Upload 3 files

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+pose_landmarker_heavy.task filter=lfs diff=lfs merge=lfs -text

app.py

@@ -0,0 +1,85 @@
+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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

pose_landmarker_heavy.task

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:64437af838a65d18e5ba7a0d39b465540069bc8aae8308de3e318aad31fcbc7b
+size 30664242

requirements.txt

@@ -0,0 +1,3 @@
+gradio~=4.12.0
+numpy~=1.26.2
+mediapipe~=0.10.9