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import cv2 as cv
import numpy as np
import torch
def image_normalization(img, img_min=0, img_max=255):
"""This is a typical image normalization function
where the minimum and maximum of the image is needed
source: https://en.wikipedia.org/wiki/Normalization_(image_processing)
:param img: an image could be gray scale or color
:param img_min: for default is 0
:param img_max: for default is 255
:return: a normalized image, if max is 255 the dtype is uint8
"""
img = np.float32(img)
epsilon = 1e-12 # whenever an inconsistent image
img = (img-np.min(img))*(img_max-img_min) / \
((np.max(img)-np.min(img))+epsilon)+img_min
return img
# def visualize_result(imgs_list, arg):
# """
# function for tensorflow results
# :param imgs_list: a list of prediction, gt and input data
# :param arg:
# :return: one image with the whole of imgs_list data
# """
# n_imgs = len(imgs_list)
# data_list = []
# for i in range(n_imgs):
# tmp = imgs_list[i]
# if tmp.shape[1] == 3:
# tmp = np.transpose(np.squeeze(tmp), [1, 2, 0])
# tmp = restore_rgb(
# [arg.channel_swap, arg.mean_pixel_values[:3]], tmp)
# tmp = np.uint8(image_normalization(tmp))
# else:
# tmp = np.squeeze(tmp)
# if len(tmp.shape) == 2:
# tmp = np.uint8(image_normalization(tmp))
# tmp = cv.bitwise_not(tmp)
# tmp = cv.cvtColor(tmp, cv.COLOR_GRAY2BGR)
# else:
# tmp = np.uint8(image_normalization(tmp))
# data_list.append(tmp)
# img = data_list[0]
# if n_imgs % 2 == 0:
# imgs = np.zeros((img.shape[0] * 2 + 10, img.shape[1]
# * (n_imgs // 2) + ((n_imgs // 2 - 1) * 5), 3))
# else:
# imgs = np.zeros((img.shape[0] * 2 + 10, img.shape[1]
# * ((1 + n_imgs) // 2) + ((n_imgs // 2) * 5), 3))
# n_imgs += 1
# k = 0
# imgs = np.uint8(imgs)
# i_step = img.shape[0]+10
# j_step = img.shape[1]+5
# for i in range(2):
# for j in range(n_imgs//2):
# if k < len(data_list):
# imgs[i*i_step:i*i_step+img.shape[0], j*j_step:j *
# j_step+img.shape[1], :] = data_list[k]
# k += 1
# else:
# pass
# return imgs
# def cv_imshow(title='image', img=None):
# cv.imshow(title, img)
# cv.waitKey(0)
# cv.destroyAllWindows()
def tensor2edge(tensor):
print(tensor.shape)
tensor =torch.squeeze(tensor) if len(tensor.shape)>2 else tensor
tmp = torch.sigmoid(tensor)
tmp = tmp.cpu().detach().numpy()
# tmp = np.transpose(np.squeeze(tmp[1]), [1, 2, 0])
tmp = np.uint8(image_normalization(tmp))
tmp = cv.bitwise_not(tmp)
tmp = cv.cvtColor(tmp, cv.COLOR_GRAY2BGR)
cv.imshow('test_img', tmp)
cv.waitKey(0)
cv.destroyAllWindows() |