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一、图像相识度检测
读取图像哈希列表数据 pash计算结构#xff0c;hash距离低于该值的都判定为相似图像
import cv2
import shutil
import numpy as np
import osdef main(hashPath, savePath, pashThre):# 读取图像哈希列表数据hashList np.loa…opencv 传统图像识别检测
一、图像相识度检测
读取图像哈希列表数据 pash计算结构hash距离低于该值的都判定为相似图像
import cv2
import shutil
import numpy as np
import osdef main(hashPath, savePath, pashThre):# 读取图像哈希列表数据hashList np.load(hashPath, allow_pickleTrue).item()# 创建图像结果保存文件夹os.makedirs(savePath, exist_okTrue)# pash计算结构phashStruct cv2.img_hash.PHash_create()while len(hashList):# 取keysnow_keys list(hashList.keys())[0]# 还剩多少图像print(待处理图像{}张.format(len(hashList.keys())))nowKeyValue hashList.pop(now_keys)# 相同图像存储similarFilename []# 循环计算值for keys in hashList:pashValue phashStruct.compare(nowKeyValue, hashList[keys])if pashValue pashThre:similarFilename.append(keys)try:# 移动图像if len(similarFilename) 0:# 获得关键key名字nowKeyFilename os.path.basename(now_keys)# 创建的保存文件路径saveFilePath os.path.join(savePath, nowKeyFilename[:-4])os.makedirs(saveFilePath, exist_okTrue)# 移动关键keys图像# shutil.move(now_keys,os.path.join(saveFilePath,nowKeyFilename))# 从字典中移除值并移动或者复制图像for i in similarFilename:hashList.pop(i)# 获得key名字keyFilename os.path.basename(i)# 复制图像移动图像就把copy改为moveshutil.copy(i, os.path.join(saveFilePath, keyFilename))except:continueif __name__ __main__:# hash文件路径hashPath hash_result.npysavePath similarImg# hash距离低于该值的都判定为相似图像pashThre 5main(hashPath, savePath, pashThre)
读取图像计算hash值
import threading
import queue
import cv2
import os
import numpy as np# 计算图像hash值
def consume(threadName, q, result):while True:fileName, img q.get()# 图像不存在已有结果中就重新计算# 判断图像是否有hash记录可以读取图像函数中执行更高效。# 放在这里主要是担心图像出问题if str(fileName) not in result.keys():phashValue cv2.img_hash.PHash_create().compute(img)result[str(fileName)] phashValueprint({} processing img: {}.format(threadName, fileName))q.task_done()# 读取图像
def produce(threadName, q, imgPath):for i in os.listdir(imgPath):if i.split(.)[-1].lower() in [jpg, png]:fileName os.path.join(imgPath, i)img cv2.imread(fileName)if img is None:continueq.put([fileName, img])print({} reading img: {}.format(threadName, fileName))q.join()def main(imgPath, savePath):# 结果result {}# 读取已有结果加快速度if os.path.exists(savePath):result np.load(savePath, allow_pickleTrue).item()q queue.Queue()# 1个读图线程p threading.Thread(targetproduce, args(producer, q, imgPath))# 4个计算线程c1 threading.Thread(targetconsume, args(consumer1, q, result))c2 threading.Thread(targetconsume, args(consumer2, q, result))c3 threading.Thread(targetconsume, args(consumer3, q, result))c4 threading.Thread(targetconsume, args(consumer4, q, result))c1.setDaemon(True)c2.setDaemon(True)c3.setDaemon(True)c4.setDaemon(True)# 启线程p.start()c1.start()c2.start()c3.start()c4.start()p.join()# 保存结果np.save(savePath, result)if __name__ __main__:# 检测文件夹imgPath C:\py\code\yolov5-flask-master\imgs# 结果保存路径savePath hash_result.npymain(imgPath, savePath)二、黑屏检测
计算偏离128的平均偏差获得亮度系数当亮度系数低于某一阈值的时候就认为他出现黑屏
import cv2
img cv2.imread(rC:\py\code\yolov5-flask-master\imgs\brightness\img1.png)# 把图片转换为单通道的灰度图
gray_img cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# 获取灰度图矩阵的行数和列数
r, c gray_img.shape[:2]
piexs_sum r * c # 整个弧度图的像素个数为r*c
# 获取偏暗的像素(表示0~19的灰度值为暗) 此处阈值可以修改
dark_points (gray_img 20)
target_array gray_img[dark_points]
dark_sum target_array.size
# 判断灰度值为暗的百分比
dark_prop dark_sum / (piexs_sum)
if dark_prop 0.85:print(黑屏了)
else:print(正常了)三、遮挡检测
将帧转换为灰度图像然后使用阈值进行二值化。然后查找二值化图像中的轮廓。如果找到的轮廓数量超过某个阈值就认为有遮挡。
import cv2
import numpy as np# 读取视频
cap cv2.VideoCapture(0)while(cap.isOpened()):# 读取帧ret, frame cap.read()if not ret:break# 转换为灰度图像gray cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)# 使用阈值进行二值化以检测遮挡ret, thresh cv2.threshold(gray, 127, 255, cv2.THRESH_BINARY)# 查找轮廓contours, _ cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)# 如果找到的轮廓数量超过某个阈值则认为有遮挡if len(contours) 300:print(contours:,len(contours))print(遮挡检测到)# 显示帧cv2.imshow(frame, frame)if cv2.waitKey(1) 0xFF ord(q):break
# 释放资源并关闭窗口
cap.release()
cv2.destroyAllWindows()
四、图像过亮和过暗检测
计算偏离128的平均偏差获得亮度系数
import cv2
import matplotlib.pyplot as plt
import numpy as np
import timeimage_path rC:\py\code\yolov5-flask-master\imgs\brightness\img1.pngdef brightness(frame):# 把图片转换为单通道的灰度图gray_img cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)# 获取形状以及长宽img_shape gray_img.shapeheight, width img_shape[0], img_shape[1]size gray_img.size# 灰度图的直方图hist cv2.calcHist([gray_img], [0], None, [256], [0, 256])# 计算灰度图像素点偏离均值(128)程序a 0ma 0# np.full 构造一个数组用指定值填充其元素reduce_matrix np.full((height, width), 128)shift_value gray_img - reduce_matrixshift_sum np.sum(shift_value)da shift_sum / size# 计算偏离128的平均偏差for i in range(256):ma (abs(i - 128 - da) * hist[i])m abs(ma / size)# 亮度系数k abs(da) / m# print(k[0])if k[0] 1:# 过亮if da 0:print(过亮)else:print(过暗)# else:# print(亮度正常)cap cv2.VideoCapture(0)start time.time()while True:# 读取当前帧和下一帧ret1, frame1 cap.read()ret2, frame2 cap.read()# 显示画面cv2.imshow(frame, frame1)brightness(frame1) #过亮过暗检测# 监测键盘输入是否为q为q则退出程序if cv2.waitKey(1) 0xFF ord(q):break
# 释放视频资源
cap.release()
# 关闭所有窗口
cv2.destroyAllWindows()
五、清晰度检测
利用拉普拉斯算子计算图片的二阶导数反映图片的边缘信息同样事物的图片清晰度高的相对应的经过拉普拉斯算子滤波后的图片的方差也就越大。
import cv2image_path rC:\py\code\yolov5-flask-master\imgs\clarity\img3.png
threshold 150
#利用拉普拉斯
def getImageVar(imgPath):image cv2.imread(imgPath)img2gray cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)imageVar cv2.Laplacian(img2gray, cv2.CV_64F).var()return imageVar
imageVar getImageVar(image_path)
# 判断亮度是否低于阈值
if imageVar threshold:print(imageVar,图片不清晰)else:print(imageVar,图片正常)
print(拉普拉斯:,imageVar)六、偏色检测
读取图像将其转换为LAB色彩空间计算a和b分量的均值和方差最后输出图像的色偏值
import cv2
img cv2.imread(rC:\py\code\yolov5-flask-master\imgs\brightness\img1.png)
img cv2.cvtColor(img, cv2.COLOR_BGR2LAB)
l_channel, a_channel, b_channel cv2.split(img)
h,w,_ img.shape
print(ssssssss,a_channel.sum()/(h*w) )
da a_channel.sum()/(h*w)-128
db b_channel.sum()/(h*w)-128
histA [0]*256
histB [0]*256
for i in range(h):for j in range(w):ta a_channel[i][j]tb b_channel[i][j]histA[ta] 1histB[tb] 1
msqA 0
msqB 0
for y in range(256):msqA float(abs(y-128-da))*histA[y]/(w*h)msqB float(abs(y - 128 - db)) * histB[y] / (w * h)
import math
result math.sqrt(da*dadb*db)/math.sqrt(msqA*msqAmsqB*msqB)
print(d/m %s%result)七、对比度检测
使用了帧差法来检测画面中是否有物体在运动。具体来说它每隔一段时间采集一幅图片通过比较前后两幅图片像素变化来判断画面中是否有物体在运动。如果两幅图的差异大于给定的值认为画面中有物体在动反之则认为画面中没有物体在运动
import cv2
import time
import numpy as npcap cv2.VideoCapture(0)_, frame1 cap.read()
img1 cv2.cvtColor(frame1, cv2.COLOR_BGR2GRAY)start time.time()def moving_detect(frame1, frame2):img1 cv2.cvtColor(frame1, cv2.COLOR_BGR2GRAY)img2 cv2.cvtColor(frame2, cv2.COLOR_BGR2GRAY)grey_diff cv2.absdiff(img1, img2)change np.average(grey_diff)if change 10:cv2.putText(frame2, moving, (100, 30), 2, 1, (0,255,0),2,cv2.LINE_AA)else:cv2.putText(frame2, quiet, (100, 30), 2, 1, (255, 0, 0), 2, cv2.LINE_AA)cv2.imshow(output, frame2)while True:end time.time()if (end - start 2):start time.time()_, frame1 cap.read()_, frame2 cap.read()moving_detect(frame1, frame2)if cv2.waitKey(5) 0xFF ord(q):breakcap.release()
cv2.destroyAllWindows()八、冻结检测
import cv2
import numpy as npcap cv2.VideoCapture(0)
ret, frame cap.read()
gray_prev cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
count 0
while True:ret, frame cap.read()cv2.imshow(frame, frame)if not ret:breakcount 1if count % 40 ! 0:continuegray_curr cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)hist_curr cv2.calcHist([gray_curr], [0], None, [256], [0, 256])hist_prev cv2.calcHist([gray_prev], [0], None, [256], [0, 256])similarity cv2.compareHist(hist_curr, hist_prev, cv2.HISTCMP_CORREL)print(f检测视频有无冻结相识度 {count-40} and {count}: {similarity})gray_prev gray_curr# 监测键盘输入是否为q为q则退出程序if cv2.waitKey(1) 0xFF ord(q):break
cap.release()
# 关闭所有窗口
cv2.destroyAllWindows()九、噪点检测
将图像转换为灰度图像计算图像的均值和标准差再计算图像的方差
import cv2image_path rC:\py\code\yolov5-flask-master\imgs\noise\img3.png# 阈值
threshold 4
# 读取图像
img cv2.imread(image_path)# 将图像转换为灰度图像
gray_img cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)# 计算图像的均值和标准差
mean, stddev cv2.meanStdDev(gray_img)
# 计算图像的方差
variance (stddev ** 2)/1000
if(variancethreshold):print(图片噪声过高)
else:print(图片噪声正常)print(Mean: , mean[0][0], Standard deviation: , stddev[0][0])
print(The variance of the image is:, variance[0][0])十、 条纹检测
使用傅里叶变换检测图像中的条纹。它首先将图像转换为灰度图像然后对其进行傅里叶变换。通过将频谱图中心化可以将低频分量移动到图像的中心而高频分量则移动到图像的边缘。通过将高频分量设置为零可以滤除图像中的条纹。最后通过阈值处理和轮廓检测可以确定图像是否存在条纹
import cv2
import numpy as npdef stripeDetection(frame):# img cv2.imread(imgPath)img_hsv cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)H cv2.split(img_hsv)[0]# 傅里叶变换f np.fft.fft2(H)r, c f.shapefshift np.fft.fftshift(f)# 计算频谱图的异常亮点数# f_img 20 * np.log(np.abs(f))magnitude_spectrum 20 * np.log(np.abs(fshift))matrix_mean np.mean(magnitude_spectrum)# 计算阈值matrix_std np.std(magnitude_spectrum)# 最大值matrix_max magnitude_spectrum.max()# 计算阈值(均值加3倍标准差 和 最大值/2 中大的值为阈值)T max(matrix_mean 3 * matrix_std, matrix_max / 2)# 将小于T的变为0# magnitude_spectrum[magnitude_spectrum T] 0# 统计大于T的点数magnitude_points (magnitude_spectrum T)target_array magnitude_spectrum[magnitude_points]magnitude_sum target_array.sizestreak_rate magnitude_sum / (c * r)print(条纹率, streak_rate)if streak_rate 0.004:return 图片条纹else:return 图片正常image_path rC:\py\code\yolov5-flask-master\imgs\img4.pngcap cv2.VideoCapture(0)
# 读取摄像头画面
ret, frame cap.read()while True:ret, frame cap.read()# 判断是否读取到帧if not ret:breakimageVar stripeDetection(frame)print(imageVar)# 显示画面cv2.imshow(frame, frame)# 按下q键退出循环if cv2.waitKey(1) 0xFF ord(q):break十一、视频抖动检测
我们将计算每帧的光流并将结果作为视频抖动的一个指标。通过sim计算图像相识度设定好阈值后也可以完成检测。
import cv2 as cv
import cv2
import numpy as np
import os
#
#
#
path C:/py/code/yolov5-flask-master/imgs/videoJitter/video1.mp4
# threshold 20000;
# # 读取视频
# cap cv2.VideoCapture(path)
#
# # 初始化光流对象
# # optical_flow cv2.createOptFlow_DualTVL1()
# # TVL1 cv2.optflow.DualTVL1OpticalFlow()
# TVL1 cv2.optflow.DualTVL1OpticalFlow_create()
#
#
# # 初始化变量来存储光流的总和
# total_flow 0
#
# # 计数器
# frame_count 0
#
# while (cap.isOpened()):
# ret, frame cap.read()
# if ret True:
# # 将BGR图像转换为灰度图像
# gray cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
#
# # 如果不是第一帧则计算光流
# if frame_count 0:
# flow TVL1.calc(prev_gray, gray, None)
#
# # 计算光流的绝对值的总和
# total_flow np.sum(np.abs(flow))
#
# # 更新前一帧
# prev_gray gray
#
# # 显示当前帧
# cv2.imshow(frame, frame)
#
# # 增加计数器
# frame_count 1
#
# # 按q键退出
# if cv2.waitKey(1) 0xFF ord(q):
# break
# else:
# break
#
# # 计算平均光流
# avg_flow total_flow / frame_count
#
# # 判断是否存在抖动
# if avg_flow threshold:
# print(Video is shaky)
# else:
# print(Video is stable)
#
# # 打印结果
# print(Average Optical Flow: , avg_flow)
#
# # 释放资源并关闭窗口
# cap.release()
# cv2.destroyAllWindows()
#
#
# # videos os.listdir(path)
# # TVL1 cv.optflow.DualTVL1OpticalFlow_create()
# # for video in videos:
# # video_path path / video
# # cap cv.VideoCapture(video_path)
# # ret, frame1 cap.read()
# # prvs cv.cvtColor(frame1, cv.COLOR_BGR2GRAY)
# # hsv np.zeros_like(frame1)
# # hsv[..., 1] 255
# # count 0
# # while (True):
# # ret, frame2 cap.read()
# # if not ret:
# # break
# # next cv.cvtColor(frame2, cv.COLOR_BGR2GRAY)
# #
# # # 返回一个两通道的光流向量实际上是每个点的像素位移值
# # flow TVL1.calc(prvs, next, None)
# #
# # # 打印结果
# # print(Average Optical Flow: , flow)
# #
# # # 笛卡尔坐标转换为极坐标获得极轴和极角
# # mag, ang cv.cartToPolar(flow[..., 0], flow[..., 1])
# #
# # hsv[..., 0] ang * 180 / np.pi / 2 # 角度
# # hsv[..., 2] cv.normalize(mag, None, 0, 255, cv.NORM_MINMAX)
# # bgr cv.cvtColor(hsv, cv.COLOR_HSV2BGR)
# # path_ path / os.path.basename(video).split(.)[0]
# # if not os.path.exists(path_):
# # os.makedirs(path_)
# # # cv.imwrite(path_ /frame{0:06d}.jpg.format(count), bgr)
# # count 1
# # prvs next
# # cap.release()
# # cv.destroyAllWindows()# 读取视频文件
video cv2.VideoCapture(0)# 定义一个函数用于计算两帧之间的相似度
def similarity(frame1, frame2):# 将两帧转换为灰度图gray1 cv2.cvtColor(frame1, cv2.COLOR_BGR2GRAY)gray2 cv2.cvtColor(frame2, cv2.COLOR_BGR2GRAY)# 计算两帧的直方图hist1 cv2.calcHist([gray1], [0], None, [256], [0, 256])hist2 cv2.calcHist([gray2], [0], None, [256], [0, 256])# 使用卡方距离作为相似度的度量dist cv2.compareHist(hist1, hist2, cv2.HISTCMP_CHISQR)return dist# 定义一个阈值用于判断是否发生抖动
threshold 23000# 初始化一个列表用于存储抖动的帧数
shaky_frames []# 初始化一个变量用于记录当前的帧数
frame_count 0# 循环读取视频的每一帧
while True:# 读取当前帧和下一帧ret1, frame1 video.read()ret2, frame2 video.read()# 显示画面cv2.imshow(frame, frame1)# 如果读取成功继续处理if ret1 and ret2:# 计算当前帧和下一帧的相似度sim similarity(frame1, frame2)print(计算当前帧和下一帧的相似度,sim)# 如果相似度大于阈值说明发生了抖动if sim threshold:# 将当前帧的帧数添加到列表中shaky_frames.append(frame_count)print(视频开始抖动, sim)# 更新帧数frame_count 1# 如果读取失败退出循环else:break# 监测键盘输入是否为q为q则退出程序if cv2.waitKey(1) 0xFF ord(q):break# 释放视频资源
video.release()
# 关闭所有窗口
cv2.destroyAllWindows()# 打印抖动的帧数
print(The following frames are shaky:)
print(shaky_frames)
十二、视频卡顿检测
每隔40帧取一帧和前面做对比
def freezeDetection(cap):ret, frame cap.read()gray_prev cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)count 0while True:ret, frame cap.read()# cv2.imshow(frame, frame)if not ret:breakcount 1if count % 40 ! 0:continuegray_curr cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)hist_curr cv2.calcHist([gray_curr], [0], None, [256], [0, 256])hist_prev cv2.calcHist([gray_prev], [0], None, [256], [0, 256])similarity cv2.compareHist(hist_curr, hist_prev, cv2.HISTCMP_CORREL)gray_prev gray_curr
