def proposed_test_each(op, data, size):
acc_list = []
auc_list = []
for path, img, mask, ground in data:
img = 255 - img[:, :, 1]
line_str = op(path, img, mask, size)
line_str_norm = gray_norm(line_str, mask)
bin = find_best_thresh(line_str_norm, ground, mask)[1]
temp_acc = []
for optic_thresh in range(1, 255):
optic = cached_optic_norm(path, img, mask, size)
optic = cv2.threshold(optic, optic_thresh, 255,
cv2.THRESH_BINARY)[1]
optic = cv2.erode(optic, np.ones((3, 3), np.uint8), iterations=1)
bin_subtract = bin.copy()
bin_subtract[optic == 255] = 0
acc = accuracy(ground, bin_subtract)
temp_acc.append(acc)
best_optic_thresh = np.argmax(temp_acc) + 1
optic = cached_optic_norm(path, img, mask, size)
optic = cv2.threshold(optic, best_optic_thresh, 255,
cv2.THRESH_BINARY)[1]
optic = cv2.erode(optic, np.ones((3, 3), np.uint8), iterations=1)
temp_acc = []
for proposed_thresh in range(1, 255):
min_window = proposed_norm(path, img, mask, size)
min_window = cv2.threshold(min_window, proposed_thresh, 255,
cv2.THRESH_BINARY)[1]
bin_subtract = bin.copy()
bin_subtract[optic == 255] = 0
bin_subtract[min_window == 255] = 0
acc = accuracy(ground, bin_subtract)
temp_acc.append(acc)
best_acc = np.max(temp_acc)
acc_list.append(best_acc)
return np.mean(acc_list)
def optic_train(op, thresh, data, size):
avg_acc_list = []
for optic_thresh in range(1, 255):
acc_list = []
for path, img, mask, ground in data:
img = 255 - img[:, :, 1]
optic = cached_optic_norm(path, img, mask, size)
optic = cv2.threshold(optic, optic_thresh, 255,
cv2.THRESH_BINARY)[1]
optic = cv2.erode(optic, np.ones((3, 3), np.uint8), iterations=1)
line_str = op(path, img, mask, size)
line_str[optic == 255] = line_str[mask == 255].min()
line_str = gray_norm(line_str, mask)
bin = cv2.threshold(line_str, thresh, 255, cv2.THRESH_BINARY)[1]
bin_fov = bin[mask == 255]
ground_fov = ground[mask == 255]
acc = accuracy(bin_fov, ground_fov)
acc_list.append(acc)
avg_acc = np.mean(acc_list)
avg_acc_list.append(avg_acc)
best = np.argmax(avg_acc_list)
return best + 1, avg_acc_list[best]
def proposed_test(op, data, thresh, optic_thresh, proposed_thresh):
size = 15
acc_list = []
for path, img, mask, ground in data:
img = 255 - img[:, :, 1]
line_str = op(path, img, mask, size)
line_str = gray_norm(line_str, mask)
bin = cv2.threshold(line_str, thresh, 255, cv2.THRESH_BINARY)[1]
optic = cached_optic_norm(path, img, mask, size)
optic = cv2.threshold(optic, optic_thresh, 255, cv2.THRESH_BINARY)[1]
optic = cv2.erode(optic, np.ones((3, 3), np.uint8), iterations=1)
bin[optic == 255] = 0
min_window = proposed_norm(path, img, mask, size)
min_window = cv2.threshold(min_window, proposed_thresh, 255,
cv2.THRESH_BINARY)[1]
bin[min_window == 255] = 0
bin_fov = bin[mask == 255]
ground_fov = ground[mask == 255]
acc = accuracy(bin_fov, ground_fov)
acc_list.append(acc)
return np.mean(acc_list)
def line_with_training():
train_data = DriveDatasetLoader('D:/Datasets/DRIVE', 10).load_train()
test_data = DriveDatasetLoader('D:/Datasets/DRIVE', 10).load_test()
# op = cached_single_norm
op = cached_multi_norm
size = 15
timer = Timer()
green('line_with_training')
timer.start('Train')
thresh, train_acc = find_best_acc(op, train_data, size)
train_auc = calc_auc(train_data, op, size)
timer.stop()
timer.start('Test')
test_acc = accuracy(op, test_data, thresh, size)
test_auc = calc_auc(test_data, op, size)
timer.stop()
green(f'Threshold: {thresh}')
green(f'Train average accuracy: {train_acc}')
green(f'Train average AUC: {train_auc}')
green(f'Test average accuracy: {test_acc}')
green(f'Test average AUC: {test_auc}')
def basic_test(op, data, thresh, size):
acc_list = []
for path, img, mask, ground in data:
img = 255 - img[:, :, 1]
line_str = op(path, img, mask, size)
bin = cv2.threshold(line_str, thresh, 255, cv2.THRESH_BINARY)[1]
bin_fov = bin[mask == 255]
ground_fov = ground[mask == 255]
acc = accuracy(bin_fov, ground_fov)
acc_list.append(acc)
return np.mean(acc_list)
def find_best_thresh(line_str, ground, mask):
line_str = gray_norm(line_str, mask)
ground_fov = ground[mask == 255]
acc_list = []
img_list = []
for t in range(1, 255):
bin = cv2.threshold(line_str, t, 255, cv2.THRESH_BINARY)[1]
bin_fov = bin[mask == 255]
acc = accuracy(bin_fov, ground_fov)
acc_list.append(acc)
img_list.append(bin)
best = np.argmax(acc_list)
return best + 1, img_list[best], acc_list[best]
def proposed_train(op, thresh, optic_thresh, data, size):
avg_acc_list = []
for proposed_thresh in range(1, 255):
acc_list = []
for path, img, mask, ground in data:
img = 255 - img[:, :, 1]
line_str = op(path, img, mask, size)
line_str = gray_norm(line_str, mask)
bin = cv2.threshold(line_str, thresh, 255, cv2.THRESH_BINARY)[1]
optic = cached_optic_norm(path, img, mask, size)
optic = cv2.threshold(optic, optic_thresh, 255,
cv2.THRESH_BINARY)[1]
optic = cv2.erode(optic, np.ones((3, 3), np.uint8), iterations=1)
bin[optic == 255] = 0
min_window = proposed_norm(path, img, mask, size)
min_window = cv2.threshold(min_window, proposed_thresh, 255,
cv2.THRESH_BINARY)[1]
bin[min_window == 255] = 0
bin_fov = bin[mask == 255]
ground_fov = ground[mask == 255]
acc = accuracy(bin_fov, ground_fov)
acc_list.append(acc)
# cv2.imshow('Before proposed', bin)
# cv2.imshow('Line', line_str)
# cv2.imshow('Min-window', min_window)
# cv2.imshow('Proposed', bin)
# cv2.waitKey(0)
# cv2.destroyAllWindows()
# return
avg_acc = np.mean(acc_list)
avg_acc_list.append(avg_acc)
best = np.argmax(avg_acc_list)
return best + 1, avg_acc_list[best]
def basic_train(op, data, size):
avg_acc_list = []
for thresh in range(1, 255):
acc_list = []
for path, img, mask, ground in data:
img = 255 - img[:, :, 1]
line_str = op(path, img, mask, size)
bin = cv2.threshold(line_str, thresh, 255, cv2.THRESH_BINARY)[1]
bin_fov = bin[mask == 255]
ground_fov = ground[mask == 255]
acc = accuracy(bin_fov, ground_fov)
acc_list.append(acc)
avg_acc = np.mean(acc_list)
avg_acc_list.append(avg_acc)
best = np.argmax(avg_acc_list)
return best + 1, avg_acc_list[best]