Answer4.py-20170920

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# -*- coding: utf-8 -*-
import cv2
import csv
import statistics as sts
import numpy as np
from PIL import  Image

#设计一个类，进行异常值检测
class Detect:
    def __init__(self):
        self.path_read = ''
        self.path_write = ''
        self.filename = ''
        self.dynamic_dataset = []
        self.dynamic_volume = 1000
        self.save_style = 'Orig'#'New'
        self.threshold = 0
        self.safe = 'y'
        self.video_type = 'avi'
        self.process_type = 'KNN'

    def setup(self,path_read,filename,path_write,video_type='avi',process_type='KNN',save_style='Orig',dynamic_volume=1000):
        self.path_read = path_read
        self.path_write = path_write
        self.filename = filename
        self.video_type = video_type
        self.process_type = process_type
        self.save_style = save_style
        self.dynamic_volume = dynamic_volume

    def outlier(self,observ_data):
        if len(self.dynamic_dataset) > 20:
            Upper = max(sts.quantile(self.dynamic_dataset, 0.9),0.0)
            Lower = max(sts.quantile(self.dynamic_dataset, 0.1),0.0)
            Index1 = []
            for data in self.dynamic_dataset:
                if data <= Upper and data >= Lower:
                    Index1.append(0)
                else:
                    Index1.append(1)
            # 数据集中的异常值检测——拉依达法则
            DATA = []
            Count = 0
            for ind in Index1:
                if ind == 0:
                    DATA.append(self.dynamic_dataset[Count])
                Count += 1
            MEAN_ALL = np.mean(DATA)
            Resid = []
            Resid_Square = []
            N = 0
            N1 = 0
            for data in self.dynamic_dataset:
                Resid.append(np.abs(data - MEAN_ALL))
                if Index1[N] == 0:
                    Resid_Square.append((data - MEAN_ALL) * (data - MEAN_ALL))
                    N1 += 1
                N += 1
            SUM = np.sum(Resid_Square)
            S = np.sqrt(SUM / (N1 - 1))
            ##注意，这是本程序中，唯一在程序内部进行调整的参数，可以使用3*S,S/4
            if np.abs(observ_data-MEAN_ALL) >  S/2 :
                #print(3 * S, observ_data, '---------------------')
                self.safe = 'n'
            else:
                self.safe = 'y'
            #判定是否将数据添加进来
            Length_Dataset = len(self.dynamic_dataset)
            if Length_Dataset < self.dynamic_volume:
                self.dynamic_dataset.append(observ_data)
            else:
                self.dynamic_dataset.append(observ_data)
                del self.dynamic_dataset[0]
        else:
            self.dynamic_dataset.append(observ_data)

    def MatrixToImage(self,data):
        data = data
        new_im = Image.fromarray(data.astype(np.uint8))
        return new_im

    def video_process(self):
        Filename_Read = self.path_read + '\' + self.filename + '.' + self.video_type
        cap = cv2.VideoCapture(Filename_Read)
        # 创建一个5*5的椭圆核
        kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5, 5))
        # 创建BackgroundSubtractorMOG2
        if self.process_type == 'KNN':
            fgbg = cv2.createBackgroundSubtractorKNN()
        else:
            fgbg = cv2.createBackgroundSubtractorMOG2()

        # Define the codec and create VideoWriter object
        Filename_Write = self.path_write + '\' + self.filename + '.avi'
        print(Filename_Write)
        fps = cap.get(cv2.CAP_PROP_FPS)
        size = (int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)), int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)))
        out = cv2.VideoWriter(Filename_Write, cv2.VideoWriter_fourcc('I','4','2','0'), fps, size)
        Filename_Write_csv = self.path_write + '\' + self.filename + '.csv'
        zh_count_write = csv.writer(open(Filename_Write_csv, 'w', newline=''), dialect='excel')
        Process_Order = 0
        ZH_Count = 1
        #处理视频
        while (1):
            ret, frame = cap.read(0)
            if ret == True:
                img_new = frame
                fgmask = fgbg.apply(img_new)
                # 形态学开运算去噪点
                fgmask = cv2.morphologyEx(fgmask, cv2.MORPH_OPEN, kernel)
                # 平滑方式去除噪声,
                blured1 = cv2.bilateralFilter(fgmask, 10, 5, 5)
                blured2 = cv2.GaussianBlur(fgmask, (3, 3), 0)
                blured3 = cv2.blur(fgmask, (5, 5))
                blured4 = cv2.medianBlur(fgmask, 5)
                fgmask = blured2
                # 闭运算去噪点
                fgmask = cv2.morphologyEx(fgmask, cv2.MORPH_CLOSE, kernel)
                # 寻找视频中的轮廓
                im, contours, hierarchy = cv2.findContours(fgmask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
                Count_Contour = 0
                Whether_Write = False
                for c in contours:
                    # 计算各轮廓的周长
                    perimeter = cv2.arcLength(c, True)
                    Count_Contour += 1
                    self.outlier(perimeter)
                    if self.safe == 'n':
                        # 找到一个直矩形（不会旋转）
                        x, y, w, h = cv2.boundingRect(c)
                        # 画出这个矩形
                        # cv2.rectangle(frame,(x,y),(x+w,y+h),(0,0,255),-1)#作出填充的矩形
                        cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 0, 255), 1)  # 做矩形框
                        Whether_Write = True
                #截取图片保存
                if Whether_Write == True:
                    Save_Filename = self.path_write + '\截图\' + self.filename + '\' + str(ZH_Count) + '.jpg'
                    Img = self.MatrixToImage(frame)
                    Img.save(Save_Filename)
                    zh_count_write.writerow([ZH_Count])
                print(Count_Contour)
                if self.save_style == 'Orig':
                    out.write(frame)
                else:
                    out.write(fgmask)
                    # 保存截图
                cv2.imshow('frame', frame)
                cv2.imshow('fgmask', fgmask)
                Process_Order += 1
            else:
                break

            ZH_Count += 1
            if cv2.waitKey(25) & 0xFF == ord('q'):
                break
        cap.release()
        cv2.destroyAllWindows()

#-------------------------------------类的调用-----------------------------
ANS = Detect()
ANS.setup(path_read='/Users/vicky/Desktop/附件3-检测前景视频/campus',path_write='/Users/vicky/Desktop/附件3-检测前景视频/campus',filename='Campus',video_type='avi',process_type='MOG2',save_style='Orig',
          dynamic_volume=10000)
ANS.video_process()