first commit

helper.py

@@ -0,0 +1,214 @@
+import cv2
+from scipy import ndimage
+import numpy as np
+
+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 show_active_img_and_save(name,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)
+    cv2.imshow(name,mat)
+    cv2.imwrite(path,mat)
+    return
+
+def denoise_mat(img,i):
+    return ndimage.median_filter(img, i)
+
+def show_active_img_and_save_denoise(name,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.imshow(name,mat)
+    cv2.imwrite(path,mat)
+    return
+
+def show_active_img_and_save_denoise_filter(name,img,path):
+    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)
+    mat = ndimage.median_filter(mat, 1)
+    cv2.imshow(name,mat)
+    cv2.imwrite(path,mat)
+    return
+
+def show_active_img_and_save_denoise_filter2(name,img,path):
+    mat = img.astype(np.float)
+    mat[mat<0.1] = 0
+    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.imshow(name,mat)
+    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)

main.py

@@ -0,0 +1,40 @@
+from keras.models import load_model
+import cv2
+import numpy as np
+from helper import *
+mod = load_model('mod.h5')
+
+def get(path):
+    from_mat = cv2.imread(path)
+    width = float(from_mat.shape[1])
+    height = float(from_mat.shape[0])
+    new_width = 0
+    new_height = 0
+    if (width > height):
+        from_mat = cv2.resize(from_mat, (512, int(512 / width * height)), interpolation=cv2.INTER_AREA)
+        new_width = 512
+        new_height = int(512 / width * height)
+    else:
+        from_mat = cv2.resize(from_mat, (int(512 / height * width), 512), interpolation=cv2.INTER_AREA)
+        new_width = int(512 / height * width)
+        new_height = 512
+    cv2.imshow('raw', from_mat)
+    cv2.imwrite('raw.jpg',from_mat)
+    from_mat = from_mat.transpose((2, 0, 1))
+    light_map = np.zeros(from_mat.shape, dtype=np.float)
+    for channel in range(3):
+        light_map[channel] = get_light_map_single(from_mat[channel])
+    light_map = normalize_pic(light_map)
+    light_map = resize_img_512_3d(light_map)
+    line_mat = mod.predict(light_map, batch_size=1)
+    line_mat = line_mat.transpose((3, 1, 2, 0))[0]
+    line_mat = line_mat[0:int(new_height), 0:int(new_width), :]
+    show_active_img_and_save('sketchKeras_colored', line_mat, 'sketchKeras_colored.jpg')
+    line_mat = np.amax(line_mat, 2)
+    show_active_img_and_save_denoise_filter2('sketchKeras_enhanced', line_mat, 'sketchKeras_enhanced.jpg')
+    show_active_img_and_save_denoise_filter('sketchKeras_pured', line_mat, 'sketchKeras_pured.jpg')
+    show_active_img_and_save_denoise('sketchKeras', line_mat, 'sketchKeras.jpg')
+    cv2.waitKey(0)
+    return
+
+get('test.jpg')