#!/usr/bin/env python # -*- coding: utf-8 -*- # @Author : linjie import os import cv2 from base_camera import BaseCamera from models.experimental import attempt_load import torch.backends.cudnn as cudnn import torch import torch.nn as nn import torchvision import numpy as np import argparse from utils.datasets import * from utils.utils import * from utils.torch_utils import select_device, load_classifier, time_synchronized from flask import Flask,url_for from log import Logger from deep_sort_pytorch.deep_sort import DeepSort from deep_sort_pytorch.utils.parser import get_config class Camera(BaseCamera): #video_source = 'people.mp4' palette = (2 ** 11 - 1, 2 ** 15 - 1, 2 ** 20 - 1) video_source = 'people.mp4' def __init__(self): if os.environ.get('OPENCV_CAMERA_SOURCE'): print('走了吗') Camera.set_video_source(int(os.environ['OPENCV_CAMERA_SOURCE'])) super(Camera, self).__init__() print('走了') @staticmethod def set_video_source(source): Camera.video_source = source @staticmethod def bbox_rel(*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 @staticmethod 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 Camera.palette] return tuple(color) # @staticmethod # def people_appeal1(): # a = 'people' # yield a @staticmethod def draw_boxes(img, bbox, cls_names, scores, 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 = Camera.compute_color_for_labels(id) label = '%d %s %d' % (id, cls_names[i], scores[i]) label += '%' print("{0}号人物出现！========================================".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 @staticmethod def frames(): logger = Logger() print('初始化过了，。。。。。') camera = cv2.VideoCapture(Camera.video_source) if not camera.isOpened(): raise RuntimeError('Could not start camera.') out, weights, imgsz = \ '.inference/output', 'weights/yolov5s.pt', 640 source = "people.mp4" # print(source) # print(type(source)) #webcam = source.isnumeric() # print('看看webcam:{0}'.format(webcam)) ''' 初始化deepsort ''' # initialize deepsort # cfg = get_config() # cfg.merge_from_file('deep_sort_pytorch/configs/deep_sort.yaml') # 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) device = torch_utils.select_device() # print(weights) # print(os.getcwd()) if os.path.exists(out): shutil.rmtree(out) # delete output folder os.makedirs(out) # make new output folder #shutil.rmtree(out) # Load model # google_utils.attempt_download(weights) # model = torch.load(weights, map_location=device)['model'] model = attempt_load(weights, map_location=device) # load FP32 model model.to(device).eval() # 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() # Half precision half = False and device.type != 'cpu' # print('half = ' + str(half)) if half: model.half() # 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 # # # 如果检测视频的时候想显示出来，可以在这里加一行view_img = True # view_img = True # dataset = LoadImages(source, img_size=imgsz) # vid_path, vid_writer = None, None dataset = LoadImages(source, img_size=imgsz) #dataset = LoadStreams(source, img_size=imgsz) # print('看看dataset:{0}'.format(dataset)) names = model.names if hasattr(model, 'names') else model.modules.names # print('----') # print(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 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 = torch_utils.time_synchronized() pred = model(img, augment=False)[0] # Apply NMS pred = non_max_suppression(pred, 0.4, 0.5, fast=True, classes=None, agnostic=False) t2 = torch_utils.time_synchronized() # Apply Classifier if classify: pred = apply_classifier(pred, modelc, img, im0s) people_coords = [] for i, det in enumerate(pred): # detections per image #p, s, im0 = path, '', im0s p, s, im0 = path[i], '%g: ' % i, im0s[i].copy() #save_path = str(Path(out) / Path(p).name) 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() #for c in det[:, -1].unique(): #probably error with torch 1.5 for c in det[:, -1].detach().unique(): n = (det[:, -1] == c).sum() # detections per class s += '%g %s, ' % (n, names[int(c)]) # add to string # --- linjie # bbox_xywh = [] # confs = [] # clses = [] # for *xyxy, conf, cls in det: # label = '%s %.2f' % (names[int(cls)], conf) # plot_one_box(xyxy, im0, label=label, color=colors[int(cls)], line_thickness=3) # Write results for *xyxy, conf, cls in reversed(det): # -- linjie deepsort # x_c, y_c, bbox_w, bbox_h = Camera.bbox_rel(*xyxy) # obj = [x_c, y_c, bbox_w, bbox_h] # bbox_xywh.append(obj) # confs.append([conf.item()]) # clses.append([cls.item()]) label = '%s %.2f' % (names[int(cls)], conf) print('看看这次打的标签：{0}'.format(label)) 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': logger.info('当前进程：{0}.遇到了人'.format(os.getpid())) #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) 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 yield cv2.imencode('.jpg', im0)[1].tobytes()