mirror of
https://github.com/qurator-spk/sbb_pixelwise_segmentation.git
synced 2025-06-09 11:50:04 +02:00
first working update of branch
This commit is contained in:
vahidrezanezhad
parent
02b1436f39
commit
d27647a0f1
4 changed files with 452 additions and 151 deletions
273
utils.py
273
utils.py
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@ -9,6 +9,15 @@ from tqdm import tqdm
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import imutils
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import math
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def do_brightening(img_in_dir, factor):
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im = Image.open(img_in_dir)
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enhancer = ImageEnhance.Brightness(im)
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out_img = enhancer.enhance(factor)
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out_img = out_img.convert('RGB')
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opencv_img = np.array(out_img)
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opencv_img = opencv_img[:,:,::-1].copy()
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return opencv_img
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def bluring(img_in, kind):
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if kind == 'gauss':
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@ -138,11 +147,11 @@ def IoU(Yi, y_predi):
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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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#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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#print("_________________")
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#print("Mean IoU: {:4.3f}".format(mIoU))
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return mIoU
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@ -241,124 +250,170 @@ def get_patches(dir_img_f, dir_seg_f, img, label, height, width, indexer):
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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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def do_padding_white(img):
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img_org_h = img.shape[0]
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img_org_w = img.shape[1]
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index_start_h = 4
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index_start_w = 4
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img_padded = np.zeros((img.shape[0] + 2*index_start_h, img.shape[1]+ 2*index_start_w, img.shape[2])) + 255
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img_padded[index_start_h: index_start_h + img.shape[0], index_start_w: index_start_w + img.shape[1], :] = img[:, :, :]
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return img_padded.astype(float)
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def do_degrading(img, scale):
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img_org_h = img.shape[0]
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img_org_w = img.shape[1]
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img_res = resize_image(img, int(img_org_h * scale), int(img_org_w * scale))
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return resize_image(img_res, img_org_h, img_org_w)
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def do_padding_black(img):
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img_org_h = img.shape[0]
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img_org_w = img.shape[1]
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index_start_h = 4
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index_start_w = 4
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img_padded = np.zeros((img.shape[0] + 2*index_start_h, img.shape[1] + 2*index_start_w, img.shape[2]))
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img_padded[index_start_h: index_start_h + img.shape[0], index_start_w: index_start_w + img.shape[1], :] = img[:, :, :]
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return img_padded.astype(float)
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def do_padding_label(img):
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img_org_h = img.shape[0]
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img_org_w = img.shape[1]
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index_start_h = 4
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index_start_w = 4
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img_padded = np.zeros((img.shape[0] + 2*index_start_h, img.shape[1] + 2*index_start_w, img.shape[2]))
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img_padded[index_start_h: index_start_h + img.shape[0], index_start_w: index_start_w + img.shape[1], :] = img[:, :, :]
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return img_padded.astype(np.int16)
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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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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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@ -366,78 +421,65 @@ def get_patches_num_scale_new(dir_img_f, dir_seg_f, img, label, height, width, i
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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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def provide_patches(imgs_list_train, segs_list_train, dir_img, dir_seg, dir_flow_train_imgs,
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dir_flow_train_labels, input_height, input_width, blur_k, blur_aug,
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padding_white, padding_black, flip_aug, binarization, scaling, degrading,
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brightening, scales, degrade_scales, brightness, flip_index,
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scaling_bluring, scaling_brightness, scaling_binarization, rotation,
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rotation_not_90, thetha, scaling_flip, augmentation=False, patches=False):
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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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for im, seg_i in tqdm(zip(imgs_list_train, segs_list_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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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 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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resize_image(cv2.flip(cv2.imread(dir_img+'/'+im),f_i),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.flip(cv2.imread(dir_seg + '/' + img_name + '.png'), f_i),
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input_height, input_width))
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resize_image(cv2.flip(cv2.imread(dir_seg + '/' + img_name + '.png'), f_i), input_height, input_width))
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indexer += 1
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if blur_aug:
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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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(resize_image(bluring(cv2.imread(dir_img + '/' + im), blur_i), 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,
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input_width))
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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 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,
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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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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),
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cv2.imread(
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dir_seg + '/' + img_name + '.png'),
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thetha_i)
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img_max_rotated, label_max_rotated = rotation_not_90_func(cv2.imread(dir_img + '/'+im),
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cv2.imread(dir_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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@ -448,47 +490,84 @@ def provide_patches(dir_img, dir_seg, dir_flow_train_imgs,
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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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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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if scaling:
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input_height, input_width, indexer=indexer)
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if padding_black:
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indexer = get_patches(dir_flow_train_imgs, dir_flow_train_labels,
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do_padding_black(cv2.imread(dir_img + '/' + im)),
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do_padding_label(cv2.imread(dir_seg + '/' + img_name + '.png')),
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input_height, input_width, indexer=indexer)
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if padding_white:
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indexer = get_patches(dir_flow_train_imgs, dir_flow_train_labels,
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do_padding_white(cv2.imread(dir_img + '/'+im)),
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do_padding_label(cv2.imread(dir_seg + '/' + img_name + '.png')),
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input_height, input_width, indexer=indexer)
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if brightening:
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for factor in brightness:
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try:
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indexer = get_patches(dir_flow_train_imgs, dir_flow_train_labels,
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do_brightening(dir_img + '/' +im, factor),
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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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except:
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pass
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if scaling:
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for sc_ind in scales:
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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_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 degrading:
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for degrade_scale_ind in degrade_scales:
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indexer = get_patches(dir_flow_train_imgs, dir_flow_train_labels,
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do_degrading(cv2.imread(dir_img + '/' + im), degrade_scale_ind),
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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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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:
|
||||
if scaling_brightness:
|
||||
for sc_ind in scales:
|
||||
for factor in brightness:
|
||||
try:
|
||||
indexer = get_patches_num_scale_new(dir_flow_train_imgs,
|
||||
dir_flow_train_labels,
|
||||
do_brightening(dir_img + '/' + im, factor)
|
||||
,cv2.imread(dir_seg + '/' + img_name + '.png')
|
||||
,input_height, input_width, indexer=indexer, scaler=sc_ind)
|
||||
except:
|
||||
pass
|
||||
|
||||
if scaling_bluring:
|
||||
for sc_ind in scales:
|
||||
for blur_i in blur_k:
|
||||
indexer = get_patches_num_scale_new(dir_flow_train_imgs, dir_flow_train_labels,
|
||||
bluring(cv2.imread(dir_img + '/' + im), blur_i),
|
||||
cv2.imread(dir_seg + '/' + img_name + '.png'),
|
||||
input_height, input_width, indexer=indexer,
|
||||
scaler=sc_ind)
|
||||
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:
|
||||
|
||||
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)
|
||||
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