python检测“无内容”图片

思路1：通过图像熵检测，“无内容”图像熵较小，可通过设置阈值检测“无内容”图像，计算图像熵可参考：https://www.cnblogs.com/niulang/p/12195152.html

思路2：检测图像中连通区域个数和面积

思路2代码：

import cv2

import numpy as np

import math

import time

import os

import shutil

def get_cell_cnt(img_):

    x, y = img_.shape[0:2]

    img_ = cv2.resize(img_, (int(y/4), int(x/4)))

    ret, binary = cv2.threshold(img_, 95, 255, cv2.THRESH_BINARY)

    cv2.imwrite(path + 'abc.png', binary)

    kernel_1 = np.ones((3,3),np.uint8) # 定义核

    opening = cv2.morphologyEx(binary, cv2.MORPH_CLOSE, kernel_1)

    cv2.imwrite(path + 'abc_1.png', opening)

    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

    # 检测图像连通区（输入为二值化图像）

    image, contours, hierarchy = cv2.findContours(opening,1,2) # 检测连通区域

    sign = 0

    for n in range(len(contours)):

        cnt = contours[n]

        area = cv2.contourArea(cnt)

        if area > 12:

            sign = sign + 1

 #   cv2.imwrite(path + 'abc_open.png', opening)

 #   cv2.imwrite(path + 'abc_close_range.png', img_)

    return sign

for path_ in open('list.txt'):

    t1 = time.time()

    path = path_[:-1]

    image = cv2.imread(path,0)

    t2 = time.time()

    cell_cnt = get_cell_cnt(image)

    t3 = time.time()

    if cell_cnt < 2:

        newpath = os.path.join('copy', path.split('/')[1])

        shutil.copy(path, newpath)

    print('time',t2-t1,t3-t1)