Final Commit

Python/tectcrime/.idea/.gitignore

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+# Default ignored files
+/shelf/
+/workspace.xml

Python/tectcrime/.idea/inspectionProfiles/Project_Default.xml

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+<component name="InspectionProjectProfileManager">
+  <profile version="1.0">
+    <option name="myName" value="Project Default" />
+    <inspection_tool class="PyPep8Inspection" enabled="true" level="WEAK WARNING" enabled_by_default="true">
+      <option name="ignoredErrors">
+        <list>
+          <option value="W292" />
+        </list>
+      </option>
+    </inspection_tool>
+  </profile>
+</component>

Python/tectcrime/.idea/inspectionProfiles/profiles_settings.xml

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+<component name="InspectionProjectProfileManager">
+  <settings>
+    <option name="USE_PROJECT_PROFILE" value="false" />
+    <version value="1.0" />
+  </settings>
+</component>

Python/tectcrime/.idea/misc.xml

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+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="Black">
+    <option name="sdkName" value="Python 3.12 (tectcrime)" />
+  </component>
+  <component name="ProjectRootManager" version="2" project-jdk-name="Python 3.12 (tectcrime)" project-jdk-type="Python SDK" />
+  <component name="PyCharmProfessionalAdvertiser">
+    <option name="shown" value="true" />
+  </component>
+</project>

Python/tectcrime/.idea/modules.xml

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+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="ProjectModuleManager">
+    <modules>
+      <module fileurl="file://$PROJECT_DIR$/.idea/tectcrime.iml" filepath="$PROJECT_DIR$/.idea/tectcrime.iml" />
+    </modules>
+  </component>
+</project>

Python/tectcrime/.idea/tectcrime.iml

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+<?xml version="1.0" encoding="UTF-8"?>
+<module type="PYTHON_MODULE" version="4">
+  <component name="NewModuleRootManager">
+    <content url="file://$MODULE_DIR$">
+      <excludeFolder url="file://$MODULE_DIR$/.venv" />
+    </content>
+    <orderEntry type="inheritedJdk" />
+    <orderEntry type="sourceFolder" forTests="false" />
+  </component>
+</module>

Python/tectcrime/app.py

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+from flask import Flask, request, jsonify, make_response
+import numpy as np
+import cv2
+from skimage.feature import local_binary_pattern
+import os
+from joblib import load
+
+app = Flask(__name__)
+
+
+def extract_lbp_features(image, radius=1, n_points=8, method='uniform'):
+    lbp = local_binary_pattern(image, n_points, radius, method)
+    (hist, _) = np.histogram(lbp.ravel(), bins=np.arange(0, n_points + 3), range=(0, n_points + 2))
+    # Normalize the histogram
+    hist = hist.astype("float")
+    hist /= (hist.sum() + 1e-7)
+    return hist
+
+
+# Mendapatkan path lengkap ke file facemodel.joblib
+model_path = os.path.join(os.path.dirname(__file__), 'facemodel.joblib')
+# Memuat model dari file
+knn_model = load(model_path)
+
+
+@app.route('/classify', methods=['POST'])
+def classify_face():
+    # Mendapatkan gambar dari request
+    file = request.files['image']
+
+    # Membaca gambar
+    image_bytes = file.read()
+    original_image = cv2.imdecode(np.frombuffer(image_bytes, np.uint8), cv2.IMREAD_COLOR)
+
+    gray_image = cv2.cvtColor(original_image, cv2.COLOR_BGR2GRAY)
+    gray_image = cv2.equalizeHist(gray_image)
+
+    face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')
+    faces = face_cascade.detectMultiScale(gray_image, scaleFactor=1.2, minNeighbors=10, minSize=(50, 50))
+
+    predictions = []
+    lbp_features = []
+
+    for i, (x, y, w, h) in enumerate(faces, 1):
+        cropped_face = original_image[y:y + h, x:x + w]
+        resized_face = cv2.resize(cropped_face, (100, 100))
+        gray_resized_face = cv2.cvtColor(resized_face, cv2.COLOR_BGR2GRAY)
+        normalized_face = cv2.normalize(gray_resized_face, None, alpha=0, beta=255, norm_type=cv2.NORM_MINMAX)
+        test_lbp_features = extract_lbp_features(normalized_face, radius=1, n_points=8, method='uniform')
+        lbp_features.append(test_lbp_features.tolist())
+        predicted_class = knn_model.predict([test_lbp_features])[0]
+        predictions.append(predicted_class)  # Hanya menambahkan kelas prediksi
+
+    response = make_response(jsonify(predictions))
+    response.headers['X-LBP-Features'] = str(lbp_features)
+    return response
+
+
+if __name__ == '__main__':
+    app.run(debug=True)

Python/tectcrime/classification.py

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+import numpy as np
+import cv2
+from skimage.feature import local_binary_pattern
+import joblib
+
+# Load the trained model from file
+knn_model = joblib.load('facemodel.joblib')
+
+def extract_lbp_features(image, radius=1, n_points=8, method='uniform'):
+    lbp = local_binary_pattern(image, n_points, radius, method)
+    (hist, _) = np.histogram(lbp.ravel(), bins=np.arange(0, n_points + 3), range=(0, n_points + 2))
+    # Normalize the histogram
+    hist = hist.astype("float")
+    hist /= (hist.sum() + 1e-7)
+    return hist
+
+# Fungsi untuk deteksi wajah dan cropping
+def detect_and_crop_faces(image_path):
+    # Baca gambar
+    original_image = cv2.imread(image_path)
+    gray_image = cv2.cvtColor(original_image, cv2.COLOR_BGR2GRAY)
+
+    # Peningkatan kontras
+    gray_image = cv2.equalizeHist(gray_image)
+
+    # Muat pre-trained Haar Cascade untuk deteksi wajah
+    face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')
+
+    # Deteksi wajah pada gambar dengan parameter yang disesuaikan
+    faces = face_cascade.detectMultiScale(gray_image, scaleFactor=1.4, minNeighbors=6, minSize=(50, 50))
+
+    # Cek jumlah wajah yang terdeteksi
+    print(f'{image_path}: {len(faces)} face(s) detected')
+
+    # Loop melalui setiap wajah yang terdeteksi, potong, dan tampilkan
+    for i, (x, y, w, h) in enumerate(faces, 1):
+        # Pemotongan (Cropping) wajah
+        cropped_face = original_image[y:y + h, x:x + w]
+
+        # # Tampilkan gambar wajah yang terpotong
+        # cv2.imshow(f'Cropped Face {i}', cropped_face)
+        #
+        # # Tunggu 1 detik sebelum menampilkan gambar berikutnya
+        # cv2.waitKey(1000)
+
+    return faces
+
+# Path ke gambar wajah uji
+test_image_path = 'images/testface/Alia Bhatt/Alia Bhatt_77.jpg'
+
+# Deteksi wajah dan cropping pada gambar uji
+test_faces = detect_and_crop_faces(test_image_path)
+
+# Loop melalui setiap wajah yang terdeteksi di gambar uji
+for i, (x, y, w, h) in enumerate(test_faces, 1):
+    # Pemotongan (Cropping) wajah
+    cropped_test_face = cv2.imread(test_image_path)[y:y + h, x:x + w]
+
+    # Resize gambar menjadi 100x100 piksel
+    resized_test_face = cv2.resize(cropped_test_face, (100, 100))
+
+    # Konversi gambar resized ke grayscale
+    gray_resized_test_face = cv2.cvtColor(resized_test_face, cv2.COLOR_BGR2GRAY)
+
+    # Normalisasi intensitas
+    normalized_test_face = cv2.normalize(gray_resized_test_face, None, alpha=0, beta=255, norm_type=cv2.NORM_MINMAX)
+
+    # Ekstraksi fitur LBP untuk gambar uji
+    test_lbp_features = extract_lbp_features(normalized_test_face, radius=1, n_points=8, method='uniform')
+
+    # Tampilkan nilai ekstraksi LBP ke konsol
+    print(f'LBP Features for face: {test_lbp_features}')
+
+    # Prediksi kelas menggunakan model K-NN
+    predicted_class = knn_model.predict([test_lbp_features])[0]
+
+    # Tampilkan hasil prediksi
+    print(f'Predicted class for face {i}: {predicted_class}')
+
+# Tunggu sampai tombol ditekan dan tutup jendela
+cv2.waitKey(0)
+cv2.destroyAllWindows()

Python/tectcrime/documentation.py

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+import cv2
+
+# Membaca gambar asli
+original_image = cv2.imread('images/backup/testface/Akshay Kumar/Akshay Kumar_40.jpg')
+cv2.imshow('Original Image', original_image)
+
+# Mengonversi gambar ke grayscale
+gray_image = cv2.cvtColor(original_image, cv2.COLOR_BGR2GRAY)
+cv2.imshow('Grayscale Image', gray_image)
+
+# Meningkatkan kontras gambar grayscale
+contrastpic = cv2.equalizeHist(gray_image)
+cv2.imshow('Contrast Enhanced Image', contrastpic)
+
+# Memuat classifier Haar Cascade untuk deteksi wajah
+face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')
+
+# Mendeteksi wajah dalam gambar grayscale
+faces = face_cascade.detectMultiScale(gray_image, scaleFactor=1.2, minNeighbors=10, minSize=(50, 50))
+
+# Inisialisasi variabel cropped_face dengan nilai kosong
+cropped_face = None
+
+for i, (x, y, w, h) in enumerate(faces, 1):
+    # Memotong wajah dari gambar asli
+    cropped_face = original_image[y:y + h, x:x + w]
+    cv2.imshow(f'Cropped Face {i}', cropped_face)
+
+# Mengubah ukuran wajah terpotong
+if cropped_face is not None:
+    resized_face = cv2.resize(cropped_face, (300, 300))
+    cv2.imshow('Resized Face', resized_face)
+
+    # Mengonversi wajah yang diubah ukurannya ke grayscale
+    gray_resized_face = cv2.cvtColor(resized_face, cv2.COLOR_BGR2GRAY)
+    cv2.imshow('Grayscale Resized Face', gray_resized_face)
+
+    # Melakukan normalisasi pada wajah grayscale yang diubah ukurannya
+    normalized_face = cv2.normalize(gray_resized_face, None, alpha=0, beta=255, norm_type=cv2.NORM_MINMAX)
+    cv2.imshow('Normalized Face', normalized_face)
+
+cv2.waitKey(0)
+cv2.destroyAllWindows()