Update app.py

app.py

@@ -2,11 +2,11 @@ import gradio as gr
 import cv2
 from gradio_webrtc import WebRTC
 import os
-import mediapipe as mp
-from mediapipe.tasks import python
-from mediapipe.tasks.python import vision, BaseOptions
-from mediapipe import solutions
-from mediapipe.framework.formats import landmark_pb2
+# import mediapipe as mp
+# from mediapipe.tasks import python
+# from mediapipe.tasks.python import vision, BaseOptions
+# from mediapipe import solutions
+# from mediapipe.framework.formats import landmark_pb2
 import numpy as np
 import cv2
 from PIL import Image
@@ -14,43 +14,45 @@ from PIL import Image
 MODEL_PATH = r"pose_landmarker_lite.task"
 
 # Drawing landmarks
-def draw_landmarks_on_image(rgb_image, detection_result):
-    pose_landmarks_list = detection_result.pose_landmarks
-    annotated_image = np.copy(rgb_image)
+# def draw_landmarks_on_image(rgb_image, detection_result):
+#     pose_landmarks_list = detection_result.pose_landmarks
+#     annotated_image = np.copy(rgb_image)
 
-    for pose_landmarks in pose_landmarks_list:
-        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
+#     for pose_landmarks in pose_landmarks_list:
+#         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
 
 
-base_options = python.BaseOptions(delegate=0,model_asset_path=MODEL_PATH)
-options = vision.PoseLandmarkerOptions(
-    base_options=base_options,
-    output_segmentation_masks=True)
-detector = vision.PoseLandmarker.create_from_options(options)
+# base_options = python.BaseOptions(delegate=0,model_asset_path=MODEL_PATH)
+# options = vision.PoseLandmarkerOptions(
+#     base_options=base_options,
+#     output_segmentation_masks=True)
+# detector = vision.PoseLandmarker.create_from_options(options)
 
 
 
 def detection(image, conf_threshold=0.3):
     frame = cv2.flip(image, 1)
     rgb_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
-    mp_image = mp.Image(image_format=mp.ImageFormat.SRGB, data=rgb_frame)
+
+    rgb_frame=cv2.circle(rgb_frame,(90,90),(255,0,0),20)
+    # mp_image = mp.Image(image_format=mp.ImageFormat.SRGB, data=rgb_frame)
 
     # # Pose detection
-    detection_result = detector.detect(mp_image)
+    # detection_result = detector.detect(mp_image)
 
-        # Draw landmarks
-    annotated_image = draw_landmarks_on_image(mp_image.numpy_view(), detection_result)
+    #     # Draw landmarks
+    # annotated_image = draw_landmarks_on_image(mp_image.numpy_view(), detection_result)
 
-    return annotated_image
+    return rgb_frame
 
 
 with gr.Blocks() as demo:
@@ -69,4 +71,4 @@ with gr.Blocks() as demo:
     )
 
 if __name__ == "__main__":
-    demo.launch()
+    demo.launch(share=True)