233 lines
9.9 KiB
Python
233 lines
9.9 KiB
Python
from flask import Flask, render_template, Response , request, jsonify, send_file
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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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import json
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import os
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app = Flask(__name__,
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static_folder='assets',
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template_folder='templates'
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)
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# Load JSON file
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with open('dataset.json', 'r') as f:
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all_data = json.load(f)
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the_image_path = "gambar/tutorial.jpg"
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the_name = None
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the_keterangan = "Posisikan satu tangan kepada menunjuk dan tangan lainnya kepada terbuka dan tunjukkan ke arah titik tangan"
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# Initialize MediaPipe hand detection
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mp_hands = mp.solutions.hands
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hands = mp_hands.Hands(static_image_mode=False, max_num_hands=2, min_detection_confidence=0.7, min_tracking_confidence=0.7)
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# Initialize MediaPipe drawing utilities
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mp_drawing = mp.solutions.drawing_utils
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drawing_styles = mp.solutions.drawing_styles
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# Configure drawing options
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dot_drawing_spec = mp_drawing.DrawingSpec(color=(255, 0, 0), thickness=3, circle_radius=5)
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def draw_landmarks_no_lines(image, hand_landmarks, landmark_drawing_spec):
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if not hand_landmarks:
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return
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for idx, landmark in enumerate(hand_landmarks.landmark):
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landmark_px = mp_drawing._normalized_to_pixel_coordinates(landmark.x, landmark.y, image.shape[1], image.shape[0])
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if landmark_px:
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cv2.circle(image, landmark_px, landmark_drawing_spec.circle_radius, landmark_drawing_spec.color, landmark_drawing_spec.thickness)
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# Initialize camera capture
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cap = cv2.VideoCapture(0)
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# Landmark names
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landmark_names = [str(landmark) for landmark in mp_hands.HandLandmark]
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def generate_frames(stat):
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global the_image_path, the_name, the_keterangan
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while True:
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# Read frame from camera
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ret, frame = cap.read()
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if not ret:
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break
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# Convert frame to RGB for MediaPipe
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frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
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# Detect hand landmarks with MediaPipe
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results = hands.process(frame_rgb)
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pointing_hand = []
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open_palm = []
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if results.multi_hand_landmarks:
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for idx, hand_landmarks in enumerate(results.multi_hand_landmarks):
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landmarks = hand_landmarks.landmark
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# Calculate the palm center
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palm_landmarks = [
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landmarks[mp_hands.HandLandmark.WRIST],
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landmarks[mp_hands.HandLandmark.INDEX_FINGER_MCP],
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landmarks[mp_hands.HandLandmark.MIDDLE_FINGER_MCP],
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landmarks[mp_hands.HandLandmark.RING_FINGER_MCP],
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landmarks[mp_hands.HandLandmark.PINKY_MCP]
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]
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palm_center_x = sum([lm.x for lm in palm_landmarks]) / len(palm_landmarks)
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palm_center_y = sum([lm.y for lm in palm_landmarks]) / len(palm_landmarks)
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# Convert palm center to pixel coordinates
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h, w, _ = frame.shape
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palm_center_px = int(palm_center_x * w)
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palm_center_py = int(palm_center_y * h) - 90 # Shift the center point downwards by 90 pixels
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# Draw a circle at the palm center
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cv2.circle(frame, (palm_center_px, palm_center_py), 10, (255, 0, 0), -1) # Increase the radius to 10
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# Check for gestures
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gesture = None
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if is_pointing_gesture(landmarks):
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gesture = "Pointing"
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# Check the idx of the hand
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pointing_hand = hand_landmarks.landmark
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else:
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gesture = "Open Palm"
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open_palm = hand_landmarks.landmark
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if gesture:
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# Get the coordinates for the wrist landmark to place the text above the hand
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wrist = landmarks[mp_hands.HandLandmark.WRIST]
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cx, cy = int(wrist.x * w), int(wrist.y * h)
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# Add text annotation to the frame
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cv2.putText(frame, gesture, (cx, cy - 20), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2, cv2.LINE_AA)
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if gesture == "Pointing":
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if open_palm:
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finger_tip = pointing_hand[mp_hands.HandLandmark.INDEX_FINGER_TIP]
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# Indices to exclude
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excluded_indices = {1, 3, 5, 6, 10, 13, 16, 17, 18}
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distances = [np.sqrt((landmark.x - finger_tip.x) ** 2 +
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(landmark.y - finger_tip.y) ** 2)
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for i, landmark in enumerate(open_palm) if i not in excluded_indices]
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############# ini utk kira palm center ##########
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# Calculate the finger tip distance from palm center
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finger_tip_distance = np.sqrt((finger_tip.x - palm_center_x) ** 2 +
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(finger_tip.y - palm_center_y) ** 2)
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# Set a threshold distance in normalized coordinates
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threshold_distance = 0.34
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threshold_distance2 = 0.37
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# Print the finger tip distance
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print(finger_tip_distance)
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# Check if the finger tip is close to the palm center within the threshold distance
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if threshold_distance < finger_tip_distance < threshold_distance2:
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the_image_path = all_data[21]["image_path"]
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the_name = all_data[21]["name"]
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the_keterangan = all_data[21]["keterangan"]
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cv2.putText(frame, 'Closest Landmark: Tengah Telapak Tangan',
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(int(finger_tip.x * w), int(finger_tip.y * h)),
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cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 2)
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########## ending palm center ##########
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elif distances:
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closest_landmark_idx = np.argmin(distances)
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the_image_path = all_data[closest_landmark_idx]["image_path"]
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the_name = all_data[closest_landmark_idx]["name"]
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the_keterangan = all_data[closest_landmark_idx]["keterangan"]
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closest_landmark_name = landmark_names[closest_landmark_idx]
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cv2.putText(frame, f'Closest Landmark: {closest_landmark_name}',
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(int(finger_tip.x * w), int(finger_tip.y * h)),
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cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 2)
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else:
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the_image_path = "gambar/tutorial.jpg"
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the_name = None
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the_keterangan = "Posisikan satu tangan kepada menunjuk dan tangan lainnya kepada terbuka dan tunjukkan ke arah titik tangan"
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cv2.putText(frame, 'No open palm detected', (cx, cy - 50), cv2.FONT_HERSHEY_SIMPLEX, 1,
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(0, 0, 255), 2, cv2.LINE_AA)
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# Draw landmarks on the frame, excluding specific indices
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excluded_indices = {1, 3, 5, 6, 10, 13, 16, 17, 18}
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for i, landmark in enumerate(hand_landmarks.landmark):
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if i not in excluded_indices:
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cv2.circle(frame, (int(landmark.x * w), int(landmark.y * h)), 10, (255, 0, 0), -1)
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else:
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the_image_path = "gambar/tutorial.jpg"
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the_name = None
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the_keterangan = "Posisikan satu tangan kepada menunjuk dan tangan lainnya kepada terbuka dan tunjukkan ke arah titik tangan"
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# Convert the frame to bytes
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ret, buffer = cv2.imencode('.jpg', frame)
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frame = buffer.tobytes()
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if stat == 'camera':
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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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@app.route('/')
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def index():
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return render_template('index2.html')
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@app.route('/video_feed')
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def video_feed():
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stat = request.args.get('stat','camera')
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return Response(generate_frames(stat), mimetype='multipart/x-mixed-replace; boundary=frame')
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@app.route('/get_data', methods=['GET'])
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def get_data():
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global the_image_path, the_name, the_keterangan
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if the_image_path and the_name and the_keterangan:
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return jsonify({"name": the_name, "keterangan": the_keterangan, "image_path": the_image_path})
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else:
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# just return empty
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return jsonify({})
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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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def is_pointing_gesture(landmarks):
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# Index finger tip and other joints
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index_tip = landmarks[mp_hands.HandLandmark.INDEX_FINGER_TIP]
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index_dip = landmarks[mp_hands.HandLandmark.INDEX_FINGER_DIP]
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index_pip = landmarks[mp_hands.HandLandmark.INDEX_FINGER_PIP]
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index_mcp = landmarks[mp_hands.HandLandmark.INDEX_FINGER_MCP]
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# Check if the index finger is extended
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index_extended = (index_tip.y < index_dip.y < index_pip.y < index_mcp.y)
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# Check if other fingers are bent
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other_fingers_bent = True
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for finger_tip, finger_pip in [
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(mp_hands.HandLandmark.MIDDLE_FINGER_TIP, mp_hands.HandLandmark.MIDDLE_FINGER_PIP),
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(mp_hands.HandLandmark.RING_FINGER_TIP, mp_hands.HandLandmark.RING_FINGER_PIP),
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(mp_hands.HandLandmark.PINKY_TIP, mp_hands.HandLandmark.PINKY_PIP)]:
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if landmarks[finger_tip].y < landmarks[finger_pip].y:
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other_fingers_bent = False
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if other_fingers_bent is False:
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return True
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return False
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if __name__ == '__main__':
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app.run(debug=True)
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