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Create app.py
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app.py
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from io import BytesIO
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import cv2
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import gradio as gr
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import numpy as np
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import requests
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from PIL import Image
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from super_gradients.common.object_names import Models
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from super_gradients.training import models
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from super_gradients.training.utils.visualization.detection import draw_bbox
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from super_gradients.training.utils.visualization.pose_estimation import PoseVisualization
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# Initialize your pose estimation model
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yolo_nas_pose = models.get("yolo_nas_pose_l",
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num_classes=17,
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checkpoint_path="/content/yolo_nas_pose_l_coco_pose.pth")
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def process_and_predict(url=None,
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image=None,
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confidence=0.5,
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iou=0.5):
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# If a URL is provided, use it directly for prediction
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if url is not None and url.strip() != "":
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response = requests.get(url)
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image = Image.open(BytesIO(response.content))
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image = np.array(image)
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result = yolo_nas_pose.predict(image, conf=confidence,iou=iou)
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# If a file is uploaded, read it, convert it to a numpy array and use it for prediction
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elif image is not None:
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result = yolo_nas_pose.predict(image, conf=confidence,iou=iou)
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else:
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return None # If no input is provided, return None
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# Extract prediction data
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image_prediction = result._images_prediction_lst[0]
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pose_data = image_prediction.prediction
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# Visualize the prediction
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output_image = PoseVisualization.draw_poses(
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image=image_prediction.image,
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poses=pose_data.poses,
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boxes=pose_data.bboxes_xyxy,
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scores=pose_data.scores,
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is_crowd=None,
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edge_links=pose_data.edge_links,
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edge_colors=pose_data.edge_colors,
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keypoint_colors=pose_data.keypoint_colors,
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joint_thickness=2,
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box_thickness=2,
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keypoint_radius=5
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)
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blank_image = np.zeros_like(image_prediction.image)
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skeleton_image = PoseVisualization.draw_poses(
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image=blank_image,
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poses=pose_data.poses,
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boxes=pose_data.bboxes_xyxy,
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scores=pose_data.scores,
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is_crowd=None,
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edge_links=pose_data.edge_links,
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edge_colors=pose_data.edge_colors,
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keypoint_colors=pose_data.keypoint_colors,
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joint_thickness=2,
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box_thickness=2,
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keypoint_radius=5
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)
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# Convert the resulting visualization to a PIL Image
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# output_image_pil = Image.fromarray(output_image.astype('uint8'), 'RGB')
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# Return the PIL Image directly
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return output_image, skeleton_image
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# Define the Gradio interface
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iface = gr.Interface(
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fn=process_and_predict,
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inputs=[
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gr.Textbox(placeholder="Enter Image URL", label="Image URL"),
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gr.Image(label="Upload Image", type='numpy'),
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gr.Slider(minimum=0, maximum=1, step=0.01, value=0.5, label="Confidence Threshold"),
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gr.Slider(minimum=0, maximum=1, step=0.01, value=0.5, label="IoU Threshold")
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],
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outputs=[
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gr.components.Image(label="Estimated Pose"),
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gr.components.Image(label="Skeleton Only")
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],
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title="YOLO-NAS-Pose Demo",
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description="Upload an image, enter an image URL, or use your webcam to use a pretrained YOLO-NAS-Pose L for inference. You can check out the ",
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live=False,
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allow_flagging=False,
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)
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# Launch the interface
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iface.launch()
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