update tracking
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07d28e24cd
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dataset
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test
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import cv2
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import face_recognition
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import os
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import numpy as np
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import pickle
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datasetPath = "dataset"
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images = []
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classNames = []
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lisFileTrain = os.listdir(datasetPath)
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for file in lisFileTrain:
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currentImg = cv2.imread(f"{datasetPath}/{file}")
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images.append(currentImg)
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classNames.append(os.path.splitext(file)[0].split('_')[0])
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print(len(images))
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def encodeImgs(images, save_path="encodings.pkl"):
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if os.path.exists(save_path):
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print(f"Loading encodings from {save_path}...")
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with open(save_path, "rb") as f:
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return pickle.load(f)
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encodeList = []
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for i, img in enumerate(images):
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print(i+1)
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img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
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encode = face_recognition.face_encodings(img)
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if encode: # Check if encodings list is not empty
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encodeList.append(encode[0])
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else:
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print("No face detected in an image. Skipping...")
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os.remove(f"{datasetPath}/{lisFileTrain[i]}")
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# Lưu encodeList vào file
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print(f"Saving encodings to {save_path}...")
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with open(save_path, "wb") as f:
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pickle.dump(encodeList, f)
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return encodeList
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encodeListKnow = encodeImgs(images)
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print("Load data success")
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print(len(encodeListKnow))
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cap = cv2.VideoCapture(0)
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while True:
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ret, frame = cap.read()
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frameS = cv2.resize(frame, (0,0), None, fx=1, fy=1)
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frameS = cv2.cvtColor(frameS, cv2.COLOR_BGR2RGB)
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faceCurFrame = face_recognition.face_locations(frameS)
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encodeCurFrame = face_recognition.face_encodings(frameS)
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for encodeFace, faceLoc in zip(encodeCurFrame, faceCurFrame):
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matches = face_recognition.compare_faces(encodeListKnow, encodeFace)
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faceDis = face_recognition.face_distance(encodeListKnow, encodeFace)
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print(faceDis)
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matchIndex = np.argmin(faceDis)
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if faceDis[matchIndex] < 0.3:
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name = classNames[matchIndex].upper()
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else:
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name = "Unknow"
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y1, x2, y2, x1 = faceLoc
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y1, x2, y2, x1 = y1, x2, y2, x1
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cv2.rectangle(frame, (x1,y1), (x2,y2), (0,255,0), 2)
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cv2.putText(frame, name + f"({(1 - round(faceDis[matchIndex], 2))*100}%)", (x2, y2), cv2.FONT_HERSHEY_COMPLEX, 1, (255,255,255), 2)
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cv2.imshow('Face decting', frame)
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if cv2.waitKey(1) == ord("q"):
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break
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cap.release()
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cv2.destroyAllWindows()
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Binary file not shown.
Binary file not shown.
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import tensorflow as tf
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import numpy as np
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import cv2
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import os
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from tensorflow.keras import layers, models
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from sklearn.model_selection import train_test_split
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# 1. Load dữ liệu
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def load_data(data_dir):
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images = []
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labels = []
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for label, folder in enumerate(os.listdir(data_dir)):
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folder_path = os.path.join(data_dir, folder)
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for file in os.listdir(folder_path):
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img_path = os.path.join(folder_path, file)
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img = cv2.imread(img_path)
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img = cv2.resize(img, (128, 128)) # Resize về kích thước cố định
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images.append(img)
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labels.append(label)
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images = np.array(images) / 255.0 # Chuẩn hóa
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labels = np.array(labels)
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return images, labels
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# Đường dẫn dữ liệu
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data_dir = '/home/joseph/DetectFace/dataset'
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images, labels = load_data(data_dir)
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# Chia dữ liệu train/test
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X_train, X_test, y_train, y_test = train_test_split(images, labels, test_size=0.2, random_state=42)
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# 2. Tạo mô hình phát hiện khuôn mặt
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model = models.Sequential([
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layers.Conv2D(32, (3, 3), activation='relu', input_shape=(128, 128, 3)),
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layers.MaxPooling2D((2, 2)),
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layers.Conv2D(64, (3, 3), activation='relu'),
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layers.MaxPooling2D((2, 2)),
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layers.Conv2D(128, (3, 3), activation='relu'),
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layers.Flatten(),
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layers.Dense(128, activation='relu'),
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layers.Dense(len(set(labels)), activation='softmax') # Số lớp tương ứng số nhãn
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])
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# Compile mô hình
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model.compile(optimizer='adam',
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loss='sparse_categorical_crossentropy',
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metrics=['accuracy'])
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# Huấn luyện mô hình
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model.fit(X_train, y_train, epochs=10, validation_data=(X_test, y_test))
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# 3. Lưu mô hình
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model.save('face_detection_model.h5')
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# 4. Sử dụng mô hình để dự đoán
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def predict_face(image_path, model_path='face_detection_model.h5'):
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model = tf.keras.models.load_model(model_path)
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img = cv2.imread(image_path)
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img_resized = cv2.resize(img, (128, 128)) / 255.0
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img_resized = np.expand_dims(img_resized, axis=0)
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prediction = model.predict(img_resized)
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return np.argmax(prediction), np.max(prediction)
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# Test với một ảnh
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image_path = '/home/joseph/DetectFace/test/NGUYEN\ HOANG\ VI_check\ in_at_2024_09_05_11_31_24.png'
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label, confidence = predict_face(image_path)
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print(f"Detected label: {label}, Confidence: {confidence:.2f}")
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@ -6,6 +6,9 @@ import pyautogui
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from pyzbar import pyzbar
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from datetime import datetime
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import requests
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import face_recognition
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import numpy as np
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import pickle
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# Khởi tạo danh sách rỗng để lưu trữ thông tin người dùng
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user_data = []
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history = []
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@ -193,6 +196,70 @@ def process_qr_code(frame):
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cv.destroyWindow(WINDOW_QR_CODE)
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return frame
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# Hàm để xử lý quá trình quét mã QR code
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def process_face_detect(text, frame):
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if text.endswith("\n\n"):
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file_name = ""
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status = ""
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id_log = 0
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if text not in [user["name"] for user in user_data]:
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print(f"{text} đã check in lúc {datetime.now()}")
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status += "check in"
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file_name+=text.split('\n')[0]+"_"+f"{status}_at_{datetime.now().strftime("%Y_%m_%d_%H_%M_%S")}.png"
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# screenshot_window(file_name)
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res = check_in(text, frame, text)
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id_log = res.get('data').get('id')
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else:
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print(f"{text} đã check out lúc {datetime.now()}")
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status += "check out"
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file_name+=text.split('\n')[0]+"_"+f"{status}_at_{datetime.now().strftime("%Y_%m_%d_%H_%M_%S")}.png"
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# screenshot_window(file_name)
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res = check_out(text, frame, text)
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id_log = res.get('data').get('id')
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cv.namedWindow(WINDOW_QR_CODE, cv.WINDOW_NORMAL)
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cv.resizeWindow(WINDOW_QR_CODE, screen_width, screen_height)
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cv.imshow(WINDOW_QR_CODE, frame)
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cv.moveWindow(WINDOW_QR_CODE, 10, 10)
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cv.waitKey(5000) # Chờ 5 giây
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screenshot_window(file_name)
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send_image(id_log, file_name)
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cv.destroyWindow(WINDOW_QR_CODE)
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else:
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display_text(frame, f"QR invalid", (25, 25), 0.7, (6, 6, 255), 2)
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display_text(frame, f"Failed", (25, 50), 0.7, (6, 6, 255), 2)
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speak("Failed")
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cv.namedWindow(WINDOW_QR_CODE, cv.WINDOW_NORMAL)
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cv.resizeWindow(WINDOW_QR_CODE, screen_width, screen_height)
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cv.imshow(WINDOW_QR_CODE, frame)
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cv.moveWindow(WINDOW_QR_CODE, 10, 10)
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cv.waitKey(2000)
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cv.destroyWindow(WINDOW_QR_CODE)
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return frame
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datasetPath = "../DetectFace/dataset"
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listFilesPath = '../DetectFace/listFiles.pkl'
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images = []
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classNames = []
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lisFileTrain = []
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if os.path.exists(listFilesPath):
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with open(listFilesPath, 'rb') as f:
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lisFileTrain = pickle.load(f)
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else:
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lisFileTrain = os.listdir(datasetPath)
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with open(listFilesPath, 'wb') as f:
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pickle.dump(lisFileTrain, f)
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for file in lisFileTrain:
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classNames.append(os.path.splitext(file)[0].split('_')[0])
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def encodeImgs(save_path="../DetectFace/encodings.pkl"):
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if os.path.exists(save_path):
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print(f"Loading encodings from {save_path}...")
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with open(save_path, "rb") as f:
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return pickle.load(f)
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encodeListKnow = encodeImgs()
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print("Load data success")
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# Khởi tạo camera
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def main():
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cap = cv.VideoCapture(0)
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@ -203,20 +270,52 @@ def main():
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ret, frame = cap.read()
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if not ret:
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break
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frameS = cv.resize(frame, (0,0), None, fx=1, fy=1)
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frameS = cv.cvtColor(frameS, cv.COLOR_BGR2RGB)
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faceCurFrame = face_recognition.face_locations(frameS)
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encodeCurFrame = face_recognition.face_encodings(frameS)
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frame = process_qr_code(frame)
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for encodeFace, faceLoc in zip(encodeCurFrame, faceCurFrame):
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matches = face_recognition.compare_faces(encodeListKnow, encodeFace)
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faceDis = face_recognition.face_distance(encodeListKnow, encodeFace)
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print(faceDis)
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matchIndex = np.argmin(faceDis)
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if faceDis[matchIndex] < 0.3:
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name = classNames[matchIndex].upper()
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process_face_detect(f"{name}\n{"Staff"}\n\n", frame)
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else:
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name = "Unknow"
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display_text(frame, f"Face not found - use QRcode", (20, 55), 0.7, (6, 6, 255), 2)
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y1, x2, y2, x1 = faceLoc
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y1, x2, y2, x1 = y1, x2, y2, x1
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cv.rectangle(frame, (x1,y1), (x2,y2), (0,255,0), 2)
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cv.putText(frame, name + f"({(1 - round(faceDis[matchIndex], 2))*100}%)", (20, 25), cv.FONT_HERSHEY_COMPLEX, 1, (255,255,255), 2)
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# Convert the frame to grayscale
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gray_frame = cv.cvtColor(frame, cv.COLOR_BGR2GRAY)
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# gray_frame = cv.cvtColor(frame, cv.COLOR_BGR2GRAY)
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# Detect faces in the frame
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faces = face_cascade.detectMultiScale(gray_frame, scaleFactor=1.1, minNeighbors=25, minSize=(30, 30))
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# # Detect faces in the frame
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# faces = face_cascade.detectMultiScale(gray_frame, scaleFactor=1.1, minNeighbors=25, minSize=(30, 30))
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# Draw rectangles around the faces
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if len(faces) == 1:
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for (x, y, w, h) in faces:
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cv.rectangle(frame, (x, y), (x+w, y+h), (0, 255, 0), 2)
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display_text(frame, f"Face detected", (430, 25), 0.7, (0, 255, 0), 2)
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frame = process_qr_code(frame)
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else:
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display_text(frame, f"Face not found", (430, 25), 0.7, (6, 6, 255), 2)
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# # Draw rectangles around the faces
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# if len(faces) == 1:
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# for (x, y, w, h) in faces:
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# cv.rectangle(frame, (x, y), (x+w, y+h), (0, 255, 0), 2)
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# display_text(frame, f"Face detected", (430, 25), 0.7, (0, 255, 0), 2)
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# frame = process_qr_code(frame)
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# else:
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# display_text(frame, f"Face not found", (430, 25), 0.7, (6, 6, 255), 2)
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cv.imshow(WINDOW_TRACKING, frame)
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cv.moveWindow(WINDOW_TRACKING, 10, 10)
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if cv.waitKey(1) == ord('q'):
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