import os import cv2 from base_camera import BaseCamera import torch import torch.nn as nn import torchvision import numpy as np import argparse from utils.datasets import * from utils.plots import * from utils.general import * from utils.torch_utils import * def time_synchronized(): # pytorch-accurate time if torch.cuda.is_available(): torch.cuda.synchronize() return time.time() def select_device(device='', batch_size=None): # device = 'cpu' or '0' or '0,1,2,3' s = f'YOLOR 🚀 {git_describe() or date_modified()} torch {torch.__version__} ' # string cpu = device.lower() == 'cpu' if cpu: os.environ['CUDA_VISIBLE_DEVICES'] = '-1' # force torch.cuda.is_available() = False elif device: # non-cpu device requested # os.environ['CUDA_VISIBLE_DEVICES'] = device # set environment variable assert torch.cuda.is_available(), f'CUDA unavailable, invalid device {device} requested' # check availability cuda = not cpu and torch.cuda.is_available() if cuda: n = torch.cuda.device_count() if n > 1 and batch_size: # check that batch_size is compatible with device_count assert batch_size % n == 0, f'batch-size {batch_size} not multiple of GPU count {n}' space = ' ' * len(s) for i, d in enumerate(device.split(',') if device else range(n)): p = torch.cuda.get_device_properties(i) s += f"{'' if i == 0 else space}CUDA:{d} ({p.name}, {p.total_memory / 1024 ** 2}MB)\n" # bytes to MB else: s += 'CPU\n' logger.info(s.encode().decode('ascii', 'ignore') if platform.system() == 'Windows' else s) # emoji-safe return torch.device('cuda:0' if cuda else 'cpu') class Camera(BaseCamera): video_source = 'rtsp://astrodom:hdci12@192.168.170.73:554/stream1' def __init__(self): if os.environ.get('OPENCV_CAMERA_SOURCE'): Camera.set_video_source(int(os.environ['OPENCV_CAMERA_SOURCE'])) super(Camera, self).__init__() @staticmethod def set_video_source(source): Camera.video_source = source @staticmethod def frames(): out, weights, imgsz = \ 'inference/output', 'models/best.pt', 640 source = 'rtsp://astrodom:hdci12@192.168.170.73:554/stream1' # device = torch_utils.select_device() device = select_device() if os.path.exists(out): shutil.rmtree(out) # delete output folder os.makedirs(out) # make new output folder # Load model # google_utils.attempt_download(weights) model = torch.load(weights, map_location=device)['model'] model.to(device).eval() # # Second-stage classifier # classify = False # if classify: # modelc = torch_utils.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 = True and device.type != 'cpu' print('half = ' + str(half)) if half: model.half() # Set Dataloader vid_path, vid_writer = None, None dataset = LoadStreams(source, img_size=imgsz) #dataset = LoadStreams(source, img_size=imgsz) names = model.names if hasattr(model, 'names') else model.modules.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: 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() t1 = time_synchronized() pred = model(img, augment=False)[0] # Apply NMS pred = non_max_suppression(pred, 0.4, 0.5, classes=None, agnostic=False) # t2 = torch_utils.time_synchronized() t2 = time_synchronized() # # Apply Classifier # if classify: # pred = apply_classifier(pred, modelc, img, im0s) for i, det in enumerate(pred): # detections per image p, s, im0 = path, '', im0s # 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 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) print('%sDone. (%.3fs)' % (s, t2 - t1)) yield cv2.imencode('.jpg', im0)[1].tobytes()