Update app.py

app.py

@@ -145,30 +145,31 @@ def detect(opt, save_img=False):
             print(f'{s}Done. ({(1E3 * (t2 - t1)):.1f}ms) Inference, ({(1E3 * (t3 - t2)):.1f}ms) NMS')
 
             # Stream results
-            if view_img:
-                cv2.imshow(str(p), im0)
-                cv2.waitKey(1)  # 1 millisecond
+            # if view_img:
+            #     cv2.imshow(str(p), im0)
+            #     cv2.waitKey(1)  # 1 millisecond
 
             # Save results (image with detections)
             if save_img:
                 if dataset.mode == 'image':
-                    Image.fromarray(im0).resize((300,250)).show()
-                    cv2.imwrite(save_path, im0)
+                    Image.fromarray(im0).show()
+                    im0.save(save_path)
+                    # cv2.imwrite(save_path, im0)
                     print(f" The image with the result is saved in: {save_path}")
-                else:  # 'video' or 'stream'
-                    if vid_path != save_path:  # new video
-                        vid_path = save_path
-                        if isinstance(vid_writer, cv2.VideoWriter):
-                            vid_writer.release()  # release previous video writer
-                        if vid_cap:  # video
-                            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))
-                        else:  # stream
-                            fps, w, h = 30, im0.shape[1], im0.shape[0]
-                            save_path += '.mp4'
-                        vid_writer = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*'mp4v'), fps, (w, h))
-                    vid_writer.write(im0)
+                # else:  # 'video' or 'stream'
+                #     if vid_path != save_path:  # new video
+                #         vid_path = save_path
+                #         if isinstance(vid_writer, cv2.VideoWriter):
+                #             vid_writer.release()  # release previous video writer
+                #         if vid_cap:  # video
+                #             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))
+                #         else:  # stream
+                #             fps, w, h = 30, im0.shape[1], im0.shape[0]
+                #             save_path += '.mp4'
+                #         vid_writer = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*'mp4v'), fps, (w, h))
+                #     vid_writer.write(im0)
 
     if save_txt or save_img:
         s = f"\n{len(list(save_dir.glob('labels/*.txt')))} labels saved to {save_dir / 'labels'}" if save_txt else ''
@@ -207,12 +208,12 @@ def get_output(image):
     opt = options(weights='logo_detection.pt',source=source)
     bbox = None
     with torch.no_grad():
-        if opt.update:  # update all models (to fix SourceChangeWarning)
-            for opt.weights in ['yolov7.pt']:
-                bbox,output_path = detect(opt)
-                strip_optimizer(opt.weights)
-        else:
-            bbox,output_path = detect(opt)
+        # if opt.update:  # update all models (to fix SourceChangeWarning)
+        #     for opt.weights in ['yolov7.pt']:
+        #         bbox,output_path = detect(opt)
+        #         strip_optimizer(opt.weights)
+        # else:
+        bbox,output_path = detect(opt)
     return Image.open(output_path)
 
 gr.Interface(fn=get_output,