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import numpy as np
from skimage.feature.texture import graycomatrix, graycoprops
from skimage.feature import local_binary_pattern, hog
from sklearn.svm import SVC
from sklearn.model_selection import train_test_split, GridSearchCV
from sklearn.metrics import accuracy_score, classification_report, precision_score, confusion_matrix
from sklearn.preprocessing import StandardScaler
def rgb_histogram(image, bins=256):
hist_features = []
for i in range(3):
hist, _ = np.histogram(image[:, :, i], bins=bins, range=(0, 256), density=True)
hist_features.append(hist)
return np.concatenate(hist_features)
def hu_moments(image):
gray = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
moments = cv2.moments(gray)
hu_moments = cv2.HuMoments(moments).flatten()
return hu_moments
def glcm_features(image, distances=[1], angles=[0], levels=256, symmetric=True, normed=True):
gray = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
glcm = graycomatrix(gray, distances=distances, angles=angles, levels=levels, symmetric=symmetric, normed=normed)
contrast = graycoprops(glcm, 'contrast').flatten()
dissimilarity = graycoprops(glcm, 'dissimilarity').flatten()
homogeneity = graycoprops(glcm, 'homogeneity').flatten()
energy = graycoprops(glcm, 'energy').flatten()
correlation = graycoprops(glcm, 'correlation').flatten()
asm = graycoprops(glcm, 'ASM').flatten()
return np.concatenate([contrast, dissimilarity, homogeneity, energy, correlation, asm])
def local_binary_pattern_features(image, P=8, R=1):
gray = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
lbp = local_binary_pattern(gray, P, R, method='uniform')
(hist, _) = np.histogram(lbp.ravel(), bins=np.arange(0, P + 3), range=(0, P + 2), density=True)
return hist
def extract_features_from_image(image):
hist_features = rgb_histogram(image, bins=64)
hu_features = hu_moments(image)
glcm_features_vector = glcm_features(image)
lbp_features = local_binary_pattern_features(image)
gray = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
hog_features = hog(
gray,
orientations=8,
pixels_per_cell=(32, 32),
cells_per_block=(2, 2),
visualize=False,
feature_vector=True
)
color_moments_features = []
for channel in cv2.split(image):
color_moments_features.append(np.mean(channel))
color_moments_features.append(np.std(channel))
color_moments_features = np.array(color_moments_features)
edges = cv2.Canny(gray, 50, 150)
edge_density = np.sum(edges > 0) / edges.size
edge_features = np.array([edge_density])
image_features = np.concatenate([
hist_features,
hu_features,
glcm_features_vector,
lbp_features,
hog_features,
color_moments_features,
edge_features
])
return image_features
def perform_pca(data, num_components):
mean = np.mean(data, axis=0)
std_dev = np.std(data, axis=0)
data_standardized = (data - mean) / std_dev
covariance_matrix = np.cov(data_standardized, rowvar=False)
eigenvalues, eigenvectors = np.linalg.eig(covariance_matrix)
sorted_indices = np.argsort(eigenvalues)[::-1]
sorted_eigenvalues = eigenvalues[sorted_indices]
sorted_eigenvectors = eigenvectors[:, sorted_indices]
top_k_eigenvectors = sorted_eigenvectors[:, :num_components]
data_reduced = np.dot(data_standardized, top_k_eigenvectors)
data_reduced = np.real(data_reduced)
return data_reduced
def train_svm_model(features, labels, test_size=0.2, use_grid_search=False, use_precision_optimization=False):
if labels.ndim > 1 and labels.shape[1] > 1:
labels = np.argmax(labels, axis=1)
X_train, X_test, y_train, y_test = train_test_split(
features, labels, test_size=test_size, random_state=42,
stratify=labels
)
scaler = StandardScaler()
X_train_scaled = scaler.fit_transform(X_train)
X_test_scaled = scaler.transform(X_test)
if use_grid_search:
print("Grid Search...")
param_grid = {
'C': [0.1, 1, 10, 100, 1000],
'kernel': ['rbf', 'linear', 'poly'],
'gamma': ['scale', 'auto', 0.001, 0.01, 0.1],
'class_weight': ['balanced', None],
'degree': [2, 3, 4]
}
svm = SVC(random_state=42, probability=True)
scoring = 'precision_weighted' if use_precision_optimization else 'accuracy'
grid_search = GridSearchCV(
svm, param_grid,
cv=5,
scoring=scoring,
n_jobs=-1,
verbose=1
)
grid_search.fit(X_train_scaled, y_train)
svm_model = grid_search.best_estimator_
print(f"\nbest params: {grid_search.best_params_}")
print(f" CV Score: {grid_search.best_score_:.4f}")
else:
svm_model = SVC(
kernel='rbf',
C=10,
gamma='scale',
class_weight='balanced',
random_state=42,
probability=True
)
svm_model.fit(X_train_scaled, y_train)
y_pred = svm_model.predict(X_test_scaled)
accuracy = accuracy_score(y_test, y_pred)
print(f'Test Accuracy: {accuracy:.2f}')
if use_precision_optimization:
precision_weighted = precision_score(y_test, y_pred, average='weighted', zero_division=0)
precision_macro = precision_score(y_test, y_pred, average='macro', zero_division=0)
print(f'Test Precision (Weighted): {precision_weighted:.4f}')
print(f'Test Precision (Macro): {precision_macro:.4f}')
print(f'\nClassification Report:')
print(classification_report(y_test, y_pred, zero_division=0))
results = {
'model': svm_model,
'scaler': scaler,
'accuracy': accuracy,
'precision_weighted': precision_weighted,
'precision_macro': precision_macro,
'y_test': y_test,
'y_pred': y_pred
}
if use_grid_search:
results['best_params'] = grid_search.best_params_
results['cv_score'] = grid_search.best_score_
return svm_model, results
return svm_model
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