Croquis à partir de dessins colorisés

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)

from keras.models import load_model
import cv2
import numpy as np
from matplotlib import pyplot as plt
from helper import *
 
mod = load_model('mod.h5')
 
def get(path):
    from_mat = cv2.imread(path)
 
    plt.imshow(cv2.cvtColor(from_mat.astype('uint8'), cv2.COLOR_BGR2RGB))
    plt.axis('off')
    plt.title('Originale')
    plt.show()
 
    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
 
    plt.imshow(cv2.cvtColor(from_mat.astype('uint8'), cv2.COLOR_BGR2RGB))
    plt.axis('off')
    plt.title('raw')
    plt.show()
 
    cv2.imwrite('test/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, 'test/sketchKeras_colored.jpg')
    line_mat = np.amax(line_mat, 2)
    show_active_img_and_save_denoise_filter2('sketchKeras_enhanced', line_mat, 'test/sketchKeras_enhanced.jpg')
    show_active_img_and_save_denoise_filter('sketchKeras_pured', line_mat, 'test/sketchKeras_pured.jpg')
    show_active_img_and_save_denoise('sketchKeras', line_mat, 'test/sketchKeras.jpg')
    return
 
get('test.jpg')