_segmentation.git
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code formatting with black; typos
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cneud
parent
5f84938839
commit
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8 changed files with 741 additions and 768 deletions
763
utils.py
763
utils.py
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@ -10,18 +10,17 @@ import imutils
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import math
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def bluring(img_in,kind):
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if kind=='guass':
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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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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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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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@ -37,461 +36,459 @@ def elastic_transform(image, alpha, sigma,seedj, random_state=None):
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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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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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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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"""
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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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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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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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# 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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return wr, hr
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def rotate_max_area(image,rotated, rotated_label,angle):
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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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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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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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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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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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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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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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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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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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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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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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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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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),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,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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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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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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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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index_x_d = i * width
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index_x_u = (i + 1) * width
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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):
|
||||
|
||||
|
||||
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
|
||||
index_y_d = j * height
|
||||
index_y_u = (j + 1) * height
|
||||
|
||||
return indexer
|
||||
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
|
||||
|
||||
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,:]
|
||||
|
||||
#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
|
||||
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(dir_img,dir_seg,dir_flow_train_imgs,
|
||||
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, :]
|
||||
|
||||
# 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 provide_patches(dir_img, dir_seg, dir_flow_train_imgs,
|
||||
dir_flow_train_labels,
|
||||
input_height,input_width,blur_k,blur_aug,
|
||||
flip_aug,binarization,scaling,scales,flip_index,
|
||||
scaling_bluring,scaling_binarization,rotation,
|
||||
rotation_not_90,thetha,scaling_flip,
|
||||
augmentation=False,patches=False):
|
||||
|
||||
imgs_cv_train=np.array(os.listdir(dir_img))
|
||||
segs_cv_train=np.array(os.listdir(dir_seg))
|
||||
|
||||
indexer=0
|
||||
for im, seg_i in tqdm(zip(imgs_cv_train,segs_cv_train)):
|
||||
img_name=im.split('.')[0]
|
||||
input_height, input_width, blur_k, blur_aug,
|
||||
flip_aug, binarization, scaling, scales, flip_index,
|
||||
scaling_bluring, scaling_binarization, rotation,
|
||||
rotation_not_90, thetha, scaling_flip,
|
||||
augmentation=False, patches=False):
|
||||
imgs_cv_train = np.array(os.listdir(dir_img))
|
||||
segs_cv_train = np.array(os.listdir(dir_seg))
|
||||
|
||||
indexer = 0
|
||||
for im, seg_i in tqdm(zip(imgs_cv_train, segs_cv_train)):
|
||||
img_name = im.split('.')[0]
|
||||
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_seg+'/'+img_name+'.png'),input_height,input_width ) )
|
||||
indexer+=1
|
||||
|
||||
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_seg + '/' + img_name + '.png'), 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_seg+'/'+img_name+'.png'),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_seg+'/'+img_name+'.png'),input_height,input_width) )
|
||||
indexer+=1
|
||||
|
||||
|
||||
if binarization:
|
||||
cv2.imwrite(dir_flow_train_imgs+'/img_'+str(indexer)+'.png',
|
||||
resize_image(otsu_copy( cv2.imread(dir_img+'/'+im)),input_height,input_width ))
|
||||
|
||||
cv2.imwrite(dir_flow_train_labels+'/img_'+str(indexer)+'.png',
|
||||
resize_image( cv2.imread(dir_seg+'/'+img_name+'.png'),input_height,input_width ))
|
||||
indexer+=1
|
||||
|
||||
|
||||
|
||||
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_seg + '/' + img_name + '.png'), 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_seg + '/' + img_name + '.png'), input_height,
|
||||
input_width))
|
||||
indexer += 1
|
||||
|
||||
if binarization:
|
||||
cv2.imwrite(dir_flow_train_imgs + '/img_' + str(indexer) + '.png',
|
||||
resize_image(otsu_copy(cv2.imread(dir_img + '/' + im)), input_height, input_width))
|
||||
|
||||
cv2.imwrite(dir_flow_train_labels + '/img_' + str(indexer) + '.png',
|
||||
resize_image(cv2.imread(dir_seg + '/' + img_name + '.png'), input_height, input_width))
|
||||
indexer += 1
|
||||
|
||||
|
||||
|
||||
if patches:
|
||||
|
||||
indexer=get_patches(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)
|
||||
|
||||
|
||||
indexer = get_patches(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)
|
||||
|
||||
if augmentation:
|
||||
|
||||
|
||||
if rotation:
|
||||
|
||||
|
||||
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)
|
||||
|
||||
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)
|
||||
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:
|
||||
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)
|
||||
|
||||
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:
|
||||
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)
|
||||
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:
|
||||
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)
|
||||
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:
|
||||
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:
|
||||
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)
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
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)
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue