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