yolov5.py

import argparse
import os
import platform
import shutil
import time
from pathlib import Path
import os
os.environ['KMP_DUPLICATE_LIB_OK']='True'
import cv2
import torch
import torch.backends.cudnn as cudnn
from numpy import random
from utils.utils import *
from models.experimental import attempt_load
from utils.datasets import LoadStreams, LoadImages
from utils.general import (
    check_img_size, non_max_suppression, apply_classifier, scale_coords,
    xyxy2xywh, plot_one_box, strip_optimizer, set_logging)
from utils.torch_utils import select_device, load_classifier, time_synchronized
from utils.general import (
    check_img_size, non_max_suppression, apply_classifier, scale_coords,
    xyxy2xywh, plot_one_box, strip_optimizer, set_logging)

# def detect_image(source,out,imgsz = 640,save_img=False,save_txt = False,weights = "./weights/yolov5s.pt"):
#     # out, source, weights, view_img, save_txt, imgsz = \
#     #     opt.output, opt.source, opt.weights, opt.view_img, opt.save_txt, opt.img_size
#     # webcam = source == '0' or source.startswith('rtsp') or source.startswith('http') or source.endswith('.txt')
#     webcam = source =='0'
#     # Initialize
#     set_logging()
#     device = select_device('')
#     # if os.path.exists(out):
#     #     shutil.rmtree(out)  # delete output folder
#     # os.mkdir(out)  # make new output folder
#     half = device.type != 'cpu'  # half precision only supported on CUDA
#
#     # Load model
#     model = attempt_load(weights, map_location=device)  # load FP32 model
#     imgsz = check_img_size(imgsz, s=model.stride.max())  # check img_size
#     if half:
#         model.half()  # to FP16
#
#     # Second-stage classifier
#     # classify = False
#     # if classify:
#     #     modelc = load_classifier(name='resnet101', n=2)  # initialize
#     #     modelc.load_state_dict(torch.load('weights/resnet101.pt', map_location=device)['model'])  # load weights
#     #     modelc.to(device).eval()
#
#     # Set Dataloader
#     vid_path, vid_writer = None, None
#     if webcam:
#         view_img = True
#         cudnn.benchmark = True  # set True to speed up constant image size inference
#         dataset = LoadStreams(source, img_size=imgsz)
#     else:
#         save_img = True
#         view_img = False
#         dataset = LoadImages(source, img_size=imgsz)
#
#     # Get names and colors
#     names = model.module.names if hasattr(model, 'module') else model.names
#     colors = [[random.randint(0, 255) for _ in range(3)] for _ in range(len(names))]
#
#     # Run inference
#     t0 = time.time()
#     img = torch.zeros((1, 3, imgsz, imgsz), device=device)  # init img
#     _ = model(img.half() if half else img) if device.type != 'cpu' else None
#     list_file = open("detection.txt", 'w')# run once
#     for path, img, im0s, vid_cap in 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='store_true')[0]
#
#         # Apply NMS
#         pred = non_max_suppression(pred, 0.4,0.5, agnostic='store_true')
#         t2 = time_synchronized()
#
#         # # Apply Classifier
#         # if classify:
#         #     pred = apply_classifier(pred, modelc, img, im0s)
#
#         # 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
#
#             save_path = str(Path(out) / Path(p).name)
#             txt_path = str(Path(out) / Path(p).stem) + ('_%g' % dataset.frame if dataset.mode == 'video' else '')
#             s += '%gx%g ' % img.shape[2:]  # print string
#             gn = torch.tensor(im0.shape)[[1, 0, 1, 0]]  # normalization gain whwh
#             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
#
#                 # Write results
#
#
#                 for *xyxy, conf, cls in reversed(det):
#                     if save_txt:  # Write to file
#                         xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist()  # normalized xywh
#                         with open(txt_path + '.txt', 'a') as f:
#                             f.write(('%g ' * 5 + '\n') % (cls, *xywh))
#
#                         # label format
#
#                     if save_img or view_img:  # Add bbox to image
#                         label = '%s %.2f' % (names[int(cls)], conf)
#                         plot_one_box(xyxy, im0, label=label, color=colors[int(cls)], line_thickness=3)
#
#             # Print time (inference + NMS)
#             # with open(os.getcwd()+'output.txt','w') as f:
#             #     f.write('%sDone. (%.3fs)' % (s, t2 - t1))
#
#             list_file.write('%sDone. (%.3fs)' % (s, t2 - t1))
#             list_file.write('\n')
#             print('%sDone. (%.3fs)' % (s, t2 - t1))
#
#             # Stream results
#             if view_img:
#                 cv2.imshow(p, im0)
#                 if cv2.waitKey(1) == ord('q'):  # q to quit
#                     raise StopIteration
#
#             # Save results (image with detections)
#             if save_img:
#                 if dataset.mode == 'images':
#                     cv2.imwrite(save_path, im0)
#                 else:
#                     if vid_path != save_path:  # new video
#                         vid_path = save_path
#                         if isinstance(vid_writer, cv2.VideoWriter):
#                             vid_writer.release()  # release previous video writer
#
#                         fourcc = 'mp4v'  # output video codec
#                         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))
#                         vid_writer = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*fourcc), fps, (w, h))
#                     vid_writer.write(im0)
#
#     if save_txt or save_img:
#         print('Results saved to %s' % Path(out))
#         # if platform.system() == 'Darwin' and not opt.update:  # MacOS
#         #     os.system('open ' + save_path)
#
#     print('Done. (%.3fs)' % (time.time() - t0))

def detect(save_img=False):
    out, source, weights, view_img, save_txt, imgsz = \
        opt.output, opt.source, opt.weights, opt.view_img, opt.save_txt, opt.img_size
    webcam = source.isnumeric() or source.startswith('rtsp') or source.startswith('http') or source.endswith('.txt')
    print('-----')
    print(source)
    print(type(source))
    # Initialize
    set_logging()
    device = select_device(opt.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(weights, map_location=device)  # load FP32 model
    imgsz = check_img_size(imgsz, s=model.stride.max())  # check img_size
    if half:
        model.half()  # to FP16

    # Second-stage classifier
    classify = False
    if classify:
        modelc = load_classifier(name='resnet101', n=2)  # initialize
        modelc.load_state_dict(torch.load('weights/resnet101.pt', map_location=device)['model'])  # load weights
        modelc.to(device).eval()

    # Set Dataloader
    vid_path, vid_writer = None, None
    if webcam:
        view_img = True
        cudnn.benchmark = True  # set True to speed up constant image size inference
        dataset = LoadStreams(source, img_size=imgsz)
    else:
        save_img = True
        dataset = LoadImages(source, img_size=imgsz)

    # Get names and colors
    names = model.module.names if hasattr(model, 'module') else model.names
    colors = [[random.randint(0, 255) for _ in range(3)] for _ in range(len(names))]

    # Run inference
    t0 = time.time()
    img = torch.zeros((1, 3, imgsz, imgsz), device=device)  # init img
    _ = model(img.half() if half else img) if device.type != 'cpu' else None  # run once
    for path, img, im0s, vid_cap in dataset:
        print('path:{0}'.format(path))
        print('im0s:{0}'.format(im0s))
        print('im0s类型:{0}'.format(type(im0s)))
        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()



        # Apply Classifier
        if classify:
            pred = apply_classifier(pred, modelc, img, im0s)

        # 用于存储人员边界坐标的列表  ---linjie
        people_coords = []

        # 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

            save_path = str(Path(out) / Path(p).name)
            txt_path = str(Path(out) / Path(p).stem) + ('_%g' % dataset.frame if dataset.mode == 'video' else '')
            s += '%gx%g ' % img.shape[2:]  # print string
            gn = torch.tensor(im0.shape)[[1, 0, 1, 0]]  # normalization gain whwh
            if det is not None and len(det):
               # print('先看看这里能不能进行，再看看im0多少:{0}。再看看im0类型:{1}'.format(im0,type(im0)))
                # 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

                # Write results
                for *xyxy, conf, cls in reversed(det):
                    if save_txt:  # Write to file
                        xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist()  # normalized xywh
                        with open(txt_path + '.txt', 'a') as f:
                            f.write(('%g ' * 5 + '\n') % (cls, *xywh))  # label format

                    if save_img or view_img:  # Add bbox to image
                        label = '%s %.2f' % (names[int(cls)], conf)
                        plot_one_box(xyxy, im0, label=label, color=colors[int(cls)], line_thickness=3)
                        #判断标签是否为人 --linjie
                        if label is not None:
                            if (label.split())[0] == 'person':
                                print('标签是人')
                                distancing(people_coords, im0, dist_thres_lim=(200, 250))
                                people_coords.append(xyxy)
                                # plot_one_box(xyxy, im0, line_thickness=3)
                                plot_dots_on_people(xyxy, im0)

            # 画上人与人的连接线 --linjie
            distancing(people_coords, im0, dist_thres_lim=(200, 250))

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

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

            # Save results (image with detections)
            if save_img:
                if dataset.mode == 'images':
                    cv2.imwrite(save_path, im0)
                else:
                    if vid_path != save_path:  # new video
                        vid_path = save_path
                        if isinstance(vid_writer, cv2.VideoWriter):
                            vid_writer.release()  # release previous video writer

                        fourcc = 'mp4v'  # output video codec
                        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))
                        vid_writer = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*fourcc), fps, (w, h))
                    vid_writer.write(im0)

    if save_txt or save_img:
        print('Results saved to %s' % Path(out))
        if platform == 'Darwin' and not opt.update:  # MacOS
            os.system('open ' + save_path)

    print('Done. (%.3fs)' % (time.time() - t0))
if __name__ == '__main__':
    parser = argparse.ArgumentParser()
    parser.add_argument('--weights', nargs='+', type=str, default='yolov5s.pt', help='model.pt path(s)')
    parser.add_argument('--source', type=str, default='inference/images', help='source')  # file/folder, 0 for webcam
    parser.add_argument('--output', type=str, default='inference/output', help='output folder')  # output folder
    parser.add_argument('--img-size', type=int, default=640, 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('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
    parser.add_argument('--view-img', action='store_true', help='display results')
    parser.add_argument('--save-txt', action='store_true', help='save results to *.txt')
    parser.add_argument('--classes', nargs='+', type=int, help='filter by class: --class 0, or --class 0 2 3')
    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('--update', action='store_true', help='update all models')
    opt = parser.parse_args()
    print(opt)

    with torch.no_grad():
        if opt.update:  # update all models (to fix SourceChangeWarning)
            for opt.weights in ['yolov5s.pt', 'yolov5m.pt', 'yolov5l.pt', 'yolov5x.pt']:
                print('model1')
                detect()
                strip_optimizer(opt.weights)
        else:
            print('model2')
            detect()