| import cv2 |
| import numpy as np |
|
|
| def read_img(input_path) : |
| img = cv2.cvtColor(cv2.imread(input_path), cv2.COLOR_BGR2RGB) |
| return img |
|
|
| def edge_detection(img, line_wdt, blur): |
| gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY) |
| grayBlur = cv2.medianBlur(gray,blur) |
| edges = cv2.adaptiveThreshold(grayBlur, 255,cv2.ADAPTIVE_THRESH_MEAN_C,cv2.THRESH_BINARY,line_wdt,blur) |
| return edges |
|
|
| def color_quantisation(img, k): |
| data = np.float32(img).reshape((-1,3)) |
| criteria = (cv2.TermCriteria_EPS + cv2.TERM_CRITERIA_MAX_ITER,20,0.001) |
| ret, label, center = cv2.kmeans(data,k,None,criteria,10,cv2.KMEANS_RANDOM_CENTERS) |
| center = np.uint8(center) |
| result = center[label.flatten()] |
| result=result.reshape(img.shape) |
| return result |
