Spaces:
Sleeping
Sleeping
Commit ·
dc5cbbb
1
Parent(s): 7c70ded
removed hand pose estimation
Browse files
app.py
CHANGED
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@@ -6,13 +6,8 @@ import tempfile
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import yaml
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from src.utils import (
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create_video_from_images,
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process_hand_pose_estimation,
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process_body_pose_estimation,
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)
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from src.mediapipe_pose.get_landmarks_and_connections import (
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get_default_hand_landmark_style,
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get_default_hand_connection_style,
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)
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def hex_to_bgr(color_input):
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@@ -54,18 +49,16 @@ def hex_to_bgr(color_input):
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def process_video(
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video_file,
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task_type,
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landmark_radius,
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landmark_color,
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connection_color,
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connection_thickness,
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):
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"""
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Process video with pose estimation and return output video path.
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Args:
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video_file: Uploaded video file
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task_type: "hand_pose_estimation" or "body_pose_estimation"
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landmark_radius: Radius size for landmarks (int)
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landmark_color: Color for landmarks (hex string or tuple)
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connection_color: Color for connections (hex string or tuple)
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@@ -101,21 +94,12 @@ def process_video(
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print(f"Drawing settings: {drawing_settings}")
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# Process video
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-
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-
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-
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)
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elif task_type == "body_pose_estimation":
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process_body_pose_estimation(
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video_file,
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str(frames_dir),
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drawing_settings
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)
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else:
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raise ValueError(f"Unknown task type: {task_type}")
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# Create video from processed frames
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create_video_from_images(str(frames_dir), str(output_video_path), fps=30)
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@@ -130,21 +114,15 @@ def process_video(
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# Create Gradio interface
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with gr.Blocks(title="Pose Estimation") as demo:
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gr.Markdown("# Pose Estimation")
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gr.Markdown("Upload a video and process it with
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with gr.Row():
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with gr.Column():
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# Input controls
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gr.Markdown("## Input Settings")
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task_dropdown = gr.Radio(
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choices=["hand_pose_estimation", "body_pose_estimation"],
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value="hand_pose_estimation",
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label="Select Pose Estimation Task"
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)
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landmark_radius = gr.Number(
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value=20,
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label="Landmark Radius",
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@@ -154,14 +132,14 @@ with gr.Blocks(title="Pose Estimation") as demo:
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precision=0
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)
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landmark_color = gr.
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value="#B37CF7",
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label="Landmark Color (
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)
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connection_color = gr.
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value="#E1E1E1",
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label="Connection Color (
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)
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connection_thickness = gr.Number(
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@@ -207,14 +185,13 @@ with gr.Blocks(title="Pose Estimation") as demo:
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)
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# Handle processing
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def process_and_update(video,
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try:
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# Update status
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gr.Info("Processing video... This may take a few minutes.")
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output_path = process_video(
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video,
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task,
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int(radius),
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land_color,
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conn_color,
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@@ -229,7 +206,7 @@ with gr.Blocks(title="Pose Estimation") as demo:
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process_button.click(
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fn=process_and_update,
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inputs=[video_upload,
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outputs=[video_output, download_button]
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)
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import yaml
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from src.utils import (
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create_video_from_images,
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process_body_pose_estimation,
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)
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def hex_to_bgr(color_input):
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def process_video(
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video_file,
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landmark_radius,
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landmark_color,
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connection_color,
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connection_thickness,
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):
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"""
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+
Process video with body pose estimation and return output video path.
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Args:
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video_file: Uploaded video file
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landmark_radius: Radius size for landmarks (int)
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landmark_color: Color for landmarks (hex string or tuple)
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connection_color: Color for connections (hex string or tuple)
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print(f"Drawing settings: {drawing_settings}")
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# Process video with body pose estimation
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process_body_pose_estimation(
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video_file,
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str(frames_dir),
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drawing_settings
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)
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# Create video from processed frames
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create_video_from_images(str(frames_dir), str(output_video_path), fps=30)
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# Create Gradio interface
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with gr.Blocks(title="Body Pose Estimation") as demo:
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gr.Markdown("# Body Pose Estimation")
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gr.Markdown("Upload a video and process it with body pose estimation")
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with gr.Row():
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with gr.Column():
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# Input controls
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gr.Markdown("## Input Settings")
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landmark_radius = gr.Number(
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value=20,
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label="Landmark Radius",
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precision=0
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)
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landmark_color = gr.Textbox(
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value="#B37CF7",
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label="Landmark Color (hex, e.g. #B37CF7)"
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)
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connection_color = gr.Textbox(
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value="#E1E1E1",
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label="Connection Color (hex, e.g. #E1E1E1)"
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)
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connection_thickness = gr.Number(
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)
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# Handle processing
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def process_and_update(video, radius, land_color, conn_color, conn_thickness):
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try:
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# Update status
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gr.Info("Processing video... This may take a few minutes.")
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output_path = process_video(
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video,
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int(radius),
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land_color,
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conn_color,
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process_button.click(
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fn=process_and_update,
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inputs=[video_upload, landmark_radius, landmark_color, connection_color, connection_thickness],
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outputs=[video_output, download_button]
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)
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