新增推理视频流的逻辑

app/service/project_detect_service.py

@@ -128,8 +128,7 @@ def run_detect_yolo(detect_in: ProjectDetectLogIn, detect: ProjectDetect, train:
     return detect_log
 
 
-async def run_commend(weights: str, source: str, project: str, name: str,
-                log_id: int, detect_id: int, session: Session):
+async def run_detect_img(weights: str, source: str, project: str, name: str, log_id: int, detect_id: int, session: Session):
     """
     执行yolov5的推理
     :param weights: 权重文件
@@ -180,9 +179,10 @@ async def run_commend(weights: str, source: str, project: str, name: str,
             pdc.add_detect_imgs(detect_log_imgs, session)
 
 
-def run_detect_rtsp(weights_pt: str, rtsp_url: str, data: str):
+async def run_detect_rtsp(weights_pt: str, rtsp_url: str, data: str, detect_id: int):
     """
     rtsp 视频流推理
+    :param detect_id: 训练集的id
     :param weights_pt: 权重文件
     :param rtsp_url: 视频流地址
     :param data: yaml文件
@@ -233,43 +233,21 @@ def run_detect_rtsp(weights_pt: str, rtsp_url: str, data: str):
 
         # Process predictions
         for i, det in enumerate(pred):  # per image
-            seen += 1
             p, im0, frame = path[i], im0s[i].copy(), dataset.count
-            s += f"{i}: "
-
-            p = Path(p)  # to Path
-            s += "{:g}x{:g} ".format(*im.shape[2:])  # print string
-            gn = torch.tensor(im0.shape)[[1, 0, 1, 0]]  # normalization gain whwh
-            imc = im0.copy() if False else im0  # for save_crop
             annotator = Annotator(im0, line_width=3, example=str(names))
             if len(det):
                 # Rescale boxes from img_size to im0 size
                 det[:, :4] = scale_boxes(im.shape[2:], det[:, :4], im0.shape).round()
 
-                # Print results
-                for c in det[:, 5].unique():
-                    n = (det[:, 5] == 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):
-                    c = int(cls)  # integer class
-                    label = names[c] if False else f"{names[c]}"
-                    confidence = float(conf)
-                    confidence_str = f"{confidence:.2f}"
-
                     c = int(cls)  # integer class
                     label = None if False else (names[c] if False else f"{names[c]} {conf:.2f}")
                     annotator.box_label(xyxy, label, color=colors(c, True))
 
             # Stream results
             im0 = annotator.result()
-            if platform.system() == "Linux" and p not in windows:
-                windows.append(p)
-                cv2.namedWindow(str(p), cv2.WINDOW_NORMAL | cv2.WINDOW_KEEPRATIO)  # allow window resize (Linux)
-                cv2.resizeWindow(str(p), im0.shape[1], im0.shape[0])
-            cv2.imshow(str(p), im0)
-            cv2.waitKey(1)  # 1 millisecond
+