first working update of branch

This commit is contained in:
vahidrezanezhad 2024-04-16 01:00:48 +02:00
parent 02b1436f39
commit d27647a0f1
4 changed files with 452 additions and 151 deletions

273
utils.py
View file

@ -9,6 +9,15 @@ from tqdm import tqdm
import imutils
import math
def do_brightening(img_in_dir, factor):
im = Image.open(img_in_dir)
enhancer = ImageEnhance.Brightness(im)
out_img = enhancer.enhance(factor)
out_img = out_img.convert('RGB')
opencv_img = np.array(out_img)
opencv_img = opencv_img[:,:,::-1].copy()
return opencv_img
def bluring(img_in, kind):
if kind == 'gauss':
@ -138,11 +147,11 @@ def IoU(Yi, y_predi):
FP = np.sum((Yi != c) & (y_predi == c))
FN = np.sum((Yi == c) & (y_predi != c))
IoU = TP / float(TP + FP + FN)
print("class {:02.0f}: #TP={:6.0f}, #FP={:6.0f}, #FN={:5.0f}, IoU={:4.3f}".format(c, TP, FP, FN, IoU))
#print("class {:02.0f}: #TP={:6.0f}, #FP={:6.0f}, #FN={:5.0f}, IoU={:4.3f}".format(c, TP, FP, FN, IoU))
IoUs.append(IoU)
mIoU = np.mean(IoUs)
print("_________________")
print("Mean IoU: {:4.3f}".format(mIoU))
#print("_________________")
#print("Mean IoU: {:4.3f}".format(mIoU))
return mIoU
@ -241,124 +250,170 @@ def get_patches(dir_img_f, dir_seg_f, img, label, height, width, indexer):
return indexer
def do_padding(img, label, height, width):
height_new = img.shape[0]
width_new = img.shape[1]
def do_padding_white(img):
img_org_h = img.shape[0]
img_org_w = img.shape[1]
index_start_h = 4
index_start_w = 4
img_padded = np.zeros((img.shape[0] + 2*index_start_h, img.shape[1]+ 2*index_start_w, img.shape[2])) + 255
img_padded[index_start_h: index_start_h + img.shape[0], index_start_w: index_start_w + img.shape[1], :] = img[:, :, :]
return img_padded.astype(float)
def do_degrading(img, scale):
img_org_h = img.shape[0]
img_org_w = img.shape[1]
img_res = resize_image(img, int(img_org_h * scale), int(img_org_w * scale))
return resize_image(img_res, img_org_h, img_org_w)
def do_padding_black(img):
img_org_h = img.shape[0]
img_org_w = img.shape[1]
index_start_h = 4
index_start_w = 4
img_padded = np.zeros((img.shape[0] + 2*index_start_h, img.shape[1] + 2*index_start_w, img.shape[2]))
img_padded[index_start_h: index_start_h + img.shape[0], index_start_w: index_start_w + img.shape[1], :] = img[:, :, :]
return img_padded.astype(float)
def do_padding_label(img):
img_org_h = img.shape[0]
img_org_w = img.shape[1]
index_start_h = 4
index_start_w = 4
img_padded = np.zeros((img.shape[0] + 2*index_start_h, img.shape[1] + 2*index_start_w, img.shape[2]))
img_padded[index_start_h: index_start_h + img.shape[0], index_start_w: index_start_w + img.shape[1], :] = img[:, :, :]
return img_padded.astype(np.int16)
def do_padding(img, label, height, width):
height_new=img.shape[0]
width_new=img.shape[1]
h_start = 0
w_start = 0
if img.shape[0] < height:
h_start = int(abs(height - img.shape[0]) / 2.)
height_new = height
if img.shape[1] < width:
w_start = int(abs(width - img.shape[1]) / 2.)
width_new = width
img_new = np.ones((height_new, width_new, img.shape[2])).astype(float) * 255
label_new = np.zeros((height_new, width_new, label.shape[2])).astype(float)
img_new[h_start:h_start + img.shape[0], w_start:w_start + img.shape[1], :] = np.copy(img[:, :, :])
label_new[h_start:h_start + label.shape[0], w_start:w_start + label.shape[1], :] = np.copy(label[:, :, :])
return img_new, label_new
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
@ -366,78 +421,65 @@ def get_patches_num_scale_new(dir_img_f, dir_seg_f, img, label, height, width, i
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))
def provide_patches(imgs_list_train, segs_list_train, dir_img, dir_seg, dir_flow_train_imgs,
dir_flow_train_labels, input_height, input_width, blur_k, blur_aug,
padding_white, padding_black, flip_aug, binarization, scaling, degrading,
brightening, scales, degrade_scales, brightness, flip_index,
scaling_bluring, scaling_brightness, scaling_binarization, rotation,
rotation_not_90, thetha, scaling_flip, augmentation=False, patches=False):
indexer = 0
for im, seg_i in tqdm(zip(imgs_cv_train, segs_cv_train)):
for im, seg_i in tqdm(zip(imgs_list_train, segs_list_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))
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))
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))
resize_image(cv2.flip(cv2.imread(dir_seg + '/' + img_name + '.png'), f_i), input_height, input_width))
indexer += 1
if blur_aug:
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)))
(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))
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)
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)
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)
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,
@ -448,47 +490,84 @@ def provide_patches(dir_img, dir_seg, dir_flow_train_imgs,
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:
if blur_aug:
for blur_i in blur_k:
indexer = get_patches(dir_flow_train_imgs, dir_flow_train_labels,
bluring(cv2.imread(dir_img + '/' + im), blur_i),
cv2.imread(dir_seg + '/' + img_name + '.png'),
input_height, input_width, indexer=indexer)
if scaling:
input_height, input_width, indexer=indexer)
if padding_black:
indexer = get_patches(dir_flow_train_imgs, dir_flow_train_labels,
do_padding_black(cv2.imread(dir_img + '/' + im)),
do_padding_label(cv2.imread(dir_seg + '/' + img_name + '.png')),
input_height, input_width, indexer=indexer)
if padding_white:
indexer = get_patches(dir_flow_train_imgs, dir_flow_train_labels,
do_padding_white(cv2.imread(dir_img + '/'+im)),
do_padding_label(cv2.imread(dir_seg + '/' + img_name + '.png')),
input_height, input_width, indexer=indexer)
if brightening:
for factor in brightness:
try:
indexer = get_patches(dir_flow_train_imgs, dir_flow_train_labels,
do_brightening(dir_img + '/' +im, factor),
cv2.imread(dir_seg + '/' + img_name + '.png'),
input_height, input_width, indexer=indexer)
except:
pass
if scaling:
for sc_ind in scales:
indexer = get_patches_num_scale_new(dir_flow_train_imgs, dir_flow_train_labels,
cv2.imread(dir_img + '/' + im),
cv2.imread(dir_img + '/' + im) ,
cv2.imread(dir_seg + '/' + img_name + '.png'),
input_height, input_width, indexer=indexer, scaler=sc_ind)
if degrading:
for degrade_scale_ind in degrade_scales:
indexer = get_patches(dir_flow_train_imgs, dir_flow_train_labels,
do_degrading(cv2.imread(dir_img + '/' + im), degrade_scale_ind),
cv2.imread(dir_seg + '/' + img_name + '.png'),
input_height, input_width, indexer=indexer)
if binarization:
indexer = get_patches(dir_flow_train_imgs, dir_flow_train_labels,
otsu_copy(cv2.imread(dir_img + '/' + im)),
cv2.imread(dir_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)