一宣决、原理

OpenCV內(nèi)置目標(biāo)檢測(cè)

OpenCV內(nèi)置的目標(biāo)檢測(cè)主要是對(duì)于特定物體（人臉）或者運(yùn)動(dòng)物體進(jìn)行檢測(cè)掩幢，本例使用OpenCV中的MOG2算法對(duì)運(yùn)動(dòng)的目標(biāo)進(jìn)行檢測(cè)绑嘹，檢測(cè)后使用腐蝕唧席、膨脹進(jìn)行處理钾军，得到較合理的初步目標(biāo)檢測(cè)區(qū)域娄涩。

OpenCV內(nèi)置目標(biāo)跟蹤

使用OpenCV內(nèi)置的MIL跟蹤器或者DaSiamRPN跟蹤器窗怒，使用其對(duì)于已經(jīng)檢測(cè)到的目標(biāo)進(jìn)行跟蹤，以達(dá)到增強(qiáng)軟件檢測(cè)結(jié)果穩(wěn)定性的目的蓄拣。

軟件設(shè)計(jì)目標(biāo)

通過(guò)結(jié)合OpenCV內(nèi)置的兩種算法扬虚，達(dá)到較為穩(wěn)定的目標(biāo)檢測(cè)目的。

二球恤、軟件設(shè)計(jì)

軟件主要設(shè)計(jì)如圖所示辜昵，表征了軟件的循環(huán)結(jié)構(gòu)

循環(huán)中每次調(diào)用類方法(process_one_frame)

需要強(qiáng)調(diào)的是，本例完成匆忙僅采用了簡(jiǎn)單的平均值濾波器咽斧，但是本例非常適合采用卡爾曼濾波器對(duì)于檢測(cè)和跟蹤的結(jié)果進(jìn)行預(yù)測(cè)堪置。

三、代碼

將上述軟件設(shè)計(jì)封裝在類中

import copy

import cv2
import numpy as np

class ImageProcessorCloseLoop:
    def __init__(self, subtractor_type="MOG2", tracker_type="DaSiamRPN"):
        self.sub = self.create_subtractor(subtractor_type)
        self.tracker_type = tracker_type
        self.trackers = list()

    def create_subtractor(self, subtractor_type):
        if subtractor_type == "MOG2":
            return cv2.createBackgroundSubtractorMOG2()
        elif subtractor_type == "KNN":
            return cv2.createBackgroundSubtractorKNN()

    def create_tracker(self, tracker_type):
        if tracker_type == "MIL":
            return cv2.TrackerMIL_create()
        elif tracker_type == "GOTURN":
            return cv2.TrackerGOTURN_create()
        elif tracker_type == "DaSiamRPN":
            return cv2.TrackerDaSiamRPN_create()

    def process_one_frame(self, frame):
        '''
        :param frame: original video frame image (np.ndarray)
        :return: bboxes (tuple of bbox)
        '''
        detect_result = self.simple_detect(frame)
        track_result = list()
        for tracker in self.trackers:
            ok, bbox = tracker.update(frame)
            if ok:
                track_result.append(bbox)
            else:
                self.trackers.remove(tracker)
        matched_detect, matched_track, only_detect, only_track = self.compare_result(detect_result, track_result)

        res_matched = list()
        for i in range(len(matched_track)):
            bb1 = matched_detect[I]
            bb2 = matched_track[I]
            bb_res = self.bb_filter2(bb1, bb2)
            res_matched.append(bb_res)

        res_track = only_track
        res_detect = only_detect

        for res in res_detect:
            tracker = self.create_tracker(tracker_type=self.tracker_type)
            # tracker.init(frame, res)

        res = res_matched + res_track + res_detect
        return res

    def simple_detect(self, img: np.ndarray):
        mask = self.sub.apply(img)
        thresh, bw = cv2.threshold(mask, 120, 255, cv2.THRESH_OTSU)
        kernel = np.ones((3, 3), dtype=int)
        dilated = cv2.erode(bw, kernel, iterations=1)
        inflated = cv2.dilate(dilated, kernel, iterations=1)
        # find contour:
        cnts, hier = cv2.findContours(inflated, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
        bboxes = list()
        for i in range(len(cnts), 0, -1):
            c = cnts[i - 1]
            area = cv2.contourArea(c)
            if area < 10:
                continue
            bbox = cv2.boundingRect(c)
            x, y, w, h = bbox
            diameter = w if w > h else h
            if diameter <= 15:
                continue
            bboxes.append(bbox)
        return bboxes

    def compare_two(self, bb1, bb2):
        '''
        compare two bbox
        :param bb1: bbox tuple (x, y, w, h)
        :param bb2: bbox tuple
        :return: True or False
        '''
        x1, y1, w1, h1 = bb1
        x2, y2, w2, h2 = bb2
        IOU1 = (min(x1 + w1, x2 + w2) - max(x1, x2)) / (max(x1 + w1, x2 + w2) - min(x1, x2))
        IOU2 = (min(y1 + h1, y2 + h2) - max(y1, y2)) / (max(y1 + h1, y2 + h2) - min(x1, x2))
        IOU = IOU2 * IOU1
        if IOU > 0.9:
            return True
        else:
            return False

    def compare_result(self, detect_res, track_res):
        '''
        compare the detect results by comparing IOU of the bboxes
        :param last_res: list of bboxes
        :param this_res: list of bboxes
        :return: tuple (matched, new, only_last)
        '''
        matched_detect = list()
        matched_track = list()
        track = list()
        detect = list()

        for track_box in track_res:
            for detect_box in detect_res:
                is_matched = self.compare_two(track_box, detect_box)
                if is_matched:
                    matched_track.append(track_box)
                    matched_detect.append(detect_box)
                    detect_res.remove(detect_box)
                else:
                    track.append(track_box)

        for detect_box in detect_res:
            detect.append(detect_box)
        return matched_detect, matched_track, detect, track

    def bb_filter2(self, bbox1, bbox2, method="mean"):
        '''
        :param bbox1:bbox (x, y, w, h)
        :param bbox2:bbox
        :return: bbox
        '''
        if method == "mean":
            return (bbox1[0] + bbox2[0])/2, (bbox1[1] + bbox2[1])/2, (bbox1[2] + bbox2[2])/2, (bbox1[3] + bbox2[3])/2

實(shí)例化類和視頻讀取循環(huán)寫(xiě)在main.py中

main.py
import cv2
import processor

if __name__=="__main__":
    cap = cv2.VideoCapture("/Users/wanglingyu/sources/video1.mp4")

    proc = processor.ImageProcessorCloseLoop(tracker_type="MIL")

    ret, frame = cap.read()
    if not ret:
        exit(1)

    while True:
        ret, frame = cap.read()
        if not ret:
            break

        bboxes = proc.process_one_frame(frame)
        for bbox in bboxes:
            x, y, w, h = bbox
            cv2.rectangle(frame, (x, y), (x+w, y+h), (0, 0, 0xff), 1)

        cv2.imshow("Video", frame)
        key = cv2.waitKey(10)
        if key == ord(" "):
            k1 = cv2.waitKey()


    cap.release()
    cv2.destroyAllWindows()

四张惹、效果

能夠穩(wěn)定地對(duì)于目標(biāo)進(jìn)行檢測(cè)舀锨，且檢測(cè)框不易丟失。

目標(biāo)檢測(cè)效果

五宛逗、環(huán)境配置

本例代碼運(yùn)行于macOS 11.4 Big Sur上坎匿，該系統(tǒng)運(yùn)行于arm平臺(tái)的M1芯片上，其中OpenCV若需要編譯則需要在配置cmake時(shí)配置好python2或python3的各個(gè)選項(xiàng)雷激。如果使用PyCharm軟件則在其內(nèi)置的軟件倉(cāng)庫(kù)中直接安裝即可使用替蔬，兼容性沒(méi)有問(wèn)題。