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| import cv2 | |
| import numpy as np | |
| from PIL import Image | |
| import requests | |
| # Difference of Gaussians applied to img input | |
| def dog(img,size=(0,0),k=1.6,sigma=0.5,gamma=1): | |
| img1 = cv2.GaussianBlur(img,size,sigma) | |
| img2 = cv2.GaussianBlur(img,size,sigma*k) | |
| return (img1-gamma*img2) | |
| # Threshold the dog image, with dog(sigma,k) > 0 ? 1(255):0(0) | |
| def edge_dog(img,sigma=0.5,k=200,gamma=0.98): | |
| aux = dog(img,sigma=sigma,k=k,gamma=0.98) | |
| for i in range(0,aux.shape[0]): | |
| for j in range(0,aux.shape[1]): | |
| if(aux[i,j] > 0): | |
| aux[i,j] = 255 | |
| else: | |
| aux[i,j] = 0 | |
| return aux | |
| # garygrossi xdog version | |
| def xdog_garygrossi(img,sigma=0.5,k=200, gamma=0.98,epsilon=0.1,phi=10): | |
| aux = dog(img,sigma=sigma,k=k,gamma=gamma)/255 | |
| for i in range(0,aux.shape[0]): | |
| for j in range(0,aux.shape[1]): | |
| if(aux[i,j] >= epsilon): | |
| aux[i,j] = 1 | |
| else: | |
| ht = np.tanh(phi*(aux[i][j] - epsilon)) | |
| aux[i][j] = 1 + ht | |
| return aux*255 | |
| def hatchBlend(image): | |
| xdogImage = xdog(image,sigma=1,k=200, gamma=0.5,epsilon=-0.5,phi=10) | |
| hatchTexture = cv2.imread('./imgs/hatch.jpg', cv2.CV_LOAD_IMAGE_GRAYSCALE) | |
| hatchTexture = cv2.resize(hatchTexture,(image.shape[1],image.shape[0])) | |
| alpha = 0.120 | |
| return (1-alpha)*xdogImage + alpha*hatchTexture | |
| # version of xdog inspired by article | |
| def xdog(img,sigma=0.5,k=1.6, gamma=1,epsilon=1,phi=1): | |
| aux = dog(img,sigma=sigma,k=k,gamma=gamma)/255 | |
| for i in range(0,aux.shape[0]): | |
| for j in range(0,aux.shape[1]): | |
| if(aux[i,j] < epsilon): | |
| aux[i,j] = 1*255 | |
| else: | |
| aux[i,j] = 255*(1 + np.tanh(phi*(aux[i,j]))) | |
| return aux | |
| def to_sketch(img_orig, sigma=0.3, k=4.5, gamma=0.95, epsilon=-1, phi=10e15, area_min=2): | |
| img_cnts = [] | |
| img = cv2.cvtColor(np.array(img_orig), cv2.COLOR_RGB2GRAY) | |
| img_xdog = xdog(img, sigma=sigma, k=k, gamma=gamma, epsilon=epsilon, phi=phi).astype(np.uint8) | |
| new_img = np.zeros_like(img_xdog) | |
| thresh = cv2.threshold(img_xdog, 0, 255, cv2.THRESH_BINARY_INV | cv2.THRESH_OTSU)[1] | |
| cnts = cv2.findContours(thresh.copy(), cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) | |
| cnts = cnts[0] if len(cnts) == 2 else cnts[1] | |
| for c in cnts: | |
| area = cv2.contourArea(c) | |
| if area > area_min: | |
| img_cnts.append(c) | |
| return Image.fromarray(255 - cv2.drawContours(new_img, img_cnts, -1, (255,255,255), -1)) | |