#!/usr/bin/env python from importlib import import_module import os from flask import Flask, render_template, Response import cv2 import torch from utils.torch_utils import * from utils.general import check_img_size, non_max_suppression, scale_coords from models.experimental import attempt_load from models.yolo import Model from utils.datasets import LoadStreams, LoadImages import random from utils.plots import plot_one_box app = Flask(__name__) @app.route('/') def index(): """Video streaming home page.""" return render_template('index.html') def stream(): # WEIGHT = 'runs/train/yolov7-bsw2/weights/best.pt' WEIGHT = '/root/Public/model_ssai_fine_20221209/ipark_1208_1305.pt' # model = torch.load_state_dict(WEIGHT, map_location=select_device('0')) model = attempt_load(WEIGHT, map_location='cuda:0').half() # model = TracedModel(model, select_device('0'), img_size=640).half() imgsz = 640 stride = int(model.stride.max()) # model stride imgsz = check_img_size(imgsz, s=stride) # check img_size device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu') # Run inference model(torch.zeros(1, 3, imgsz, imgsz).to(device).type_as(next(model.parameters()))) # run once # 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 names] old_img_w = old_img_h = imgsz old_img_b = 1 """Video streaming generator function.""" # cap = cv2.VideoCapture('rtsp://astrodom:hdci12@192.168.170.73:554/stream1') dataset = LoadStreams('rtsp://astrodom:hdci12@192.168.170.73:554/stream1', img_size=imgsz, stride=stride) while True: # ret, frame = cap.read() # if not ret: # print('error') # break for path, img, im0s, vid_cap in dataset: img = torch.from_numpy(img).to(device) img = img.half() if True 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) # Warmup if device.type != 'cpu' and (old_img_b != img.shape[0] or old_img_h != img.shape[2] or old_img_w != img.shape[3]): old_img_b = img.shape[0] old_img_h = img.shape[2] old_img_w = img.shape[3] for i in range(3): model(img, augment=True)[0] with torch.no_grad(): # Calculating gradients would cause a GPU memory leak pred = model(img, augment=True)[0] # print(pred) # print('*' * 30) # Apply NMS pred = non_max_suppression(pred, 0.40, 0.45) # Process detections for i, det in enumerate(pred): # detections per image if True: # batch_size >= 1 p, s, im0, frame = path[i], '%g: ' % i, im0s[i].copy(), dataset.count # else: # p, s, im0, frame = path, '', im0s, getattr(dataset, 'frame', 0) p = Path(p) # to Path # save_path = str(save_dir / p.name) # img.jpg # txt_path = str(save_dir / 'labels' / p.stem) + ('' if dataset.mode == 'image' else f'_{frame}') # img.txt # gn = torch.tensor(im0.shape)[[1, 0, 1, 0]] # normalization gain whwh if 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 += f"{n} {names[int(c)]}{'s' * (n > 1)}, " # 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 # line = (cls, *xywh, conf) if opt.save_conf else (cls, *xywh) # label format # with open(txt_path + '.txt', 'a') as f: # f.write(('%g ' * len(line)).rstrip() % line + '\n') if True: # Add bbox to image label = f'{names[int(cls)]} {conf:.2f}' plot_one_box(xyxy, im0, label=label, color=colors[int(cls)], line_thickness=1) # x = img.permute(0,3,1,2) # (B, W, H, C) --> (B, C, W, H) image_bytes = cv2.imencode('.jpg', im0)[1].tobytes() yield (b'--frame\r\n' b'Content-Type: image/jpeg\r\n\r\n' + image_bytes + b'\r\n') @app.route('/video') def video(): """Video streaming route. Put this in the src attribute of an img tag.""" return Response(stream(), content_type='multipart/x-mixed-replace; boundary=frame') if __name__ == '__main__' : app.run(host='0.0.0.0', threaded=True, debug=True)