change similarity function
This commit is contained in:
kicap
parent
2e7c0ed8bc
commit
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Before Width: | Height: | Size: 211 KiB After Width: | Height: | Size: 211 KiB |
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Before Width: | Height: | Size: 193 KiB After Width: | Height: | Size: 193 KiB |
40
app.py
40
app.py
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@ -4,7 +4,7 @@ import cv2
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import json
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import json
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import mediapipe as mp
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import mediapipe as mp
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import numpy as np
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import numpy as np
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from scipy.spatial import distance
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from scipy.spatial import procrustes,distance
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import base64
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import base64
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import os
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import os
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@ -52,14 +52,42 @@ def calculate_color(similarity):
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green = int(normalized_similarity * 255)
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green = int(normalized_similarity * 255)
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return (0, green, red)
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return (0, green, red)
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# def calculate_similarity(landmarks1, landmarks2):
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# if not landmarks1 or not landmarks2:
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# return 0
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# norm_landmarks1 = np.array(landmarks1) - np.mean(landmarks1, axis=0)
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# norm_landmarks2 = np.array(landmarks2) - np.mean(landmarks2, axis=0)
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# dists = [distance.euclidean(lm1, lm2) for lm1, lm2 in zip(norm_landmarks1, norm_landmarks2)]
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# similarity = 1 / (1 + np.mean(dists))
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# return similarity * 100
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def calculate_similarity(landmarks1, landmarks2):
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def calculate_similarity(landmarks1, landmarks2):
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if not landmarks1 or not landmarks2:
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if not landmarks1 or not landmarks2:
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return 0
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return 0
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norm_landmarks1 = np.array(landmarks1) - np.mean(landmarks1, axis=0)
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norm_landmarks2 = np.array(landmarks2) - np.mean(landmarks2, axis=0)
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# Convert to numpy arrays
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dists = [distance.euclidean(lm1, lm2) for lm1, lm2 in zip(norm_landmarks1, norm_landmarks2)]
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landmarks1 = np.array(landmarks1)
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similarity = 1 / (1 + np.mean(dists))
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landmarks2 = np.array(landmarks2)
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return similarity * 100
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# Normalize landmarks by removing the mean
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norm_landmarks1 = landmarks1 - np.mean(landmarks1, axis=0)
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norm_landmarks2 = landmarks2 - np.mean(landmarks2, axis=0)
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# Perform Procrustes analysis to align the shapes
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_, mtx1, mtx2 = procrustes(norm_landmarks1, norm_landmarks2)
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# Calculate the Euclidean distances between corresponding landmarks
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dists = np.linalg.norm(mtx1 - mtx2, axis=1)
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# Calculate the similarity as the inverse of the average distance
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avg_dist = np.mean(dists)
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similarity = max(0, 1 - avg_dist)
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# Scale the similarity to a percentage
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similarity_percentage = similarity * 100
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return similarity_percentage
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def draw_landmarks(image, landmarks):
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def draw_landmarks(image, landmarks):
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global similarity
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global similarity
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@ -0,0 +1,212 @@
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from flask import Flask, render_template, Response, request, jsonify, send_file
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from flask_socketio import SocketIO
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import cv2
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import json
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import mediapipe as mp
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import numpy as np
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from scipy.spatial import procrustes
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import base64
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import os
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app = Flask(__name__)
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socketio = SocketIO(app)
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mp_pose = mp.solutions.pose
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mp_drawing = mp.solutions.drawing_utils
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image_name = "Camel"
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image = None
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# Extract landmarks from JSON data
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landmarks_from_json = []
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the_landmarks = None
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dataset = {"name": "", "ket": ""}
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similarity = 0
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all_data = []
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def load_image_and_landmarks(image_name):
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global image, landmarks_from_json, the_landmarks, all_data
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landmarks_from_json = [] # Clear previous landmarks
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# Load JSON data
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with open('data_yoga.json') as f:
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data = json.load(f)
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all_data = data
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# Load the image and landmarks
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for the_data in data:
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if the_data['name'] == image_name:
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for lm in the_data['landmarks']:
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landmarks_from_json.append([lm['coordinates'][0], lm['coordinates'][1]])
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the_landmarks = the_data['landmarks']
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image = cv2.imread(the_data['image_name'])
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dataset["name"] = the_data['name']
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dataset["ket"] = the_data['ket']
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# Define the function to calculate the color based on similarity
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def calculate_color(similarity):
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if similarity < 70:
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return (0, 0, 255) # Red
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else:
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normalized_similarity = (similarity - 55) / 45 # Normalize between 0 and 1 for values 71 to 100
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red = int((1 - normalized_similarity) * 255)
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green = int(normalized_similarity * 255)
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return (0, green, red)
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def resample_landmarks(landmarks, target_length):
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idxs = np.linspace(0, len(landmarks) - 1, target_length).astype(int)
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return [landmarks[i] for i in idxs]
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def calculate_similarity(landmarks1, landmarks2):
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if not landmarks1 or not landmarks2:
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return 0
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len1 = len(landmarks1)
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len2 = len(landmarks2)
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# Resample landmarks to ensure they have the same number of points
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if len1 != len2:
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if len1 < len2:
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landmarks2 = resample_landmarks(landmarks2, len1)
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else:
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landmarks1 = resample_landmarks(landmarks1, len2)
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# Convert to numpy arrays
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landmarks1 = np.array(landmarks1)
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landmarks2 = np.array(landmarks2)
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# Normalize landmarks by removing the mean
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norm_landmarks1 = landmarks1 - np.mean(landmarks1, axis=0)
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norm_landmarks2 = landmarks2 - np.mean(landmarks2, axis=0)
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# Perform Procrustes analysis to align the shapes
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_, mtx1, mtx2 = procrustes(norm_landmarks1, norm_landmarks2)
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# Calculate the Euclidean distances between corresponding landmarks
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dists = np.linalg.norm(mtx1 - mtx2, axis=1)
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# Calculate the similarity as the inverse of the average distance
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avg_dist = np.mean(dists)
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similarity = max(0, 1 - avg_dist)
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# Scale the similarity to a percentage
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similarity_percentage = similarity * 100
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return similarity_percentage
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def draw_landmarks(image, landmarks):
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global similarity
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annotated_image = image.copy()
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for landmark in landmarks:
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landmark_x = int(landmark['coordinates'][0] * annotated_image.shape[1])
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landmark_y = int(landmark['coordinates'][1] * annotated_image.shape[0])
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cv2.circle(annotated_image, (landmark_x, landmark_y), 5, (0, 255, 0), -1)
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connections = mp_pose.POSE_CONNECTIONS
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for connection in connections:
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start_idx = connection[0]
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end_idx = connection[1]
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if 0 <= start_idx < len(landmarks) and 0 <= end_idx < len(landmarks):
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start_landmark = landmarks[start_idx]['coordinates']
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end_landmark = landmarks[end_idx]['coordinates']
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start_x = int(start_landmark[0] * annotated_image.shape[1])
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start_y = int(start_landmark[1] * annotated_image.shape[0])
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end_x = int(end_landmark[0] * annotated_image.shape[1])
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end_y = int(end_landmark[1] * annotated_image.shape[0])
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cv2.line(annotated_image, (start_x, start_y), (end_x, end_y), (0, 255, 0), 2)
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return annotated_image
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def generate_frames():
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global similarity
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cap = cv2.VideoCapture(0)
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with mp_pose.Pose(min_detection_confidence=0.5, min_tracking_confidence=0.5) as pose:
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while cap.isOpened():
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ret, frame = cap.read()
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if not ret:
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break
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image_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
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image_rgb.flags.writeable = False
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results = pose.process(image_rgb)
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image_rgb.flags.writeable = True
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image_bgr = cv2.cvtColor(image_rgb, cv2.COLOR_RGB2BGR)
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the_color = calculate_color(similarity)
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if results.pose_landmarks:
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mp_drawing.draw_landmarks(
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image_bgr,
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results.pose_landmarks,
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mp_pose.POSE_CONNECTIONS,
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mp_drawing.DrawingSpec(color=(0, 255, 0), thickness=4, circle_radius=2),
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mp_drawing.DrawingSpec(color=(the_color), thickness=4, circle_radius=2),
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)
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landmarks_from_webcam = []
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for lm in results.pose_landmarks.landmark:
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landmarks_from_webcam.append([lm.x, lm.y])
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similarity = calculate_similarity(landmarks_from_json, landmarks_from_webcam)
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cv2.putText(image_bgr, f'Similarity: {similarity:.2f}%', (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)
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ret, buffer = cv2.imencode('.jpg', image_bgr)
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frame = buffer.tobytes()
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yield (b'--frame\r\n'
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b'Content-Type: image/jpeg\r\n\r\n' + frame + b'\r\n')
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cap.release()
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@app.route('/')
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def index():
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image_name = request.args.get('image_name', 'Camel')
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previous = None
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next = None
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load_image_and_landmarks(image_name)
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current_index = 0
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for index, data in enumerate(all_data):
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if data['name'] == image_name:
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current_index = index
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break
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if current_index == 0:
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previous = all_data[-1]['name']
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else:
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previous = all_data[current_index - 1]['name']
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if current_index == len(all_data) - 1:
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next = all_data[0]['name']
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else:
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next = all_data[current_index + 1]['name']
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annotated_image = draw_landmarks(image, the_landmarks)
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_, buffer = cv2.imencode('.jpg', annotated_image)
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img_str = base64.b64encode(buffer).decode('utf-8')
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return render_template('index2.html', img_str=img_str, previous=previous, next=next)
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@app.route('/video_feed')
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def video_feed():
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return Response(generate_frames(), mimetype='multipart/x-mixed-replace; boundary=frame')
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@app.route('/getdata', methods=['GET'])
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def getdata():
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return jsonify(all_data)
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@app.route('/similarity', methods=['GET'])
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def get_similarity():
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global similarity
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return {'similarity': similarity, 'data': dataset}
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@app.route('/pose_dataset')
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def pose_dataset():
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load_image_and_landmarks("Camel")
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return render_template('pose_dataset.html', data=all_data)
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@app.route('/show_image')
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def show_image():
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image_path = request.args.get('image_path')
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if image_path and os.path.exists(image_path):
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return send_file(image_path, mimetype='image/jpeg')
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else:
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return "Image not found", 404
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if __name__ == '__main__':
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socketio.run(app, debug=True)
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File diff suppressed because it is too large
Load Diff
544
data_yoga.json
544
data_yoga.json
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@ -812,548 +812,8 @@
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}
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}
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]
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]
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},
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},
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{
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"image_name": "gerakan/SidePlank.jpg",
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"name": "Side Plank",
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"ket": "Side Plank adalah pose yoga yang bagus untuk pria yang ingin merasakan kekuatan saat berlatih yoga. Karena yoga lebih dari sekadar peregangan, pose ini sebenarnya cukup menantang bagi banyak pria untuk menjadi stabil sehingga merupakan tantangan sempurna untuk membangun kekuatan dan keseimbangan.<br>Mulailah dengan Pose Papan netral dengan kaki bersentuhan<br>Akar dengan kuat ke tangan kanan Anda dan tuangkan beban Anda ke sisi kanan tubuh Anda<br>Gulingkan ke sisi kelingking kaki kanan Anda dan susun kaki kiri Anda di atas kaki kanan Anda<br>Turunkan tubuh ke lantai dengan tangan dan kaki, lalu angkat pinggul menjauh dari matras<br>Regangkan lengan kiri Anda ke langit<br>Tahan beberapa kali napas dalam-dalam lalu ganti sisi",
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"landmarks": [
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{
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"body": "nose",
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"coordinates": [
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0.7098162174224854,
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0.4569375216960907,
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-0.43993350863456726
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]
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},
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{
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"body": "left_eye_inner",
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"coordinates": [
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0.7251797318458557,
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0.47123992443084717,
|
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-0.4204886555671692
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]
|
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},
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{
|
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"body": "left_eye",
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"coordinates": [
|
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0.7261665463447571,
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0.47927266359329224,
|
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-0.4205434322357178
|
|
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]
|
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},
|
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{
|
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"body": "left_eye_outer",
|
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"coordinates": [
|
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0.7271681427955627,
|
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0.4877326190471649,
|
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-0.42069318890571594
|
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]
|
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},
|
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{
|
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"body": "right_eye_inner",
|
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"coordinates": [
|
|
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0.7234879732131958,
|
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0.4508434236049652,
|
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-0.4142134487628937
|
|
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]
|
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},
|
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{
|
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"body": "right_eye",
|
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"coordinates": [
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0.7235473394393921,
|
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0.44637542963027954,
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-0.4142872393131256
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]
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},
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{
|
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"body": "right_eye_outer",
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"coordinates": [
|
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0.72352534532547,
|
|
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0.4414880871772766,
|
|
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-0.4142611026763916
|
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]
|
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},
|
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{
|
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"body": "left_ear",
|
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"coordinates": [
|
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0.7266677021980286,
|
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||||||
0.5080437660217285,
|
|
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-0.27510419487953186
|
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]
|
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},
|
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{
|
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"body": "right_ear",
|
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"coordinates": [
|
|
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0.7228119373321533,
|
|
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0.4497060477733612,
|
|
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-0.24739260971546173
|
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]
|
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},
|
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{
|
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"body": "mouth_left",
|
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"coordinates": [
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0.6963652968406677,
|
|
||||||
0.47936636209487915,
|
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-0.38349977135658264
|
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]
|
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},
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{
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|
||||||
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|
|
||||||
-0.13209345936775208
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"body": "left_heel",
|
|
||||||
"coordinates": [
|
|
||||||
0.18081320822238922,
|
|
||||||
0.8039306402206421,
|
|
||||||
0.19741442799568176
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"body": "right_heel",
|
|
||||||
"coordinates": [
|
|
||||||
0.16176638007164001,
|
|
||||||
0.8074171543121338,
|
|
||||||
-0.13716663420200348
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"body": "left_foot_index",
|
|
||||||
"coordinates": [
|
|
||||||
0.259407639503479,
|
|
||||||
0.8775787353515625,
|
|
||||||
0.11939527839422226
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"body": "right_foot_index",
|
|
||||||
"coordinates": [
|
|
||||||
0.2452431619167328,
|
|
||||||
0.8928111791610718,
|
|
||||||
-0.25604158639907837
|
|
||||||
]
|
|
||||||
}
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
{
|
||||||
"image_name": "gerakan/Crow.jpg",
|
"image_name": "gerakan/Crow.jpg",
|
||||||
"name": "Crow",
|
"name": "Crow",
|
||||||
|
|
|
||||||
|
|
@ -208,7 +208,7 @@
|
||||||
console.log(data);
|
console.log(data);
|
||||||
var similarity = data.similarity;
|
var similarity = data.similarity;
|
||||||
|
|
||||||
if (similarity <80) {
|
if (similarity <90) {
|
||||||
counter = 0
|
counter = 0
|
||||||
}else{
|
}else{
|
||||||
counter = counter + 1
|
counter = counter + 1
|
||||||
|
|
@ -234,9 +234,9 @@
|
||||||
}, 2000);
|
}, 2000);
|
||||||
|
|
||||||
// set interval 70 second and change pose
|
// set interval 70 second and change pose
|
||||||
setInterval(function () {
|
// setInterval(function () {
|
||||||
window.location.href="{{ url_for('index', image_name=next) }}";
|
// window.location.href="{{ url_for('index', image_name=next) }}";
|
||||||
}, 70000);
|
// }, 70000);
|
||||||
</script>
|
</script>
|
||||||
</body>
|
</body>
|
||||||
|
|
||||||
|
|
|
||||||
Loading…
Reference in New Issue