detect_from_camera.py 文件源码

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)