count.py

from subprocess import list2cmdline
import sys

from numpy import ndarray
sys.path.insert(0, './yolov5')

from yolov5.utils.google_utils import attempt_download
from yolov5.models.experimental import attempt_load
from yolov5.utils.datasets import LoadImages, LoadStreams
from yolov5.utils.general import check_img_size, non_max_suppression, scale_coords, check_imshow
from yolov5.utils.torch_utils import select_device, time_synchronized
from deep_sort_pytorch.utils.parser import get_config
from deep_sort_pytorch.deep_sort import DeepSort
import argparse
import os
import platform
import shutil
import time
from pathlib import Path
import cv2
import torch
import torch.backends.cudnn as cudnn


########################################


source_dir = 'inference/input/test3.mp4' # '0'    # 要打开的文件。若要调用摄像头，需要设置为字符串'0'，而不是数字0，按q退出播放
output_dir = 'inference/output' # 要保存到的文件夹
show_video = True   # 运行时是否显示
save_video = True   # 是否保存运行结果视频
save_text = True    # 是否保存结果数据到txt文件中，
                    # result.txt的格式是(帧序号,框序号,框到左边距离,框到顶上距离,框横长,框竖高,-1,-1,-1,-1)，
                    # number.txt的内容是统计到第几帧时每条线沿两个方向的跨线物体数
class_list = [2]    # 类别序号，在coco_classes.txt中查看（注意是序号不是行号），可以有一个或多个类别
point_idx = 0       # 方框的检测点位置(0, 1, 2, 3, 4)，看下边的图，当一个方框的检测点跨过检测线时，统计数会+1

lines = [           # 在这里定义检测线
    # 一条线就是一个list，内容为[x1, y1, x2, y2, (R, G, B), 线的粗细]，例如：
    [300, 1080, 1250, 600, (255,0,0), 2],
    [1660, 610, 1920, 900, (0,255,0), 2],

]


########################################
# 一些参数的定义
# x是点到左边的距离，y是点到顶上的距离
# 线的小侧是线与x轴所夹的锐角区域

# 方框检测点的序号
#    1__________________2
#    |                  |
#    |                  |
#    |      0(中心点)   |
#    |                  |
#    |__________________|
#    4                  3


#    |-------> x轴
#    |
#    |
#    V
#    y轴

########################################
# 判断点是否位于线的大侧
def big_side(line, x, y) -> bool:
    x1 = line[0]
    y1 = line[1]
    x2 = line[2]
    y2 = line[3]

    if y1 == y2:
        if y > y1:
            return True
        elif y <= y1:
            return False

    if x1 == x2:
        if x > x1:
            return True
        elif x <= x1:
            return False

    if (x - x1)/(x2 - x1) > (y - y1)/(y2 - y1):
        return True
    else:
        return False

# 每条线添加一个list，统计大->小、小->大两个方向穿过检测线的物体数，下标为6
for line in lines:
    line.append([0,0])

########################################



palette = (2 ** 11 - 1, 2 ** 15 - 1, 2 ** 20 - 1)


def xyxy_to_xywh(*xyxy):
    """" Calculates the relative bounding box from absolute pixel values. """
    bbox_left = min([xyxy[0].item(), xyxy[2].item()])
    bbox_top = min([xyxy[1].item(), xyxy[3].item()])
    bbox_w = abs(xyxy[0].item() - xyxy[2].item())
    bbox_h = abs(xyxy[1].item() - xyxy[3].item())
    x_c = (bbox_left + bbox_w / 2)
    y_c = (bbox_top + bbox_h / 2)
    w = bbox_w
    h = bbox_h
    return x_c, y_c, w, h

def xyxy_to_tlwh(bbox_xyxy):
    tlwh_bboxs = []
    for i, box in enumerate(bbox_xyxy):
        x1, y1, x2, y2 = [int(i) for i in box]
        top = x1
        left = y1
        w = int(x2 - x1)
        h = int(y2 - y1)
        tlwh_obj = [top, left, w, h]
        tlwh_bboxs.append(tlwh_obj)
    return tlwh_bboxs


def compute_color_for_labels(label):
    """
    Simple function that adds fixed color depending on the class
    """
    color = [int((p * (label ** 2 - label + 1)) % 255) for p in palette]
    return tuple(color)


def draw_boxes(img, bbox, identities=None, offset=(0, 0)):
    for i, box in enumerate(bbox):
        x1, y1, x2, y2 = [int(i) for i in box]
        x1 += offset[0]
        x2 += offset[0]
        y1 += offset[1]
        y2 += offset[1]
        # box text and bar
        id = int(identities[i]) if identities is not None else 0
        color = compute_color_for_labels(id)
        label = '{}{:d}'.format("", id)
        t_size = cv2.getTextSize(label, cv2.FONT_HERSHEY_PLAIN, 2, 2)[0]
        cv2.rectangle(img, (x1, y1), (x2, y2), color, 3)
        cv2.rectangle(
            img, (x1, y1), (x1 + t_size[0] + 3, y1 + t_size[1] + 4), color, -1)
        cv2.putText(img, label, (x1, y1 +
                                 t_size[1] + 4), cv2.FONT_HERSHEY_PLAIN, 2, [255, 255, 255], 2)
    return img



# 在调用detect()函数进行检测时，记得加上
# with torch.no_grad():
#     detect(args)
def detect(opt):
    out, source, yolo_weights, deep_sort_weights, show_vid, save_vid, save_txt, imgsz = \
        opt.output, opt.source, opt.yolo_weights, opt.deep_sort_weights, opt.show_vid, opt.save_vid, opt.save_txt, opt.img_size
    webcam = source == '0' or source.startswith(
        'rtsp') or source.startswith('http') or source.endswith('.txt')

#####################################################
    # 参数设置
    show_vid = show_video
    save_vid = save_video
    save_txt = save_text

#####################################################
    # 获取视频的信息
    a = cv2.VideoCapture(source)
    frame_num = int(a.get(7))   # 总帧数
    frame_rate = a.get(5)       # 帧速率
    frame_w = a.get(3)          # 帧宽
    frame_h = a.get(4)          # 帧高
    print(frame_num, frame_rate, frame_w, frame_h)
    a.release()

#####################################################

    # point_list统计所有点与线之间的位置关系
    point_list = []
    for i in range(len(lines)):
        point_list.append([[],[]]) # 分别统计位于大侧、小侧的点


#####################################################

    # initialize deepsort
    cfg = get_config()
    cfg.merge_from_file(opt.config_deepsort)
    attempt_download(deep_sort_weights, repo='mikel-brostrom/Yolov5_DeepSort_Pytorch')
    deepsort = DeepSort(cfg.DEEPSORT.REID_CKPT,
                        max_dist=cfg.DEEPSORT.MAX_DIST, min_confidence=cfg.DEEPSORT.MIN_CONFIDENCE,
                        nms_max_overlap=cfg.DEEPSORT.NMS_MAX_OVERLAP, max_iou_distance=cfg.DEEPSORT.MAX_IOU_DISTANCE,
                        max_age=cfg.DEEPSORT.MAX_AGE, n_init=cfg.DEEPSORT.N_INIT, nn_budget=cfg.DEEPSORT.NN_BUDGET,
                        use_cuda=True)

    # Initialize
    device = select_device(opt.device)
    ##################################
    # 打印使用的设备
    print(device)
    ##################################
    if os.path.exists(out):
        shutil.rmtree(out)  # delete output folder
    os.makedirs(out)  # make new output folder
    half = device.type != 'cpu'  # half precision only supported on CUDA

    # Load model
    model = attempt_load(yolo_weights, map_location=device)  # load FP32 model
    stride = int(model.stride.max())  # model stride
    imgsz = check_img_size(imgsz, s=stride)  # check img_size
    names = model.module.names if hasattr(model, 'module') else model.names  # get class names
    if half:
        model.half()  # to FP16

    # Set Dataloader
    vid_path, vid_writer = None, None
    # Check if environment supports image displays
    if show_vid:
        show_vid = check_imshow()

    if webcam:
        cudnn.benchmark = True  # set True to speed up constant image size inference
        dataset = LoadStreams(source, img_size=imgsz, stride=stride)
    else:
        dataset = LoadImages(source, img_size=imgsz)

    # Get names and colors
    names = model.module.names if hasattr(model, 'module') else model.names

    # Run inference
    if device.type != 'cpu':
        model(torch.zeros(1, 3, imgsz, imgsz).to(device).type_as(next(model.parameters())))  # run once
    t0 = time.time()

    save_path = str(Path(out))
    txt_path = str(Path(out)) + '/results.txt'

    for frame_idx, (path, img, im0s, vid_cap) in enumerate(dataset):
        img = torch.from_numpy(img).to(device)
        img = img.half() if half else img.float()  # uint8 to fp16/32
        img /= 255.0  # 0 - 255 to 0.0 - 1.0
        if img.ndimension() == 3:
            img = img.unsqueeze(0)

        # Inference
        t1 = time_synchronized()
        pred = model(img, augment=opt.augment)[0]

        # Apply NMS
        pred = non_max_suppression(
            pred, opt.conf_thres, opt.iou_thres, classes=opt.classes, agnostic=opt.agnostic_nms)
        t2 = time_synchronized()

        # Process detections
        for i, det in enumerate(pred):  # detections per image
            if webcam:  # batch_size >= 1
                p, s, im0 = path[i], '%g: ' % i, im0s[i].copy()
            else:
                p, s, im0 = path, '', im0s

            s += '%gx%g ' % img.shape[2:]  # print string
            save_path = str(Path(out) / Path(p).name)

            if det is not None and len(det):
                # Rescale boxes from img_size to im0 size
                det[:, :4] = scale_coords(
                    img.shape[2:], det[:, :4], im0.shape).round()

                # Print results
                for c in det[:, -1].unique():
                    n = (det[:, -1] == c).sum()  # detections per class
                    s += '%g %ss, ' % (n, names[int(c)])  # add to string

                xywh_bboxs = []
                confs = []

                # Adapt detections to deep sort input format
                for *xyxy, conf, cls in det:
                    # to deep sort format
                    x_c, y_c, bbox_w, bbox_h = xyxy_to_xywh(*xyxy)
                    xywh_obj = [x_c, y_c, bbox_w, bbox_h]
                    xywh_bboxs.append(xywh_obj)
                    confs.append([conf.item()])

                xywhs = torch.Tensor(xywh_bboxs)
                confss = torch.Tensor(confs)

                # pass detections to deepsort
                outputs = deepsort.update(xywhs, confss, im0)
                # draw boxes for visualization
                if len(outputs) > 0:
                    bbox_xyxy = outputs[:, :4]
                    identities = outputs[:, -1]
                    draw_boxes(im0, bbox_xyxy, identities)
                    # to MOT format
                    tlwh_bboxs = xyxy_to_tlwh(bbox_xyxy)

                    #############################################
                    # 这里tlwh_bboxs是list，里边包着的也是list
                    # tlwh_bboxs的元素中的四个值分别是框到左边、顶上距离和框横长和竖高
                    # 而outputs是ndarray
                    # outputs中每一个子数组中的五个数分别是每一个框的左上角xy和右下角xy坐标和框序号
                    # x是点到左边的距离，y是点到顶上的距离
                    #############################################

                    for point in outputs:
                        # 计算检测点坐标
                        if point_idx == 0:
                            point_x = int(point[0]+point[2])/2
                            point_y = int(point[1]+point[3])/2
                        elif point_idx == 1:
                            point_x = point[0]
                            point_y = point[1]
                        elif point_idx == 2:
                            point_x = point[2]
                            point_y = point[1]
                        elif point_idx == 3:
                            point_x = point[2]
                            point_y = point[3]
                        elif point_idx == 4:
                            point_x = point[0]
                            point_y = point[3]
                        
                        # 计算检测点与每条线的位置关系
                        for line_idx, line in enumerate(lines):
                            if big_side(line, point_x, point_y):                # 点此刻位于大侧
                                if point[-1] not in point_list[line_idx][0]:    # 若不在大侧list，则加入
                                    point_list[line_idx][0].append(point[-1])
                                if point[-1] in point_list[line_idx][1]:        # 若此前位于小侧list，说明沿着小->大方向穿过了检测线
                                    line[6][1] += 1                             # 统计数+1，并从小侧list移除
                                    point_list[line_idx][1].remove(point[-1])
                            
                            else:
                                if point[-1] not in point_list[line_idx][1]:
                                    point_list[line_idx][1].append(point[-1])
                                if point[-1] in point_list[line_idx][0]:
                                    line[6][0] += 1
                                    point_list[line_idx][0].remove(point[-1])


                    #############################################

                    # Write MOT compliant results to file
                    if save_txt:
                        for j, (tlwh_bbox, output) in enumerate(zip(tlwh_bboxs, outputs)):
                            # bbox_top = tlwh_bbox[0]
                            # bbox_left = tlwh_bbox[1]
                            bbox_left = tlwh_bbox[0]
                            bbox_top = tlwh_bbox[1]
                            bbox_w = tlwh_bbox[2]
                            bbox_h = tlwh_bbox[3]
                            identity = output[-1]
                            with open(txt_path, 'a') as f:
                                # f.write(('%g ' * 10 + '\n') % (frame_idx, identity, bbox_top,
                                #                             bbox_left, bbox_w, bbox_h, -1, -1, -1, -1))  # label format
                                f.write(('%g ' * 10 + '\n') % (frame_idx, identity, bbox_left,
                                                            bbox_top, bbox_w, bbox_h, -1, -1, -1, -1))  # label format
                                                        # 修改后的格式为：帧序号、框序号、框到左边距离、框到顶上距离、框横长、框竖高，原命名应该是把顶上和左边命名写反了
            else:
                deepsort.increment_ages()

            # Print time (inference + NMS)
            print('%sDone. (%.3fs)' % (s, t2 - t1))

            #########################################################
            # 画线
            for line in lines:
                cv2.line(im0, (line[0], line[1]), (line[2], line[3]), line[4], line[5])   # 画布、起点坐标、终点坐标、线颜色、线粗细
            # 标注文字
            gap = int(frame_w / len(lines))
            for line_idx, line in enumerate(lines):
                cv2.putText(im0, f'dir1 = {line[6][0]}', (gap*line_idx+25, 25), cv2.FONT_HERSHEY_COMPLEX, 1, line[4], 2)    # 画布、内容、左下角坐标、字体、字号（数字越大字越大）、字颜色、笔画粗细
                cv2.putText(im0, f'dir2 = {line[6][1]}', (gap*line_idx+25, 50), cv2.FONT_HERSHEY_COMPLEX, 1, line[4], 2)    # 画布、内容、左下角坐标、字体、字号（数字越大字越大）、字颜色、笔画粗细

            # 写入保存文件
            if save_txt:
                with open(out+'/number.txt', 'a') as f:
                    f.write(f'frame{frame_idx}:\n')
                    for line_idx, line in enumerate(lines):
                        f.write(f'line{line_idx}\tdirection1:{line[6][0]}, direction2:{line[6][1]}\n')
                    f.write('\n')



            #########################################################

            # Stream results
            if show_vid:
                cv2.imshow(p, im0)
                if cv2.waitKey(1) == ord('q'):  # q to quit
                    raise StopIteration

            # Save results (image with detections)
            if save_vid:
                if vid_path != save_path:  # new video
                    vid_path = save_path
                    if isinstance(vid_writer, cv2.VideoWriter):
                        vid_writer.release()  # release previous video writer
                    if vid_cap:  # video
                        fps = vid_cap.get(cv2.CAP_PROP_FPS)
                        w = int(vid_cap.get(cv2.CAP_PROP_FRAME_WIDTH))
                        h = int(vid_cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
                    else:  # stream
                        fps, w, h = 30, im0.shape[1], im0.shape[0]
                        save_path += '.mp4'

                    vid_writer = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*'mp4v'), fps, (w, h))
                vid_writer.write(im0)

    if save_txt or save_vid:
        print('Results saved to %s' % os.getcwd() + os.sep + out)
        if platform == 'darwin':  # MacOS
            os.system('open ' + save_path)

    print('Done. (%.3fs)' % (time.time() - t0))


if __name__ == '__main__':
    parser = argparse.ArgumentParser()
    parser.add_argument('--yolo_weights', type=str, default='yolov5/weights/yolov5s.pt', help='model.pt path')
    parser.add_argument('--deep_sort_weights', type=str, default='deep_sort_pytorch/deep_sort/deep/checkpoint/ckpt.t7', help='ckpt.t7 path')
    # file/folder, 0 for webcam
    parser.add_argument('--source', type=str, default=source_dir, help='source')
    parser.add_argument('--output', type=str, default=output_dir, help='output folder')  # output folder
    parser.add_argument('--img-size', type=int, default=416, help='inference size (pixels)')
    parser.add_argument('--conf-thres', type=float, default=0.4, help='object confidence threshold')
    parser.add_argument('--iou-thres', type=float, default=0.5, help='IOU threshold for NMS')
    parser.add_argument('--fourcc', type=str, default='mp4v', help='output video codec (verify ffmpeg support)')
    parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
    parser.add_argument('--show-vid', action='store_true', help='display tracking video results')
    parser.add_argument('--save-vid', action='store_true', help='save video tracking results')
    parser.add_argument('--save-txt', action='store_true', help='save MOT compliant results to *.txt')
    # class 0 is person, 1 is bycicle, 2 is car... 79 is oven
    parser.add_argument('--classes', nargs='+', default=class_list, type=int, help='filter by class')
    parser.add_argument('--agnostic-nms', action='store_true', help='class-agnostic NMS')
    parser.add_argument('--augment', action='store_true', help='augmented inference')
    parser.add_argument("--config_deepsort", type=str, default="deep_sort_pytorch/configs/deep_sort.yaml")
    args = parser.parse_args()
    args.img_size = check_img_size(args.img_size)

    with torch.no_grad():
        detect(args)