update tracking

DetectFace/.gitignore

@@ -0,0 +1,2 @@
+dataset
+test

DetectFace/detect.py

@@ -0,0 +1,77 @@
+import cv2
+import face_recognition
+import os
+import numpy as np
+import pickle 
+datasetPath = "dataset"
+images = []
+classNames = []
+lisFileTrain = os.listdir(datasetPath)
+
+for file in lisFileTrain:
+    currentImg = cv2.imread(f"{datasetPath}/{file}")
+    images.append(currentImg)
+    classNames.append(os.path.splitext(file)[0].split('_')[0])
+
+print(len(images))
+
+def encodeImgs(images, save_path="encodings.pkl"):
+    if os.path.exists(save_path):
+        print(f"Loading encodings from {save_path}...")
+        with open(save_path, "rb") as f:
+            return pickle.load(f)
+        
+    encodeList = []
+    for i, img in enumerate(images):
+        print(i+1)
+        img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
+        encode = face_recognition.face_encodings(img)
+       
+        if encode:  # Check if encodings list is not empty
+            encodeList.append(encode[0])
+        else:
+            print("No face detected in an image. Skipping...")
+            os.remove(f"{datasetPath}/{lisFileTrain[i]}")
+    # Lưu encodeList vào file
+    print(f"Saving encodings to {save_path}...")
+    with open(save_path, "wb") as f:
+        pickle.dump(encodeList, f)
+
+    return encodeList
+
+encodeListKnow = encodeImgs(images)
+print("Load data success")
+print(len(encodeListKnow))
+
+cap = cv2.VideoCapture(0)
+
+while True:
+    ret, frame = cap.read()
+    frameS = cv2.resize(frame, (0,0), None, fx=1, fy=1)
+    frameS = cv2.cvtColor(frameS, cv2.COLOR_BGR2RGB)
+
+    faceCurFrame = face_recognition.face_locations(frameS)
+    encodeCurFrame = face_recognition.face_encodings(frameS)
+
+    for encodeFace, faceLoc in zip(encodeCurFrame, faceCurFrame):
+        matches = face_recognition.compare_faces(encodeListKnow, encodeFace)
+        faceDis = face_recognition.face_distance(encodeListKnow, encodeFace)
+        print(faceDis)
+        matchIndex = np.argmin(faceDis)
+
+        if faceDis[matchIndex] < 0.3:
+            name = classNames[matchIndex].upper()
+        else:
+            name = "Unknow"
+        
+        y1, x2, y2, x1 = faceLoc
+        y1, x2, y2, x1 = y1, x2, y2, x1
+        cv2.rectangle(frame, (x1,y1), (x2,y2), (0,255,0), 2)
+        cv2.putText(frame, name + f"({(1 - round(faceDis[matchIndex], 2))*100}%)", (x2, y2), cv2.FONT_HERSHEY_COMPLEX, 1, (255,255,255), 2)
+    cv2.imshow('Face decting', frame)
+
+    if cv2.waitKey(1) == ord("q"):
+        break
+
+cap.release()
+cv2.destroyAllWindows()

DetectFace/traning.py

@@ -1,66 +0,0 @@
-import tensorflow as tf
-import numpy as np
-import cv2
-import os
-from tensorflow.keras import layers, models
-from sklearn.model_selection import train_test_split
-
-# 1. Load dữ liệu
-def load_data(data_dir):
-    images = []
-    labels = []
-    for label, folder in enumerate(os.listdir(data_dir)):
-        folder_path = os.path.join(data_dir, folder)
-        for file in os.listdir(folder_path):
-            img_path = os.path.join(folder_path, file)
-            img = cv2.imread(img_path)
-            img = cv2.resize(img, (128, 128))  # Resize về kích thước cố định
-            images.append(img)
-            labels.append(label)
-    images = np.array(images) / 255.0  # Chuẩn hóa
-    labels = np.array(labels)
-    return images, labels
-
-# Đường dẫn dữ liệu
-data_dir = '/home/joseph/DetectFace/dataset'
-images, labels = load_data(data_dir)
-
-# Chia dữ liệu train/test
-X_train, X_test, y_train, y_test = train_test_split(images, labels, test_size=0.2, random_state=42)
-
-# 2. Tạo mô hình phát hiện khuôn mặt
-model = models.Sequential([
-    layers.Conv2D(32, (3, 3), activation='relu', input_shape=(128, 128, 3)),
-    layers.MaxPooling2D((2, 2)),
-    layers.Conv2D(64, (3, 3), activation='relu'),
-    layers.MaxPooling2D((2, 2)),
-    layers.Conv2D(128, (3, 3), activation='relu'),
-    layers.Flatten(),
-    layers.Dense(128, activation='relu'),
-    layers.Dense(len(set(labels)), activation='softmax')  # Số lớp tương ứng số nhãn
-])
-
-# Compile mô hình
-model.compile(optimizer='adam',
-              loss='sparse_categorical_crossentropy',
-              metrics=['accuracy'])
-
-# Huấn luyện mô hình
-model.fit(X_train, y_train, epochs=10, validation_data=(X_test, y_test))
-
-# 3. Lưu mô hình
-model.save('face_detection_model.h5')
-
-# 4. Sử dụng mô hình để dự đoán
-def predict_face(image_path, model_path='face_detection_model.h5'):
-    model = tf.keras.models.load_model(model_path)
-    img = cv2.imread(image_path)
-    img_resized = cv2.resize(img, (128, 128)) / 255.0
-    img_resized = np.expand_dims(img_resized, axis=0)
-    prediction = model.predict(img_resized)
-    return np.argmax(prediction), np.max(prediction)
-
-# Test với một ảnh
-image_path = '/home/joseph/DetectFace/test/NGUYEN\ HOANG\ VI_check\ in_at_2024_09_05_11_31_24.png'
-label, confidence = predict_face(image_path)
-print(f"Detected label: {label}, Confidence: {confidence:.2f}")

TrackingTool/tracking.py

@@ -6,6 +6,9 @@ import pyautogui
 from pyzbar import pyzbar
 from datetime import datetime
 import requests
+import face_recognition
+import numpy as np
+import pickle 
 # Khởi tạo danh sách rỗng để lưu trữ thông tin người dùng
 user_data = []
 history = []
@@ -193,6 +196,70 @@ def process_qr_code(frame):
             cv.destroyWindow(WINDOW_QR_CODE)
     return frame
 
+# Hàm để xử lý quá trình quét mã QR code
+def process_face_detect(text, frame):
+    if text.endswith("\n\n"):
+        file_name = ""
+        status = ""
+        id_log = 0
+        if text not in [user["name"] for user in user_data]:
+            print(f"{text} đã check in lúc {datetime.now()}")
+            status += "check in"
+            file_name+=text.split('\n')[0]+"_"+f"{status}_at_{datetime.now().strftime("%Y_%m_%d_%H_%M_%S")}.png"
+            # screenshot_window(file_name)
+            res = check_in(text, frame, text)
+            id_log = res.get('data').get('id')
+        else:
+            print(f"{text} đã check out lúc {datetime.now()}")
+            status += "check out"
+            file_name+=text.split('\n')[0]+"_"+f"{status}_at_{datetime.now().strftime("%Y_%m_%d_%H_%M_%S")}.png"
+            # screenshot_window(file_name)
+            res = check_out(text, frame, text)
+            id_log = res.get('data').get('id')
+        cv.namedWindow(WINDOW_QR_CODE, cv.WINDOW_NORMAL)
+        cv.resizeWindow(WINDOW_QR_CODE, screen_width, screen_height)
+        cv.imshow(WINDOW_QR_CODE, frame)
+        cv.moveWindow(WINDOW_QR_CODE, 10, 10)
+        cv.waitKey(5000)  # Chờ 5 giây
+        screenshot_window(file_name)
+        send_image(id_log, file_name)
+        cv.destroyWindow(WINDOW_QR_CODE)
+    else:
+        display_text(frame, f"QR invalid", (25, 25), 0.7, (6, 6, 255), 2)
+        display_text(frame, f"Failed", (25, 50), 0.7, (6, 6, 255), 2)
+        speak("Failed")   
+        cv.namedWindow(WINDOW_QR_CODE, cv.WINDOW_NORMAL)
+        cv.resizeWindow(WINDOW_QR_CODE, screen_width, screen_height)
+        cv.imshow(WINDOW_QR_CODE, frame)
+        cv.moveWindow(WINDOW_QR_CODE, 10, 10)
+        cv.waitKey(2000)
+        cv.destroyWindow(WINDOW_QR_CODE)
+    return frame
+
+datasetPath = "../DetectFace/dataset"
+listFilesPath = '../DetectFace/listFiles.pkl'
+images = []
+classNames = []
+lisFileTrain = []
+
+if os.path.exists(listFilesPath):
+    with open(listFilesPath, 'rb') as f:
+        lisFileTrain = pickle.load(f)
+else:
+    lisFileTrain = os.listdir(datasetPath)
+    with open(listFilesPath, 'wb') as f:
+        pickle.dump(lisFileTrain, f)
+for file in lisFileTrain:
+    classNames.append(os.path.splitext(file)[0].split('_')[0])
+
+def encodeImgs(save_path="../DetectFace/encodings.pkl"):
+    if os.path.exists(save_path):
+        print(f"Loading encodings from {save_path}...")
+        with open(save_path, "rb") as f:
+            return pickle.load(f)
+
+encodeListKnow = encodeImgs()
+print("Load data success")
 # Khởi tạo camera
 def main():
     cap = cv.VideoCapture(0)
@@ -203,20 +270,52 @@ def main():
         ret, frame = cap.read()
         if not ret:
             break
-        # Convert the frame to grayscale
-        gray_frame = cv.cvtColor(frame, cv.COLOR_BGR2GRAY)
 
-        # Detect faces in the frame
+        frameS = cv.resize(frame, (0,0), None, fx=1, fy=1)
-        faces = face_cascade.detectMultiScale(gray_frame, scaleFactor=1.1, minNeighbors=25, minSize=(30, 30))
+        frameS = cv.cvtColor(frameS, cv.COLOR_BGR2RGB)
 
-        # Draw rectangles around the faces
+        faceCurFrame = face_recognition.face_locations(frameS)
-        if len(faces) == 1:
+        encodeCurFrame = face_recognition.face_encodings(frameS)
-            for (x, y, w, h) in faces:
-                cv.rectangle(frame, (x, y), (x+w, y+h), (0, 255, 0), 2)
-                display_text(frame, f"Face detected", (430, 25), 0.7, (0, 255, 0), 2)
         frame = process_qr_code(frame)
+        for encodeFace, faceLoc in zip(encodeCurFrame, faceCurFrame):
+            matches = face_recognition.compare_faces(encodeListKnow, encodeFace)
+            faceDis = face_recognition.face_distance(encodeListKnow, encodeFace)
+            print(faceDis)
+            matchIndex = np.argmin(faceDis)
+
+            if faceDis[matchIndex] < 0.3:
+                name = classNames[matchIndex].upper()
+                process_face_detect(f"{name}\n{"Staff"}\n\n", frame)
             else:
-            display_text(frame, f"Face not found", (430, 25), 0.7, (6, 6, 255), 2)
+                name = "Unknow"
+                display_text(frame, f"Face not found - use QRcode", (20, 55), 0.7, (6, 6, 255), 2)
+            y1, x2, y2, x1 = faceLoc
+            y1, x2, y2, x1 = y1, x2, y2, x1
+            cv.rectangle(frame, (x1,y1), (x2,y2), (0,255,0), 2)
+            cv.putText(frame, name + f"({(1 - round(faceDis[matchIndex], 2))*100}%)", (20, 25), cv.FONT_HERSHEY_COMPLEX, 1, (255,255,255), 2)
+
+
+
+
+
+
+
+
+
+        # Convert the frame to grayscale
+        # gray_frame = cv.cvtColor(frame, cv.COLOR_BGR2GRAY)
+
+        # # Detect faces in the frame
+        # faces = face_cascade.detectMultiScale(gray_frame, scaleFactor=1.1, minNeighbors=25, minSize=(30, 30))
+
+        # # Draw rectangles around the faces
+        # if len(faces) == 1:
+        #     for (x, y, w, h) in faces:
+        #         cv.rectangle(frame, (x, y), (x+w, y+h), (0, 255, 0), 2)
+        #         display_text(frame, f"Face detected", (430, 25), 0.7, (0, 255, 0), 2)
+        #         frame = process_qr_code(frame)
+        # else:
+        #     display_text(frame, f"Face not found", (430, 25), 0.7, (6, 6, 255), 2)
         cv.imshow(WINDOW_TRACKING, frame)
         cv.moveWindow(WINDOW_TRACKING, 10, 10)
         if cv.waitKey(1) == ord('q'):