Python cv2 模块，HOGDescriptor_getDefaultPeopleDetector() 实例源码

def HogDescriptor(self,image):

        hog = cv2.HOGDescriptor()
        hog.setSVMDetector(cv2.HOGDescriptor_getDefaultPeopleDetector())
        (rects, weights) = hog.detectMultiScale(image, winStride=(5,5),padding=(16,16), scale=1.05, useMeanshiftGrouping=False)
        return rects

def find_people(self, img):
        '''
        Detect people in image
        :param img: numpy.ndarray
        :return: count of rectangles after non-maxima suppression, corresponding to number of people detected in picture
        '''
        t = time.time()
        # HOG descriptor/person detector
        hog = cv2.HOGDescriptor()
        hog.setSVMDetector(cv2.HOGDescriptor_getDefaultPeopleDetector())
        # Chooses whichever size is less
        image = imutils.resize(img, width=min(self.MIN_IMAGE_WIDTH, img.shape[1]))
        # detect people in the image
        (rects, wghts) = hog.detectMultiScale(image, winStride=self.WIN_STRIDE,
                                              padding=self.PADDING, scale=self.SCALE)
        # apply non-maxima suppression to the bounding boxes but use a fairly large overlap threshold,
        # to try to maintain overlapping boxes that are separate people
        rects = np.array([[x, y, x + w, y + h] for (x, y, w, h) in rects])
        pick = non_max_suppression(rects, probs=None, overlapThresh=self.OVERLAP_THRESHOLD)

        print("Elapsed time: {} seconds".format(int((time.time() - t) * 100) / 100.0))

        if self.SHOW_IMAGES:
            # draw the final bounding boxes
            for (xA, yA, xB, yB) in pick:
                # Tighten the rectangle around each person by a small margin
                shrinkW, shrinkH = int(0.05 * xB), int(0.15*yB)
                cv2.rectangle(image, (xA+shrinkW, yA+shrinkH), (xB-shrinkW, yB-shrinkH), self.BOX_COLOR, 2)

            cv2.imshow("People detection", image)
            cv2.waitKey(self.IMAGE_WAIT_TIME)
            cv2.destroyAllWindows()

        return len(pick)

def find_people(self, img):
        '''
        Detect people in image
        :param img: numpy.ndarray
        :return: count of rectangles after non-maxima suppression, corresponding to number of people detected in picture
        '''
        t = time.time()
        hog = cv2.HOGDescriptor()
        hog.setSVMDetector(cv2.HOGDescriptor_getDefaultPeopleDetector())
        # Chooses whichever size is less
        image = imutils.resize(img, width=min(self.MIN_IMAGE_WIDTH, img.shape[1]))
        # detect people in the image
        (rects, wghts) = hog.detectMultiScale(image, winStride=self.WIN_STRIDE,
                                              padding=self.PADDING, scale=self.SCALE)
        # apply non-maxima suppression to the bounding boxes using a
        # fairly large overlap threshold to try to maintain overlapping boxes that are still people
        rects = np.array([[x, y, x + w, y + h] for (x, y, w, h) in rects])
        pick = non_max_suppression(rects, probs=None, overlapThresh=self.OVERLAP_THRESHOLD)

        print("Elapsed time of detection: {} seconds".format(int((time.time() - t) * 100) / 100.0))

        if self.SHOW_IMAGES:
            # draw the final bounding boxes
            for (xA, yA, xB, yB) in pick:
                # Tighten the rectangle around each person by a small margin
                cv2.rectangle(image, (xA+5, yA+5), (xB-5, yB-10), self.BOX_COLOR, 2)

            cv2.imshow("People detection", image)
            cv2.waitKey(self.IMAGE_WAIT_TIME)
            cv2.destroyAllWindows()

        return len(pick)

def detect():
    move=0
    hog = cv2.HOGDescriptor()
    hog.setSVMDetector(cv2.HOGDescriptor_getDefaultPeopleDetector())
    cap=cv2.VideoCapture(0)
    while(1):
        ret, img=cap.read()
        gray=cv2. cvtColor(img, cv2.COLOR_BGR2GRAY)
        image = imutils.resize(img, width=min(400, img.shape[1]))
        (rects, weights) = hog.detectMultiScale(image, winStride=(4, 4),padding=(8, 8), scale=1.05)
        for (x, y, w, h) in rects:
            cv2.rectangle(image, (x, y), (x + w, y + h), (0, 0, 255), 2)
            rects = np.array([[x, y, x + w, y + h] for (x, y, w, h) in rects])
            pick = non_max_suppression(rects, probs=None, overlapThresh=0.65)
            for (xA, yA, xB, yB) in pick:
                cv2.rectangle(image, (xA, yA), (xB, yB), (0, 255, 0), 2)
            if (xA/480)>0.5 :
                print("move to right")
                move=4

            elif (yA/640)>0.5:
                print('move to down')
                move=3
            elif (xB/480)<0.3:
                print('move to left')
                move=2
            elif (yB/640)<0.3:
                print('move to up')
                move=1
            else:
                print('do nothing')
                move=0
            mqt.pass_message(move)
            #eyes = eye_cascade.detectMultiScale(roi_gray)

            #for (ex,ey,ew,eh) in eyes:
            #   cv2.rectangle(roi_color,(ex,ey),(ex+ew,ey+eh),(0,255,0),2)
        cv2.imshow('img',image)
        k=cv2.waitKey(1)& 0xff
        if k==27:
            break
        elif (k==ord('w')):
            mqt.pass_message(1)
        elif (k==ord('s')):
            mqt.pass_message(3)

    cap.release()
    cv2.destroyAllWindows()