opencv 传统图像识别检测

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 os

def main(hashPath, savePath, pashThre):
    # 读取图像哈希列表数据
    hashList = np.load(hashPath, allow_pickle=True).item()
    # 创建图像结果保存文件夹
    os.makedirs(savePath, exist_ok=True)
    # pash计算结构
    phashStruct = cv2.img_hash.PHash_create()
    while len(hashList):
        # 取keys
        now_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_ok=True)
                # 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move
                    shutil.copy(i, os.path.join(saveFilePath, keyFilename))
        except:
            continue


if __name__ == '__main__':
    # hash文件路径
    hashPath = "hash_result.npy"
    savePath = "similarImg"
    # hash距离低于该值的都判定为相似图像
    pashThre = 5
    main(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)] = phashValue
            print('{} 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:
                continue
            q.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_pickle=True).item()

    q = queue.Queue()

    # 1个读图线程
    p = threading.Thread(target=produce, args=("producer", q, imgPath))
    # 4个计算线程
    c1 = threading.Thread(target=consume, args=("consumer1", q, result))
    c2 = threading.Thread(target=consume, args=("consumer2", q, result))
    c3 = threading.Thread(target=consume, args=("consumer3", q, result))
    c4 = threading.Thread(target=consume, 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.npy"
    main(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(r'C:\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 time

image_path = r'C:\py\code\yolov5-flask-master\imgs\brightness\img1.png'

def 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.shape
    height, 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 = 0
    ma = 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_matrix
    shift_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 cv2


image_path = r'C:\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(r'C:\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] += 1
        histB[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*da+db*db)/math.sqrt(msqA*msqA+msqB*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 np


cap = 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'):
        break

cap.release()
cv2.destroyAllWindows()

八、冻结检测

import cv2
import numpy as np

cap = 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:
        break
    count += 1
    if count % 40 != 0:
        continue
    gray_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 cv2

image_path = r'C:\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(variance>threshold):
    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 np

def 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.shape
    fshift = 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.size
    streak_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 = r'C:\py\code\yolov5-flask-master\imgs\img4.png'

cap = 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:
        break
    imageVar = 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

1. 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。
2. 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 = 0
    while True:
        ret, frame = cap.read()
        # cv2.imshow('frame', frame)
        if not ret:
            break
        count += 1
        if count % 40 != 0:
            continue
        gray_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