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function.py
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function.py
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import numpy as np
import cv2
from scipy import ndimage
def get_normal_map(img):
img = img.astype(np.float)
img = img / 255.0
img = - img + 1
img[img < 0] = 0
img[img > 1] = 1
return img
def get_gray_map(img):
gray = cv2.cvtColor(img.astype(np.uint8), cv2.COLOR_BGR2GRAY)
highPass = gray.astype(np.float)
highPass = highPass / 255.0
highPass = 1 - highPass
highPass = highPass[None]
return highPass.transpose((1, 2, 0))
def get_light_map(img):
gray = cv2.cvtColor(img.astype(np.uint8), cv2.COLOR_BGR2GRAY)
blur = cv2.GaussianBlur(gray, (0, 0), 3)
highPass = gray.astype(int) - blur.astype(int)
highPass = highPass.astype(np.float)
highPass = highPass / 128.0
highPass = highPass[None]
return highPass.transpose((1, 2, 0))
def get_light_map_single(img):
gray = img
gray = gray[None]
gray = gray.transpose((1, 2, 0))
blur = cv2.GaussianBlur(gray, (0, 0), 3)
gray = gray.reshape((gray.shape[0], gray.shape[1]))
highPass = gray.astype(int) - blur.astype(int)
highPass = highPass.astype(np.float)
highPass = highPass / 128.0
return highPass
def get_light_map_drawer(img):
gray = cv2.cvtColor(img.astype(np.uint8), cv2.COLOR_BGR2GRAY)
blur = cv2.GaussianBlur(gray, (0, 0), 3)
highPass = gray.astype(int) - blur.astype(int) + 255
highPass[highPass < 0] = 0
highPass[highPass > 255] = 255
highPass = highPass.astype(np.float)
highPass = highPass / 255.0
highPass = 1 - highPass
highPass = highPass[None]
return highPass.transpose((1, 2, 0))
def get_light_map_drawer2(img):
ret = img.copy()
ret = ret.astype(np.float)
ret[:, :, 0] = get_light_map_drawer3(img[:, :, 0])
ret[:, :, 1] = get_light_map_drawer3(img[:, :, 1])
ret[:, :, 2] = get_light_map_drawer3(img[:, :, 2])
ret = np.amax(ret, 2)
return ret
def get_light_map_drawer3(img):
gray = img
blur = cv2.blur(gray, ksize=(5, 5))
highPass = gray.astype(int) - blur.astype(int) + 255
highPass[highPass < 0] = 0
highPass[highPass > 255] = 255
highPass = highPass.astype(np.float)
highPass = highPass / 255.0
highPass = 1 - highPass
return highPass
def normalize_pic(img):
img = img / np.max(img)
return img
def superlize_pic(img):
img = img * 2.33333
img[img > 1] = 1
return img
def mask_pic(img, mask):
mask_mat = mask
mask_mat = mask_mat.astype(np.float)
mask_mat = cv2.GaussianBlur(mask_mat, (0, 0), 1)
mask_mat = mask_mat / np.max(mask_mat)
mask_mat = mask_mat * 255
mask_mat[mask_mat < 255] = 0
mask_mat = mask_mat.astype(np.uint8)
mask_mat = cv2.GaussianBlur(mask_mat, (0, 0), 3)
mask_mat = get_gray_map(mask_mat)
mask_mat = normalize_pic(mask_mat)
mask_mat = resize_img_512(mask_mat)
super_from = np.multiply(img, mask_mat)
return super_from
def resize_img_512(img):
zeros = np.zeros((512, 512, img.shape[2]), dtype=np.float)
zeros[:img.shape[0], :img.shape[1]] = img
return zeros
def resize_img_512_3d(img):
zeros = np.zeros((1, 3, 512, 512), dtype=np.float)
zeros[0, 0: img.shape[0], 0: img.shape[1], 0: img.shape[2]] = img
return zeros.transpose((1, 2, 3, 0))
def denoise_mat(img, i):
return ndimage.median_filter(img, i)
def show_active_img_and_save_denoise(img, path):
mat = img.astype(np.float)
mat = - mat + 1
mat = mat * 255.0
mat[mat < 0] = 0
mat[mat > 255] = 255
mat = mat.astype(np.uint8)
mat = ndimage.median_filter(mat, 1)
cv2.imwrite(path, mat)
return
def show_active_img(name, img):
mat = img.astype(np.float)
mat = - mat + 1
mat = mat * 255.0
mat[mat < 0] = 0
mat[mat > 255] = 255
mat = mat.astype(np.uint8)
cv2.imshow(name, mat)
return
def get_active_img(img):
mat = img.astype(np.float)
mat = - mat + 1
mat = mat * 255.0
mat[mat < 0] = 0
mat[mat > 255] = 255
mat = mat.astype(np.uint8)
return mat
def get_active_img_fil(img):
mat = img.astype(np.float)
mat[mat < 0.18] = 0
mat = - mat + 1
mat = mat * 255.0
mat[mat < 0] = 0
mat[mat > 255] = 255
mat = mat.astype(np.uint8)
return mat
def show_double_active_img(name, img):
mat = img.astype(np.float)
mat = mat * 128.0
mat = mat + 127.0
mat[mat < 0] = 0
mat[mat > 255] = 255
cv2.imshow(name, mat.astype(np.uint8))
return
def debug_pic_helper():
for index in range(1130):
gray_path = 'data\\gray\\' + str(index) + '.jpg'
color_path = 'data\\color\\' + str(index) + '.jpg'
mat_color = cv2.imread(color_path)
mat_color = get_light_map(mat_color)
mat_color = normalize_pic(mat_color)
mat_color = resize_img_512(mat_color)
show_double_active_img('mat_color', mat_color)
mat_gray = cv2.imread(gray_path)
mat_gray = get_gray_map(mat_gray)
mat_gray = normalize_pic(mat_gray)
mat_gray = resize_img_512(mat_gray)
show_active_img('mat_gray', mat_gray)
cv2.waitKey(1000)