Spaces:
Running
Running
shriarul5273
commited on
Commit
·
dd13bc1
1
Parent(s):
ef2207d
Added FPS
Browse files- .github/workflows/huggingface.yml +2 -2
- app.py +12 -7
.github/workflows/huggingface.yml
CHANGED
@@ -17,9 +17,9 @@ jobs:
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env:
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HF: ${{secrets.HF_TOKEN }}
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HF_USER: ${{secrets.HF_USER }}
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run: git remote add space https://$HF_USER:[email protected]/spaces/$HF_USER/
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- name: Push to hub
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env:
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HF: ${{ secrets.HF_TOKEN}}
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HF_USER: ${{secrets.HF_USER }}
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run: git push --force https://$HF_USER:[email protected]/spaces/$HF_USER/
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env:
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HF: ${{secrets.HF_TOKEN }}
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HF_USER: ${{secrets.HF_USER }}
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+
run: git remote add space https://$HF_USER:[email protected]/spaces/$HF_USER/Yolov7
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- name: Push to hub
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env:
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HF: ${{ secrets.HF_TOKEN}}
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HF_USER: ${{secrets.HF_USER }}
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+
run: git push --force https://$HF_USER:[email protected]/spaces/$HF_USER/Yolov7
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app.py
CHANGED
@@ -9,7 +9,7 @@ from utils.general import check_img_size, non_max_suppression, \
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scale_coords
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from utils.plots import plot_one_box
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from utils.torch_utils import time_synchronized
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-
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@@ -78,8 +78,10 @@ def detect(img,model,device,iou_threshold=0.45,confidence_threshold=0.25):
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# Inference
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t1 = time_synchronized()
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with torch.no_grad(): # Calculating gradients would cause a GPU memory leak
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pred = model(img,augment=True)[0]
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t2 = time_synchronized()
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# Apply NMS
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@@ -97,7 +99,7 @@ def detect(img,model,device,iou_threshold=0.45,confidence_threshold=0.25):
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label = f'{names[int(cls)]} {conf:.2f}'
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plot_one_box(xyxy, imgs, label=label, color=colors[int(cls)], line_thickness=2)
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return imgs
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def inference(img,model_link,iou_threshold,confidence_threshold):
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print(model_link)
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@@ -118,16 +120,17 @@ def inference2(video,model_link,iou_threshold,confidence_threshold):
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fps = frames.get(cv2.CAP_PROP_FPS)
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image_size = (int(frames.get(cv2.CAP_PROP_FRAME_WIDTH)),int(frames.get(cv2.CAP_PROP_FRAME_HEIGHT)))
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finalVideo = cv2.VideoWriter('output.mp4',cv2.VideoWriter_fourcc(*'VP90'), fps, image_size)
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-
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while frames.isOpened():
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ret,frame = frames.read()
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if not ret:
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break
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frame = detect(frame,model,device,iou_threshold,confidence_threshold)
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finalVideo.write(frame)
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frames.release()
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finalVideo.release()
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return 'output.mp4'
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@@ -145,6 +148,7 @@ with gr.Blocks() as demo:
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with gr.Row():
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image_input = gr.Image(type='pil', label="Input Image", source="upload")
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image_output = gr.Image(type='pil', label="Output Image", source="upload")
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image_drop = gr.Dropdown(choices=models,value=models[0])
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image_iou_threshold = gr.Slider(label="IOU Threshold",interactive=True, minimum=0.0, maximum=1.0, value=0.45)
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image_conf_threshold = gr.Slider(label="Confidence Threshold",interactive=True, minimum=0.0, maximum=1.0, value=0.25)
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@@ -155,6 +159,7 @@ with gr.Blocks() as demo:
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with gr.Row():
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video_input = gr.Video(type='pil', label="Input Image", source="upload")
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video_output = gr.Video(type="pil", label="Output Image",format="mp4")
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video_drop = gr.Dropdown(choices=models,value=models[0])
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video_iou_threshold = gr.Slider(label="IOU Threshold",interactive=True, minimum=0.0, maximum=1.0, value=0.45)
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video_conf_threshold = gr.Slider(label="Confidence Threshold",interactive=True, minimum=0.0, maximum=1.0, value=0.25)
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@@ -167,9 +172,9 @@ with gr.Blocks() as demo:
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text_button.click(inference, inputs=[image_input,image_drop,
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image_iou_threshold,image_conf_threshold],
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outputs=image_output)
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video_button.click(inference2, inputs=[video_input,video_drop,
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video_iou_threshold,video_conf_threshold],
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outputs=video_output)
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demo.launch()
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scale_coords
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from utils.plots import plot_one_box
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from utils.torch_utils import time_synchronized
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import time
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# Inference
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t1 = time_synchronized()
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start = time.time()
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with torch.no_grad(): # Calculating gradients would cause a GPU memory leak
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pred = model(img,augment=True)[0]
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fps_inference = 1/(time.time()-start)
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t2 = time_synchronized()
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# Apply NMS
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label = f'{names[int(cls)]} {conf:.2f}'
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plot_one_box(xyxy, imgs, label=label, color=colors[int(cls)], line_thickness=2)
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return imgs,fps_inference
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def inference(img,model_link,iou_threshold,confidence_threshold):
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print(model_link)
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fps = frames.get(cv2.CAP_PROP_FPS)
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image_size = (int(frames.get(cv2.CAP_PROP_FRAME_WIDTH)),int(frames.get(cv2.CAP_PROP_FRAME_HEIGHT)))
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finalVideo = cv2.VideoWriter('output.mp4',cv2.VideoWriter_fourcc(*'VP90'), fps, image_size)
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fps_video = []
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while frames.isOpened():
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ret,frame = frames.read()
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if not ret:
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break
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frame,fps = detect(frame,model,device,iou_threshold,confidence_threshold)
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fps_video.append[fps]
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finalVideo.write(frame)
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frames.release()
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finalVideo.release()
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return 'output.mp4',np.mean(fps_video)
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with gr.Row():
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image_input = gr.Image(type='pil', label="Input Image", source="upload")
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image_output = gr.Image(type='pil', label="Output Image", source="upload")
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fps_image = gr.Number(0,label='FPS')
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image_drop = gr.Dropdown(choices=models,value=models[0])
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image_iou_threshold = gr.Slider(label="IOU Threshold",interactive=True, minimum=0.0, maximum=1.0, value=0.45)
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image_conf_threshold = gr.Slider(label="Confidence Threshold",interactive=True, minimum=0.0, maximum=1.0, value=0.25)
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with gr.Row():
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video_input = gr.Video(type='pil', label="Input Image", source="upload")
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video_output = gr.Video(type="pil", label="Output Image",format="mp4")
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fps_video = gr.Number(0,label='FPS')
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video_drop = gr.Dropdown(choices=models,value=models[0])
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video_iou_threshold = gr.Slider(label="IOU Threshold",interactive=True, minimum=0.0, maximum=1.0, value=0.45)
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video_conf_threshold = gr.Slider(label="Confidence Threshold",interactive=True, minimum=0.0, maximum=1.0, value=0.25)
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text_button.click(inference, inputs=[image_input,image_drop,
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image_iou_threshold,image_conf_threshold],
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outputs=[image_output,fps_image])
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video_button.click(inference2, inputs=[video_input,video_drop,
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video_iou_threshold,video_conf_threshold],
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outputs=[video_output,fps_video])
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demo.launch()
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