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