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這篇文章主要為大家展示了python如何使用遞歸的方式實現(xiàn)語義圖片分割,內(nèi)容簡而易懂,下面讓小編帶大家一起學習一下吧。
實現(xiàn)效果
第一張圖為原圖,其余的圖為分割后的圖形
代碼實現(xiàn):
# -*-coding:utf-8-*- import numpy as np import cv2 #---------------------------------------------------------------------- def obj_clip(img, foreground, border): result = [] height ,width = np.shape(img) visited = set() for h in range(height): for w in range(width): if img[h,w] == foreground and not (h,w) in visited: obj = visit(img, height, width, h, w, visited, foreground, border) result.append(obj) return result #---------------------------------------------------------------------- def visit(img, height, width, h, w, visited, foreground, border): visited.add((h,w)) result = [(h,w)] if w > 0 and not (h, w-1) in visited: if img[h, w-1] == foreground: result += visit(img, height, width, h, w-1, visited , foreground, border) elif border is not None and img[h, w-1] == border: result.append((h, w-1)) if w < width-1 and not (h, w+1) in visited: if img[h, w+1] == foreground: result += visit(img, height, width, h, w+1, visited, foreground, border) elif border is not None and img[h, w+1] == border: result.append((h, w+1)) if h > 0 and not (h-1, w) in visited: if img[h-1, w] == foreground: result += visit(img, height, width, h-1, w, visited, foreground, border) elif border is not None and img[h-1, w] == border: result.append((h-1, w)) if h < height-1 and not (h+1, w) in visited: if img[h+1, w] == foreground : result += visit(img, height, width, h+1, w, visited, foreground, border) elif border is not None and img[h+1, w] == border: result.append((h+1, w)) return result #---------------------------------------------------------------------- if __name__ == "__main__": import cv2 import sys sys.setrecursionlimit(100000) img = np.zeros([400,400]) cv2.rectangle(img, (10,10), (150,150), 1.0, 5) cv2.circle(img, (270,270), 70, 1.0, 5) cv2.line(img, (100,10), (100,150), 0.5, 5) #cv2.putText(img, "Martin",(200,200), 1.0, 5) cv2.imshow("img", img*255) cv2.waitKey(0) for obj in obj_clip(img, 1.0, 0.5): clip = np.zeros([400, 400]) for h, w in obj: clip[h, w] = 0.2 cv2.imshow("aa", clip*255) cv2.waitKey(0)
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