mirror of
https://github.com/qurator-spk/sbb_pixelwise_segmentation.git
synced 2025-06-09 11:50:04 +02:00
first updates, padding, rotations
This commit is contained in:
vahid
parent
63fcb96189
commit
5fb7552dbe
3 changed files with 319 additions and 151 deletions
265
utils.py
265
utils.py
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@ -6,7 +6,8 @@ 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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@ -19,6 +20,79 @@ def bluring(img_in,kind):
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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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@ -65,7 +139,7 @@ def IoU(Yi,y_predi):
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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 = 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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@ -73,18 +147,26 @@ def data_gen(img_folder, mask_folder, batch_size,input_height, input_width,n_cla
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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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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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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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@ -104,16 +186,10 @@ def otsu_copy(img):
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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 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 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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@ -151,12 +227,39 @@ def get_patches(dir_img_f,dir_seg_f,img,label,height,width,indexer):
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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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def get_patches_num_scale(dir_img_f,dir_seg_f,img,label,height,width,indexer,scaler):
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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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@ -204,6 +307,58 @@ def get_patches_num_scale(dir_img_f,dir_seg_f,img,label,height,width,indexer,sca
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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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@ -211,6 +366,7 @@ def provide_patches(dir_img,dir_seg,dir_flow_train_imgs,
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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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@ -218,25 +374,15 @@ def provide_patches(dir_img,dir_seg,dir_flow_train_imgs,
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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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#print(im, seg_i)
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img_name=im.split('.')[0]
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print(img_name,'img_name')
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if not patches:
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cv2.imwrite(dir_flow_train_imgs+'/img_'+str(indexer)+'.png', 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' , 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 rotation:
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cv2.imwrite(dir_flow_train_imgs+'/img_'+str(indexer)+'.png',
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rotation_90( resize_image(cv2.imread(dir_img+'/'+im),
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input_height,input_width) ) )
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cv2.imwrite(dir_flow_train_labels+'/img_'+str(indexer)+'.png',
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rotation_90 ( resize_image(cv2.imread(dir_seg+'/'+img_name+'.png'),
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input_height,input_width) ) )
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indexer+=1
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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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@ -270,10 +416,10 @@ def provide_patches(dir_img,dir_seg,dir_flow_train_imgs,
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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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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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@ -284,29 +430,37 @@ def provide_patches(dir_img,dir_seg,dir_flow_train_imgs,
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rotation_90( cv2.imread(dir_img+'/'+im) ),
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rotation_90( cv2.imread(dir_seg+'/'+img_name+'.png') ),
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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),cv2.imread(dir_seg+'/'+img_name+'.png'),thetha_i)
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indexer=get_patches(dir_flow_train_imgs,dir_flow_train_labels,
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img_max_rotated,
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label_max_rotated,
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input_height,input_width,indexer=indexer)
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if flip_aug:
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for f_i in flip_index:
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indexer=get_patches(dir_flow_train_imgs,dir_flow_train_labels,
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cv2.flip( cv2.imread(dir_img+'/'+im) , f_i),
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cv2.flip( cv2.imread(dir_seg+'/'+img_name+'.png') ,f_i),
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input_height,input_width,indexer=indexer)
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if blur_aug:
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for blur_i in blur_k:
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indexer=get_patches(dir_flow_train_imgs,dir_flow_train_labels,
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bluring( cv2.imread(dir_img+'/'+im) , blur_i),
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cv2.imread(dir_seg+'/'+img_name+'.png'),
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input_height,input_width,indexer=indexer)
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input_height,input_width,indexer=indexer)
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if scaling:
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for sc_ind in scales:
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indexer=get_patches_num_scale(dir_flow_train_imgs,dir_flow_train_labels,
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cv2.imread(dir_img+'/'+im) ,
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cv2.imread(dir_seg+'/'+img_name+'.png'),
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indexer=get_patches_num_scale_new(dir_flow_train_imgs,dir_flow_train_labels,
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cv2.imread(dir_img+'/'+im) ,
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cv2.imread(dir_seg+'/'+img_name+'.png'),
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input_height,input_width,indexer=indexer,scaler=sc_ind)
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if binarization:
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indexer=get_patches(dir_flow_train_imgs,dir_flow_train_labels,
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otsu_copy( cv2.imread(dir_img+'/'+im)),
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cv2.imread(dir_seg+'/'+img_name+'.png'),
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@ -317,17 +471,26 @@ def provide_patches(dir_img,dir_seg,dir_flow_train_imgs,
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if scaling_bluring:
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for sc_ind in scales:
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for blur_i in blur_k:
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indexer=get_patches_num_scale(dir_flow_train_imgs,dir_flow_train_labels,
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indexer=get_patches_num_scale_new(dir_flow_train_imgs,dir_flow_train_labels,
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bluring( cv2.imread(dir_img+'/'+im) , blur_i) ,
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cv2.imread(dir_seg+'/'+img_name+'.png') ,
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input_height,input_width,indexer=indexer,scaler=sc_ind)
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if scaling_binarization:
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for sc_ind in scales:
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indexer=get_patches_num_scale(dir_flow_train_imgs,dir_flow_train_labels,
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otsu_copy( cv2.imread(dir_img+'/'+im)) ,
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cv2.imread(dir_seg+'/'+img_name+'.png'),
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indexer=get_patches_num_scale_new(dir_flow_train_imgs,dir_flow_train_labels,
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otsu_copy( cv2.imread(dir_img+'/'+im)) ,
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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)
|
||||
|
||||
|
||||
|
||||
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue