Update app.py

app.py

@@ -1,3 +1,6 @@
+import gradio as gr
+from transformers import pipeline
+
 # Install mediapipe
 !pip install mediapipe
 
@@ -8,7 +11,7 @@ import numpy as np
 from time import time
 import mediapipe as mp
 import matplotlib.pyplot as plt
-
+from openpose import pyopenpose as op
 
 #*******************Initialize the Pose Detection Model*****************
 
@@ -131,6 +134,68 @@ mp_drawing = mp.solutions.drawing_utils
 #cv2.destroyAllWindows()
 
 
+#************************Create a Pose Detection Function*******************
+def detectPose(image, pose, display=True):
+    '''
+    This function performs pose detection on an image.
+    Args:
+        image: The input image with a prominent person whose pose landmarks needs to be detected.
+        pose: The pose setup function required to perform the pose detection.
+        display: A boolean value that is if set to true the function displays the original input image, the resultant image,
+                 and the pose landmarks in 3D plot and returns nothing.
+    Returns:
+        output_image: The input image with the detected pose landmarks drawn.
+        landmarks: A list of detected landmarks converted into their original scale.
+    '''
+
+    # Create a copy of the input image.
+    output_image = image.copy()
+
+    # Convert the image from BGR into RGB format.
+    imageRGB = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+
+    # Perform the Pose Detection.
+    results = pose.process(imageRGB)
+
+    # Retrieve the height and width of the input image.
+    height, width, _ = image.shape
+
+    # Initialize a list to store the detected landmarks.
+    landmarks = []
+
+    # Check if any landmarks are detected.
+    if results.pose_landmarks:
+
+        # Draw Pose landmarks on the output image.
+        mp_drawing.draw_landmarks(image=output_image, landmark_list=results.pose_landmarks,
+                                  connections=mp_pose.POSE_CONNECTIONS)
+
+        # Iterate over the detected landmarks.
+        for landmark in results.pose_landmarks.landmark:
+
+            # Append the landmark into the list.
+            landmarks.append((int(landmark.x * width), int(landmark.y * height),
+                                  (landmark.z * width)))
+
+    # Check if the original input image and the resultant image are specified to be displayed.
+    if display:
+
+        # Display the original input image and the resultant image.
+        plt.figure(figsize=[22,22])
+        plt.subplot(121);plt.imshow(image[:,:,::-1]);plt.title("Original Image");plt.axis('off');
+        plt.subplot(122);plt.imshow(output_image[:,:,::-1]);plt.title("Output Image");plt.axis('off');
+
+        # Also Plot the Pose landmarks in 3D.
+        mp_drawing.plot_landmarks(results.pose_world_landmarks, mp_pose.POSE_CONNECTIONS)
+
+    # Otherwise
+    else:
+
+        # Return the output image and the found landmarks.
+        return output_image, landmarks
+
+
+
 # ********************Pose Classification with Angle Heuristics*****************
 
 def calculateAngle(landmark1, landmark2, landmark3):
@@ -372,4 +437,13 @@ while camera_video.isOpened():
 # Release the VideoCapture object and close the windows.
 camera_video.release()
 cv2.destroyAllWindows()
-        
+
+
+# Create a Gradio interface
+iface = gr.Interface(
+    fn=detect_yoga_poses,
+    inputs=None,
+    outputs=None,
+    title="Live Yoga Pose Detection",
+    description="This app detects yoga poses from the live camera feed using MediaPipe.",
+)