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| import gradio as gr | |
| from mediapipe import solutions | |
| from mediapipe.framework.formats import landmark_pb2 | |
| import numpy as np | |
| import mediapipe as mp | |
| from mediapipe.tasks import python | |
| from mediapipe.tasks.python import vision | |
| def draw_landmarks_on_image(rgb_image, detection_result): | |
| pose_landmarks_list = detection_result.pose_landmarks | |
| annotated_image = np.copy(rgb_image) | |
| # Loop through the detected poses to visualize. | |
| for idx in range(len(pose_landmarks_list)): | |
| pose_landmarks = pose_landmarks_list[idx] | |
| # Draw the pose landmarks. | |
| pose_landmarks_proto = landmark_pb2.NormalizedLandmarkList() | |
| pose_landmarks_proto.landmark.extend([ | |
| landmark_pb2.NormalizedLandmark(x=landmark.x, y=landmark.y, z=landmark.z) for landmark in pose_landmarks | |
| ]) | |
| solutions.drawing_utils.draw_landmarks( | |
| annotated_image, | |
| pose_landmarks_proto, | |
| solutions.pose.POSE_CONNECTIONS, | |
| solutions.drawing_styles.get_default_pose_landmarks_style()) | |
| return annotated_image | |
| def app(image): | |
| try: | |
| image_annotated = section(image) | |
| pass | |
| except Exception as e: | |
| return gr.Error(f"An error occurred: {str(e)}") | |
| return image_annotated | |
| with gr.Blocks() as demo: | |
| with gr.Row(): | |
| ## Input | |
| image = gr.Image(label="Input Image") | |
| ## Output | |
| image_annotated = gr.Image(label="Output Image") | |
| with gr.Row(): | |
| detection_results = gr.Text("Pose Landmarks") | |
| section_btn = gr.Button("Identify keypoints") | |
| def section(image): | |
| base_options = python.BaseOptions(model_asset_path='pose_landmarker_heavy.task') | |
| options = vision.PoseLandmarkerOptions( | |
| base_options=base_options, | |
| output_segmentation_masks=True) | |
| detector = vision.PoseLandmarker.create_from_options(options) | |
| # STEP 3: Load the input image. | |
| # image = mp.Image.create_from_file(image) | |
| # image = mp.Image(ImageFormat.SRGB, image) | |
| # image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR) | |
| # image_bgr = cv2.cvtColor(image, cv2.COLOR_RGB2BGR) | |
| # Create a MediaPipe image using the BGR image | |
| # Using 1 as the image format for BGR (which is the integer representation for mediapipe::ImageFormat::SRGB) | |
| print(type(image)) | |
| mediapipe_image = mp.Image(image_format=mp.ImageFormat.SRGB, data=image) | |
| # STEP 4: Detect pose landmarks from the input image. | |
| detection_result = detector.detect(mediapipe_image) | |
| # STEP 5: Process the detection result. In this case, visualize it. | |
| annotated_image = draw_landmarks_on_image(image, detection_result) | |
| # cv2.imshow(cv2.cvtColor(annotated_image, cv2.COLOR_RGB2BGR)) | |
| return annotated_image, detection_result | |
| section_btn.click(section, image, [image_annotated, detection_results]) | |
| demo.launch(share=True) | |
| # demo.queue(max_size=99).launch(debug=True)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 |