Update app.py

app.py

@@ -1,233 +1,43 @@
-#!/usr/bin/env python
-
-from __future__ import annotations
-
-import pathlib
-import math
-
-import gradio as gr
-import cv2
-import mediapipe as mp
+from flask import Flask, request, jsonify
+from PIL import Image
+import io
+import base64
+import logging
 import numpy as np
 
-mp_drawing = mp.solutions.drawing_utils
-mp_drawing_styles = mp.solutions.drawing_styles
-mp_pose = mp.solutions.pose
-
-TITLE = "MediaPipe Human Pose Estimation"
-DESCRIPTION = "https://google.github.io/mediapipe/"
-
-
-def calculateAngle(landmark1, landmark2, landmark3):
-    '''
-    This function calculates angle between three different landmarks.
-    Args:
-        landmark1: The first landmark containing the x,y and z coordinates.
-        landmark2: The second landmark containing the x,y and z coordinates.
-        landmark3: The third landmark containing the x,y and z coordinates.
-    Returns:
-        angle: The calculated angle between the three landmarks.
-
-    '''
-
-    # Get the required landmarks coordinates.
-    x1, y1 = landmark1.x, landmark1.y
-    x2, y2 = landmark2.x, landmark2.y
-    x3, y3 = landmark3.x, landmark3.y
-
-       # Calculate the angle between the three points
-    angle = math.degrees(math.atan2(y3 - y2, x3 - x2) - math.atan2(y1 - y2, x1 - x2))
-   # angle = abs(angle) # Convert the angle to an absolute value.
-
-
-    # Check if the angle is less than zero.
-    if angle < 0:
-
-        # Add 360 to the found angle.
-        angle += 360
-
-    # Return the calculated angle.
-    return angle
-
-def classifyPose(landmarks, output_image, display=False):
-    '''
-    This function classifies yoga poses depending upon the angles of various body joints.
-    Args:
-        landmarks: A list of detected landmarks of the person whose pose needs to be classified.
-        output_image: A image of the person with the detected pose landmarks drawn.
-        display: A boolean value that is if set to true the function displays the resultant image with the pose label
-        written on it and returns nothing.
-    Returns:
-        output_image: The image with the detected pose landmarks drawn and pose label written.
-        label: The classified pose label of the person in the output_image.
-
-    '''
-
-    # Initialize the label of the pose. It is not known at this stage.
-    label = 'Unknown Pose'
-
-    # Specify the color (Red) with which the label will be written on the image.
-    color = (0, 0, 255)
-
-    # Calculate the required angles.
-    #----------------------------------------------------------------------------------------------------------------
-
-    # Get the angle between the left shoulder, elbow and wrist points.
-    left_elbow_angle = calculateAngle(landmarks[mp_pose.PoseLandmark.LEFT_SHOULDER.value],
-                                      landmarks[mp_pose.PoseLandmark.LEFT_ELBOW.value],
-                                      landmarks[mp_pose.PoseLandmark.LEFT_WRIST.value])
-
-    # Get the angle between the right shoulder, elbow and wrist points.
-    right_elbow_angle = calculateAngle(landmarks[mp_pose.PoseLandmark.RIGHT_SHOULDER.value],
-                                       landmarks[mp_pose.PoseLandmark.RIGHT_ELBOW.value],
-                                       landmarks[mp_pose.PoseLandmark.RIGHT_WRIST.value])
-
-    # Get the angle between the left elbow, shoulder and hip points.
-    left_shoulder_angle = calculateAngle(landmarks[mp_pose.PoseLandmark.LEFT_ELBOW.value],
-                                         landmarks[mp_pose.PoseLandmark.LEFT_SHOULDER.value],
-                                         landmarks[mp_pose.PoseLandmark.LEFT_HIP.value])
-
-    # Get the angle between the right hip, shoulder and elbow points.
-    right_shoulder_angle = calculateAngle(landmarks[mp_pose.PoseLandmark.RIGHT_HIP.value],
-                                          landmarks[mp_pose.PoseLandmark.RIGHT_SHOULDER.value],
-                                          landmarks[mp_pose.PoseLandmark.RIGHT_ELBOW.value])
-
-    # Get the angle between the left hip, knee and ankle points.
-    left_knee_angle = calculateAngle(landmarks[mp_pose.PoseLandmark.LEFT_HIP.value],
-                                     landmarks[mp_pose.PoseLandmark.LEFT_KNEE.value],
-                                     landmarks[mp_pose.PoseLandmark.LEFT_ANKLE.value])
-
-    # Get the angle between the right hip, knee and ankle points
-    right_knee_angle = calculateAngle(landmarks[mp_pose.PoseLandmark.RIGHT_HIP.value],
-                                      landmarks[mp_pose.PoseLandmark.RIGHT_KNEE.value],
-                                      landmarks[mp_pose.PoseLandmark.RIGHT_ANKLE.value])
-
-    #----------------------------------------------------------------------------------------------------------------
-     # Check for Five-Pointed Star Pose
-    if abs(landmarks[mp_pose.PoseLandmark.LEFT_WRIST.value].y - landmarks[mp_pose.PoseLandmark.LEFT_HIP.value].y) < 100 and \
-       abs(landmarks[mp_pose.PoseLandmark.RIGHT_WRIST.value].y - landmarks[mp_pose.PoseLandmark.RIGHT_HIP.value].y) < 100 and \
-       abs(landmarks[mp_pose.PoseLandmark.LEFT_ANKLE.value].x - landmarks[mp_pose.PoseLandmark.RIGHT_ANKLE.value].x) > 200 and \
-       abs(landmarks[mp_pose.PoseLandmark.LEFT_WRIST.value].x - landmarks[mp_pose.PoseLandmark.RIGHT_WRIST.value].x) > 200:
-        label = "Five-Pointed Star Pose"  
-    
-    # Check if it is the warrior II pose or the T pose.
-    if left_elbow_angle > 165 and left_elbow_angle < 195 and right_elbow_angle > 165 and right_elbow_angle < 195:
-        if left_shoulder_angle > 80 and left_shoulder_angle < 110 and right_shoulder_angle > 80 and right_shoulder_angle < 110:
-            if left_knee_angle > 165 and left_knee_angle < 195 or right_knee_angle > 165 and right_knee_angle < 195:
-                if left_knee_angle > 90 and left_knee_angle < 120 or right_knee_angle > 90 and right_knee_angle < 120:
-                    label = 'Warrior II Pose'
-            if left_knee_angle > 160 and left_knee_angle < 195 and right_knee_angle > 160 and right_knee_angle < 195:
-                label = 'T Pose'
-    
-    # Check if it is the tree pose.
-    if left_knee_angle > 165 and left_knee_angle < 195 or right_knee_angle > 165 and right_knee_angle < 195:
-        if left_knee_angle > 315 and left_knee_angle < 335 or right_knee_angle > 25 and right_knee_angle < 45:
-            label = 'Tree Pose'
-
-    # Check for Upward Salute Pose
-    if abs(landmarks[mp_pose.PoseLandmark.LEFT_WRIST.value].x - landmarks[mp_pose.PoseLandmark.LEFT_HIP.value].x) < 100 and \
-       abs(landmarks[mp_pose.PoseLandmark.RIGHT_WRIST.value].x - landmarks[mp_pose.PoseLandmark.RIGHT_HIP.value].x) < 100 and \
-       landmarks[mp_pose.PoseLandmark.LEFT_WRIST.value].y < landmarks[mp_pose.PoseLandmark.LEFT_SHOULDER.value].y and \
-       landmarks[mp_pose.PoseLandmark.RIGHT_WRIST.value].y < landmarks[mp_pose.PoseLandmark.RIGHT_SHOULDER.value].y and \
-       abs(landmarks[mp_pose.PoseLandmark.LEFT_SHOULDER.value].y - landmarks[mp_pose.PoseLandmark.RIGHT_SHOULDER.value].y) < 50:
-        label = "Upward Salute Pose"
-
-    # Check for Hands Under Feet Pose
-    if landmarks[mp_pose.PoseLandmark.LEFT_WRIST.value].y > landmarks[mp_pose.PoseLandmark.LEFT_KNEE.value].y and \
-       landmarks[mp_pose.PoseLandmark.RIGHT_WRIST.value].y > landmarks[mp_pose.PoseLandmark.RIGHT_KNEE.value].y and \
-       abs(landmarks[mp_pose.PoseLandmark.LEFT_WRIST.value].x - landmarks[mp_pose.PoseLandmark.LEFT_ANKLE.value].x) < 50 and \
-       abs(landmarks[mp_pose.PoseLandmark.RIGHT_WRIST.value].x - landmarks[mp_pose.PoseLandmark.RIGHT_ANKLE.value].x) < 50:
-        label = "Hands Under Feet Pose"       
+app = Flask(__name__)
+
+# Dummy pose recognition function
+def recognize_pose(image):
+    # Replace with your model inference code
+    pose = "warrior"  # Example dummy pose
+    return pose
+
+@app.route('/api/recognize_pose', methods=['POST'])
+def api_recognize_pose():
+    try:
+        # Decode the image from the request
+        image_data = request.json['image'].split(",")[1]
+        image_bytes = base64.b64decode(image_data)
+        image = Image.open(io.BytesIO(image_bytes))
         
-       
-    #----------------------------------------------------------------------------------------------------------------
-
-    # Check if the pose is classified successfully
-    if label != 'Unknown Pose':
-
-        # Update the color (to green) with which the label will be written on the image.
-        color = (0, 255, 0)
-
-    # Write the label on the output image.
-    cv2.putText(output_image, label, (220, 30),cv2.FONT_HERSHEY_PLAIN, 2, color, 2)
-
-    # Check if the resultant image is specified to be displayed.
-    if display:
-
-        # Display the resultant image.
-        plt.figure(figsize=[10,10])
-        plt.imshow(output_image[:,:,::-1]);plt.title("Output Image");plt.axis('off');
-
-    else:
-
-        # Return the output image and the classified label.
-        return output_image, label
-
-
-def run(
-    image: np.ndarray,
-    model_complexity: int,
-    enable_segmentation: bool,
-    min_detection_confidence: float,
-    background_color: str,
-) -> np.ndarray:
-    with mp_pose.Pose(
-        static_image_mode=True,
-        model_complexity=model_complexity,
-        enable_segmentation=enable_segmentation,
-        min_detection_confidence=min_detection_confidence,
-    ) as pose:
-        results = pose.process(image)
-
-    res = image[:, :, ::-1].copy()
-    if enable_segmentation:
-        if background_color == "white":
-            bg_color = 255
-        elif background_color == "black":
-            bg_color = 0
-        elif background_color == "green":
-            bg_color = (0, 255, 0)  # type: ignore
-        else:
-            raise ValueError
-
-        if results.segmentation_mask is not None:
-            res[results.segmentation_mask <= 0.1] = bg_color
-        else:
-            res[:] = bg_color
-
-    mp_drawing.draw_landmarks(
-        res,
-        results.pose_landmarks,
-        mp_pose.POSE_CONNECTIONS,
-        landmark_drawing_spec=mp_drawing_styles.get_default_pose_landmarks_style(),
-    )
-
-    if results.pose_landmarks:
-        res, pose_classification = classifyPose(results.pose_landmarks.landmark, res) #Pose Classification code
-
-    return res[:, :, ::-1]
-
-
-model_complexities = list(range(3))
-background_colors = ["white", "black", "green"]
-
-image_paths = sorted(pathlib.Path("images").rglob("*.jpg"))
-examples = [[path, model_complexities[1], True, 0.5, background_colors[0]] for path in image_paths]
-
-demo = gr.Interface(
-    fn=run,
-    inputs=[
-        gr.Image(label="Input", type="numpy"),
-        gr.Radio(label="Model Complexity", choices=model_complexities, type="index", value=model_complexities[1]),
-        gr.Checkbox(label="Enable Segmentation", value=True),
-        gr.Slider(label="Minimum Detection Confidence", minimum=0, maximum=1, step=0.05, value=0.5),
-        gr.Radio(label="Background Color", choices=background_colors, type="value", value=background_colors[0]),
-    ],
-    outputs=gr.Image(label="Output"),
-    examples=examples,
-    title=TITLE,
-    description=DESCRIPTION,
- )
-
-if __name__ == "__main__":
-    demo.queue().launch()
+        # Preprocess and recognize pose
+        image = preprocess_image(image)
+        pose = recognize_pose(image)
+        return jsonify({'pose': pose})
+    except KeyError as ke:
+        logging.error(f"Key Error: {ke}")
+        return jsonify({'error': 'Invalid input data format'}), 400
+    except Exception as e:
+        logging.error(f"Unexpected error: {e}")
+        return jsonify({'error': str(e)}), 500
+
+def preprocess_image(image):
+    # Example preprocessing; adjust as needed for your model
+    image = image.resize((224, 224))  # Resize image to expected input size
+    image = np.array(image) / 255.0  # Normalize image
+    image = np.expand_dims(image, axis=0)  # Add batch dimension
+    return image
+
+if __name__ == '__main__':
+    app.run(debug=True)