feat(dataset): Import raw data import script

clustering.py

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+import os
+import numpy as np
+from sklearn.neural_network import MLPClassifier
+from sklearn.model_selection import train_test_split
+from sklearn.metrics import accuracy_score, classification_report, confusion_matrix
+import matplotlib.pyplot as plt
+from skimage import io, color
+import seaborn as sns
+from sklearn.linear_model import Perceptron, LogisticRegression
+import shutil
+
+def load_images(folder_path):
+    images = []
+    for filename in os.listdir(folder_path):
+        if filename.endswith(('.png')):
+            img_path = os.path.join(folder_path, filename)
+            img = io.imread(img_path)
+            r = np.array(img).ravel()
+            if r.size == 256 : images.append(r)
+    return images
+
+human_faces_data = np.stack(load_images('humans'))
+non_human_faces_data = np.stack(load_images('nohumans'))
+
+
+human_labels = np.ones(human_faces_data.shape[0])
+non_human_labels = np.zeros(non_human_faces_data.shape[0])
+
+X = np.concatenate([human_faces_data, non_human_faces_data])
+y = np.concatenate([human_labels, non_human_labels])
+
+
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)
+perceptron_model = MLPClassifier(hidden_layer_sizes=(8,8,8,8), solver="lbfgs", activation="relu", max_iter=10000)
+
+
+perceptron_model.fit(X_train, y_train)
+
+y_pred = perceptron_model.predict(X_test)
+
+accuracy = accuracy_score(y_test, y_pred)
+classification_rep = classification_report(y_test, y_pred)
+
+print(f"Accuracy: {accuracy:.2f}")
+print("Classification Report:")
+print(classification_rep)
+
+
+conf_matrix = confusion_matrix(y_test, y_pred)
+
+sns.heatmap(conf_matrix, annot=True, fmt='d', cmap='Blues', cbar=False,
+            xticklabels=['Non-Human', 'Human'],
+            yticklabels=['Non-Human', 'Human'])
+plt.xlabel('Predicted')
+plt.ylabel('True')
+plt.title('Confusion Matrix')
+plt.show()
+
+
+
+fig, axes = plt.subplots(2, 5, figsize=(10, 5))
+fig.suptitle('Example Predictions')
+
+for i in range(5):
+    axes[0, i].imshow(X_test[i].reshape((8, 8, 4))[:,:,0])
+    axes[0, i].set_title(f"Actual: {y_test[i]}, Predicted: {y_pred[i]}")
+    axes[0, i].axis('off')
+
+    axes[1, i].imshow(X_test[i + 5].reshape((8, 8, 4))[:,:,0])
+    axes[1, i].set_title(f"Actual: {y_test[i + 5]}, Predicted: {y_pred[i + 5]}")
+    axes[1, i].axis('off')
+
+plt.show()
+
+
+def predict_and_move(folder_path, model):
+    for filename in os.listdir(folder_path):
+        if filename.endswith(('.png')):
+            img_path = os.path.join(folder_path, filename)
+            img = io.imread(img_path)
+            img_flat = img.reshape(1, -1)
+            if img.size==256:
+                # image_hsv = color.rgb2hsv(img[:,:,:])
+                # average_hue = np.degrees(np.mean(image_hsv[:,:,0]))
+                # pink_range = (300, 360, 0, 70)
+                # is_pink = pink_range[0] <= average_hue <= pink_range[1] or pink_range[2] <= average_hue <= pink_range[3]
+                if perceptron_model.predict_proba(img_flat)[0, 1] > 0.8:
+                    print(f"Image {filename} is predicted as a human face and will be moved.")
+                    shutil.copy(os.path.join(folder_path, filename), 'predicted_humans')
+
+
+    # for filename, img in zip(*images):
+    #     img_flat = img.reshape(1, -1)
+
+    #     prediction = model.predict(img_flat)
+
+    #     if prediction == 1:
+    #         print(f"Image {filename} is predicted as a human face and will be moved.")
+            
+    #         shutil.copy(f"../{filename}", 'predicted_humans')
+
+
+
+predict_and_move("..", perceptron_model)