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app.py

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+import gradio as gr
+import cv2
+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.queue(max_size=99).launch(debug=True)

requirements.txt

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+aiofiles==0.8.0
+anyio==3.6.1
+argcomplete==2.0.0
+backports.cached-property==1.0.2
+boto3==1.24.31
+botocore==1.27.31
+certifi==2022.9.24
+charset-normalizer==2.1.0
+click==8.1.3
+cycler==0.11.0
+dacite==1.6.0
+Deprecated==1.2.13
+dill==0.3.5.1
+dnspython==2.2.1
+eventlet==0.33.1
+fiftyone==0.16.5
+fiftyone-brain==0.8.2
+fiftyone-db==0.3.0
+fonttools==4.34.4
+future==0.18.2
+glob2==0.7
+graphql-core==3.1.7
+greenlet==1.1.2
+h11==0.12.0
+h2==4.1.0
+hpack==4.0.0
+httpcore==0.15.0
+httpx==0.23.0
+hypercorn==0.13.2
+hyperframe==6.0.1
+idna==3.3
+imageio==2.19.5
+Jinja2==3.1.2
+jmespath==1.0.1
+joblib==1.1.0
+kaleido==0.2.1
+kiwisolver==1.4.4
+MarkupSafe==2.1.1
+matplotlib==3.5.2
+mongoengine==0.20.0
+motor==2.5.1
+ndjson==0.3.1
+networkx==2.8.4
+numpy==1.23.1
+opencv-python-headless==4.6.0.66
+packaging==21.3
+pandas==1.4.3
+patool==1.12
+Pillow==9.2.0
+plotly==4.14.3
+pprintpp==0.4.0
+priority==2.0.0
+psutil==5.9.1
+Pygments==2.12.0
+pymongo==3.12.3
+pyparsing==3.0.9
+python-dateutil==2.8.2
+python-multipart==0.0.5
+pytz==2022.1
+pytz-deprecation-shim==0.1.0.post0
+PyWavelets==1.3.0
+PyYAML==6.0
+requests==2.28.1
+retrying==1.3.3
+rfc3986==1.5.0
+s3transfer==0.6.0
+scikit-image==0.19.3
+scikit-learn==1.1.1
+scipy==1.8.1
+sentinel==0.3.0
+six==1.16.0
+sniffio==1.2.0
+sortedcontainers==2.4.0
+sse-starlette==0.10.3
+sseclient-py==1.7.2
+starlette==0.16.0
+strawberry-graphql==0.96.0
+tabulate==0.8.10
+threadpoolctl==3.1.0
+tifffile==2022.5.4
+toml==0.10.2
+typing_extensions==4.3.0
+tzdata==2022.1
+tzlocal==4.2
+universal-analytics-python3==1.1.1
+urllib3==1.26.10
+voxel51-eta==0.7.1
+wrapt==1.14.1
+wsproto==1.1.0
+xmltodict==0.13.0