Upload poseDetectionHugging.py

poseDetectionHugging.py

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+import cv2
+from cvzone.PoseModule import PoseDetector
+import gradio as gr
+
+# Initialize pose detector
+poseDetector = PoseDetector()
+
+# Function to process video and detect poses
+def process_video(video_path):
+    cap = cv2.VideoCapture(video_path)
+    posList = []
+    output_frames = []
+
+    while True:
+        success, img = cap.read()
+        if not success:
+            break
+
+        # Detect pose
+        img = poseDetector.findPose(img)
+        lmList, bboxInfo = poseDetector.findPosition(img)
+
+        if bboxInfo:
+            lmString = ''
+            for lm in lmList:
+                lmString += f'{lm[0]},{img.shape[0]-lm[1]},{lm[2]},'
+            posList.append(lmString)
+
+        # Convert frame to RGB for Gradio display
+        img_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
+        output_frames.append(img_rgb)
+
+    # Release video capture
+    cap.release()
+
+    # Save pose data to a file
+    with open("AnimationFile.txt", "w") as f:
+        f.writelines(["%s\n" % item for item in posList])
+
+    # Return the processed frames as a video
+    return output_frames
+
+# Gradio interface
+def gradio_interface(video):
+    frames = process_video(video)
+    return frames
+
+# Create Gradio app
+iface = gr.Interface(
+    fn=gradio_interface,
+    inputs=gr.Video(label="Input Video"),
+    outputs=gr.Video(label="Processed Video"),
+    title="Pose Detection with MediaPipe",
+    description="Upload a video to detect human poses using MediaPipe and OpenCV.",
+)
+
+# Launch the app
+iface.launch()