python opencv實現檢測物體聚集區域

內容涉及：二值影象轉換 / 檢測連通區域面積 / 在原圖上畫框等

import cv2

import numpy as np
for n in open('list.txt'): # list.txt為目標檔案列表
path = n[:-1] # 去除檔案路徑的換行符
img = cv2.imread(path)
gray =cv2.cvtcolor(img, cv2.color_bgr2gray) # 影象轉灰度
ret, binary = cv2.threshold(gray, 75, 255, cv2.thresh_binary) # 灰度轉二值影象
cv2.imwrite(path + 'abc.png', binary)
kernel = np.ones((21,21),np.uint8) # 給影象閉運算定義核
kernel_1 = np.ones((101,101),np.uint8) # 給影象開運算定義核
# 影象先閉運算再開運算可以過濾孤立的物體， 將密集物體區域形成一片連通區
closing = cv2.morphologyex(binary, cv2.morph_close, kernel)
opening = cv2.morphologyex(closing, cv2.morph_open, kernel_1)
# 給影象的邊緣畫素設定為255，否則下面連通區的檢測無法識別貼在影象邊緣的連通區
# 特別注意！！！，此操作會將整個影象也視為乙個連通區域
opening_x = opening.shape[0]
opening_y = opening.shape[1]
opening[:,0] = 255
opening[:,opening_y-1] = 255
opening[0,:] = 255
opening[opening_x-1,:] = 255
# 檢測影象連通區（輸入為二值化影象）
image, contours, hierarchy = cv2.findcontours(opening,1,2)
for n in range(len(contours)):
# 篩選面積較大的連通區，閾值為20000
cnt = contours[n]
area = cv2.contourarea(cnt)
if area > 20000:
x,y,w,h=cv2.boundingrect(cnt)
img_ = cv2.rectangle(img ,(x,y),(x+w,y+h),(0,0,255),4) # 畫框
print('')
img__ = img[y-h:y+h,x-w:x+w,:]
cv2.imwrite(path + 'abc_open.png', opening)
cv2.imwrite(path + 'abc_close.png', closing)
cv2.imwrite(path + 'abc_close_range.png', img_)

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