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Runtime error
Runtime error
Update app.py
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app.py
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@@ -36,8 +36,12 @@ my_metadata = MetadataCatalog.get("dbmdz_coco_all")
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my_metadata.thing_classes = ["None", "BAD_BILLBOARD","BROKEN_SIGNAGE","CLUTTER_SIDEWALK","CONSTRUCTION_ROAD","FADED_SIGNAGE","GARBAGE","GRAFFITI","POTHOLES","SAND_ON_ROAD","UNKEPT_FACADE"]
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def predict_frame(frame,_):
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cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST = 0.3
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predictor = DefaultPredictor(cfg)
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outputs = predictor(frame)
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@@ -45,6 +49,46 @@ def predict_frame(frame,_):
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out = v.draw_instance_predictions(outputs["instances"].to("cpu"))
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return out.get_image()
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@spaces.GPU
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def inference(image_url, image, min_score):
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if not torch.cuda.is_available():
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@@ -74,7 +118,7 @@ def inference(image_url, image, min_score):
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@spaces.GPU
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def process_vid(video_path):
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cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST = 0.3
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if not torch.cuda.is_available():
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cfg.MODEL.DEVICE = "cpu"
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my_metadata.thing_classes = ["None", "BAD_BILLBOARD","BROKEN_SIGNAGE","CLUTTER_SIDEWALK","CONSTRUCTION_ROAD","FADED_SIGNAGE","GARBAGE","GRAFFITI","POTHOLES","SAND_ON_ROAD","UNKEPT_FACADE"]
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def predict_frame(frame,_):
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cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST = 0.3
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predictor = DefaultPredictor(cfg)
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outputs = predictor(frame)
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out = v.draw_instance_predictions(outputs["instances"].to("cpu"))
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return out.get_image()
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@spaces.GPU
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def opt_process_vid(video_path):
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cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST = 0.3
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cfg.MODEL.DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
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predictor = DefaultPredictor(cfg)
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v = VideoVisualizer(my_metadata,ColorMode.IMAGE)
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cap = cv2.VideoCapture(video_path)
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frame_size = (int(cap.get(3)), int(cap.get(4)))
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fps = int(cap.get(5))
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vid_fourcc= int(cap.get(cv2.CAP_PROP_FOURCC))
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output_path = '/content/drive/MyDrive/ColabNotebooks/gradio-exp/output.mp4'
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fourcc = cv2.VideoWriter_fourcc(*'mp4v')
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video_writer = cv2.VideoWriter(output_path,fourcc, fps, frame_size)
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num_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
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skipped_viz = 5
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skipped_outputs= 15
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# Process the video
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for i in tqdm.tqdm(range(num_frames)):
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ret, frame = cap.read()
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if not ret:
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break
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if i % skipped_outputs == 0:
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# Get prediction results for this frame
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outputs = predictor(frame)
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if i % skipped_viz == 0:
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# Draw a visualization of the predictions
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frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
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visualization = v.draw_instance_predictions(frame, outputs["instances"].to("cpu"))
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visualization = cv2.cvtColor(visualization.get_image(), cv2.COLOR_RGB2BGR)
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video_writer.write(visualization)
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# Release resources
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cap.release()
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video_writer.release()
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torch.cuda.empty_cache()
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return output_path
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@spaces.GPU
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def inference(image_url, image, min_score):
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if not torch.cuda.is_available():
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@spaces.GPU
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def process_vid(video_path):
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cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST = 0.3
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if not torch.cuda.is_available():
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cfg.MODEL.DEVICE = "cpu"
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