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Update app.py
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app.py
CHANGED
@@ -6,18 +6,21 @@ import cv2
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import requests
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from ultralytics import YOLO
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# Remove extra CLI arguments
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sys.argv = [arg for arg in sys.argv if arg != "--import"]
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# Load the YOLO11-pose model (auto-downloads if needed)
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model = YOLO("yolo11n-pose.pt")
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def process_input(uploaded_file, youtube_link, image_url):
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"""
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Process
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Priority
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Returns a tuple:
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(download_file_path, display_file_path, status_message)
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"""
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input_path = None
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@@ -29,90 +32,98 @@ def process_input(uploaded_file, youtube_link, image_url):
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stream = yt.streams.filter(file_extension='mp4', progressive=True)\
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.order_by("resolution").desc().first()
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if stream is None:
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return None, None, "No suitable mp4 stream found."
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input_path = stream.download()
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except Exception as e:
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return None, None, f"Error downloading video: {e}"
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# Priority 2: Image URL
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elif image_url and image_url.strip():
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try:
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response = requests.get(image_url, stream=True)
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if response.status_code != 200:
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return None, None, f"Error downloading image: HTTP {response.status_code}"
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temp_image_path = os.path.join(tempfile.gettempdir(), "downloaded_image.jpg")
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with open(temp_image_path, "wb") as f:
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f.write(response.content)
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input_path = temp_image_path
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except Exception as e:
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return None, None, f"Error downloading image: {e}"
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# Priority 3: Uploaded file
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elif uploaded_file is not None:
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input_path = uploaded_file.name
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else:
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return None, None, "Please provide
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# Run pose detection (with save=True so annotated outputs are written to disk)
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try:
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except Exception as e:
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return None, None, f"Error running prediction: {e}"
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output_path = None
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try:
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# If the result has a save_path attribute, use it.
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if hasattr(results[0], "save_path"):
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output_path = results[0].save_path
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else:
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# Otherwise, use plot() to get an annotated image and save it.
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annotated = results[0].plot() # returns a numpy array
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output_path = os.path.join(tempfile.gettempdir(), "annotated.jpg")
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cv2.imwrite(output_path, annotated)
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except Exception as e:
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return None, None, f"Error processing the file: {e}"
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# Clean up temporary
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if (youtube_link or (image_url and image_url.strip())) and input_path and os.path.exists(input_path):
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os.remove(input_path)
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return output_path, output_path, "Success!"
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#
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with gr.Blocks(
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gr.Markdown("## Pose Detection with YOLO11-pose")
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)
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# Only launch the interface if the script is executed directly.
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if __name__ == "__main__":
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demo.launch()
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import requests
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from ultralytics import YOLO
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# Remove extra CLI arguments that Spaces might pass.
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sys.argv = [arg for arg in sys.argv if arg != "--import"]
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# Load the YOLO11-pose model (auto-downloads if needed)
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model = YOLO("yolo11n-pose.pt")
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def process_input(uploaded_file, youtube_link, image_url, sensitivity):
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"""
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Process input from one of the three methods (Upload, YouTube, Image URL).
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Priority: YouTube link > Image URL > Uploaded file.
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The sensitivity slider value is passed as the confidence threshold.
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Returns a tuple:
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(download_file_path, display_file_path, status_message, dummy_state)
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(The dummy_state is used because Gradio requires the same number of outputs.)
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"""
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input_path = None
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stream = yt.streams.filter(file_extension='mp4', progressive=True)\
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.order_by("resolution").desc().first()
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if stream is None:
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return None, None, "No suitable mp4 stream found.", ""
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input_path = stream.download()
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except Exception as e:
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return None, None, f"Error downloading video: {e}", ""
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# Priority 2: Image URL
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elif image_url and image_url.strip():
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try:
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response = requests.get(image_url, stream=True)
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if response.status_code != 200:
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return None, None, f"Error downloading image: HTTP {response.status_code}", ""
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temp_image_path = os.path.join(tempfile.gettempdir(), "downloaded_image.jpg")
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with open(temp_image_path, "wb") as f:
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f.write(response.content)
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input_path = temp_image_path
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except Exception as e:
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return None, None, f"Error downloading image: {e}", ""
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# Priority 3: Uploaded file
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elif uploaded_file is not None:
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input_path = uploaded_file.name
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else:
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return None, None, "Please provide an input using one of the methods.", ""
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try:
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# Pass the slider value as the confidence threshold.
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results = model.predict(source=input_path, save=True, conf=sensitivity)
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except Exception as e:
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return None, None, f"Error running prediction: {e}", ""
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output_path = None
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try:
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if hasattr(results[0], "save_path"):
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output_path = results[0].save_path
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else:
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annotated = results[0].plot() # returns a numpy array
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output_path = os.path.join(tempfile.gettempdir(), "annotated.jpg")
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cv2.imwrite(output_path, annotated)
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except Exception as e:
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return None, None, f"Error processing the file: {e}", ""
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# Clean up the temporary input if it was downloaded.
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if ((youtube_link and youtube_link.strip()) or (image_url and image_url.strip())) and input_path and os.path.exists(input_path):
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os.remove(input_path)
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return output_path, output_path, "Success!", ""
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# Build the Gradio interface with custom CSS for the result image.
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with gr.Blocks(css="""
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.result_img > img {
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width: 100%;
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height: auto;
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object-fit: contain;
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}
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""") as demo:
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# Header with scaled image (25% width) and title.
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gr.HTML("<div style='text-align:center;'><img src='crowdresult.jpg' style='width:25%;'/></div>")
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gr.Markdown("## Pose Detection with YOLO11-pose")
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# Create two columns.
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with gr.Row():
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# Left column: Input tabs and sensitivity slider.
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with gr.Column(scale=1):
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with gr.Tabs():
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with gr.TabItem("Upload File"):
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file_input = gr.File(label="Upload Image/Video")
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with gr.TabItem("YouTube Link"):
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youtube_input = gr.Textbox(label="YouTube Link", placeholder="https://...")
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with gr.TabItem("Image URL"):
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image_url_input = gr.Textbox(label="Image URL", placeholder="https://...")
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sensitivity_slider = gr.Slider(minimum=0.1, maximum=1.0, step=0.05, value=0.5,
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label="Sensitivity (Confidence Threshold)")
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# Right column: Display result.
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with gr.Column(scale=2):
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output_display = gr.Image(label="Annotated Output", elem_classes="result_img")
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output_file = gr.File(label="Download Annotated Output")
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output_text = gr.Textbox(label="Status", interactive=False)
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# Set up automatic triggers for each input type.
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file_input.change(
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fn=process_input,
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inputs=[file_input, gr.State(""), gr.State(""), sensitivity_slider],
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outputs=[output_file, output_display, output_text, gr.State()]
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)
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youtube_input.change(
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fn=process_input,
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inputs=[gr.State(None), youtube_input, gr.State(""), sensitivity_slider],
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outputs=[output_file, output_display, output_text, gr.State()]
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)
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image_url_input.change(
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fn=process_input,
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inputs=[gr.State(None), gr.State(""), image_url_input, sensitivity_slider],
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outputs=[output_file, output_display, output_text, gr.State()]
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)
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if __name__ == "__main__":
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demo.launch()
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