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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.decomposition import PCA
from sklearn.svm import SVC
from sklearn.model_selection import train_test_split, GridSearchCV, StratifiedKFold
from sklearn.metrics import accuracy_score, confusion_matrix, classification_report
from sklearn.feature_selection import SelectKBest, f_classif
from sklearn.preprocessing import StandardScaler
from sklearn.pipeline import Pipeline
def rgb_histogram(image, bins=32):
features = []
# Convert to float32 for stability
image = image.astype(np.float32)
# RGB histograms
for i in range(3):
hist = cv2.calcHist([image], [i], None, [bins], [0, 256])
hist = cv2.normalize(hist, hist).flatten()
features.extend(hist)
# HSV histograms
hsv = cv2.cvtColor(image.astype(np.uint8), cv2.COLOR_RGB2HSV)
for i, (low, high) in enumerate(zip([0, 0, 0], [180, 256, 256])):
hist = cv2.calcHist([hsv], [i], None, [bins], [low, high])
hist = cv2.normalize(hist, hist).flatten()
features.extend(hist)
# Color moments (mean, std, skew)
for i in range(3):
channel = image[:, :, i]
mean = np.mean(channel)
std = np.std(channel)
skew = np.cbrt(np.mean((channel - mean) ** 3))
features.extend([mean, std, skew])
return np.array(features)
def hu_moments(image):
gray = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
moments = cv2.moments(gray)
hu = cv2.HuMoments(moments).flatten()
hu = -np.sign(hu) * np.log10(np.abs(hu) + 1e-10)
# Clip extreme values to reduce sensitivity to noise
hu = np.clip(hu, -10, 10)
return hu
def glcm_features(image, distances=[1, 2], angles=[0, np.pi/4, np.pi/2], levels=64):
gray = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
gray = (gray // (256 // levels)).astype(np.uint8) # quantization
features = []
for d in distances:
for a in angles:
glcm = graycomatrix(gray, distances=[d], angles=[a], levels=levels, symmetric=True, normed=True)
props = ['contrast', 'dissimilarity', 'homogeneity', 'energy', 'correlation']
for p in props:
val = graycoprops(glcm, p).flatten()
features.extend(val)
return np.array(features)
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
# Edge Density (Canny-based)
def edge_density(image, low_threshold=50, high_threshold=150):
gray = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
edges = cv2.Canny(gray, low_threshold, high_threshold)
density = np.sum(edges > 0) / edges.size
return np.array([density])
def hog_features(image, pixels_per_cell=(16,16), cells_per_block=(2,2), orientations=9):
image_resized = cv2.resize(image, (128, 128))
gray = cv2.cvtColor(image_resized, cv2.COLOR_RGB2GRAY)
hog_feat = hog(gray,
orientations=orientations,
pixels_per_cell=pixels_per_cell,
cells_per_block=cells_per_block,
block_norm='L2-Hys',
transform_sqrt=True,
feature_vector=True)
return hog_feat
def extract_features_from_image(image):
hist = rgb_histogram(image)
hu = hu_moments(image)
glcm = glcm_features(image)
lbp = local_binary_pattern_features(image)
edge = edge_density(image)
hog_f = hog_features(image)
return np.concatenate([hist, hu, glcm, lbp, edge, hog_f])
def perform_pca(data, num_components):
# Clean data
data = np.nan_to_num(data, nan=0.0, posinf=0.0, neginf=0.0)
# Standardize
scaler = StandardScaler()
data_standardized = scaler.fit_transform(data)
# Apply PCA
k = min(num_components, data.shape[1])
pca = PCA(n_components=k)
data_reduced = pca.fit_transform(data_standardized)
print(f"PCA: Reduced from {data.shape[1]} to {k} components")
print(f"Explained variance: {np.sum(pca.explained_variance_ratio_):.4f}")
return data_reduced
def train_svm_model(features, labels,
test_size=0.2,
random_state=42,
use_selectkbest=True,
k_best=500,
n_pca_components=100,
do_gridsearch=False):
"""
Returns:
pipeline: trained sklearn Pipeline (scaler -> optional SelectKBest -> PCA -> SVC)
X_test, y_test, y_pred for quick evaluation
grid_search (if do_gridsearch True), else None
"""
if labels.ndim > 1 and labels.shape[1] > 1:
labels = np.argmax(labels, axis=1)
# stratified split
X_train, X_test, y_train, y_test = train_test_split(
features, labels, test_size=test_size, random_state=random_state, stratify=labels)
# build pipeline steps
steps = []
steps.append(('scaler', StandardScaler()))
if use_selectkbest:
steps.append(('select', SelectKBest(score_func=f_classif, k=min(k_best, X_train.shape[1]))))
steps.append(('pca', PCA(n_components=min(n_pca_components, X_train.shape[1]))))
steps.append(('svc', SVC(kernel='linear', probability=True, class_weight='balanced', random_state=random_state)))
pipeline = Pipeline(steps)
grid_search = None
if do_gridsearch:
param_grid = {
'select__k': [int(min(200, X_train.shape[1])), int(min(500, X_train.shape[1])), int(min(1000, X_train.shape[1]))] if use_selectkbest else [],
'pca__n_components': [50, 100, 200],
'svc__C': [0.1, 1, 5, 10]
}
# remove empty keys if use_selectkbest is False
param_grid = {k: v for k, v in param_grid.items() if v}
cv = StratifiedKFold(n_splits=5, shuffle=True, random_state=random_state)
grid_search = GridSearchCV(pipeline, param_grid, cv=cv, n_jobs=-1, scoring='accuracy', verbose=2)
grid_search.fit(X_train, y_train)
best_model = grid_search.best_estimator_
pipeline = best_model
else:
pipeline.fit(X_train, y_train)
# Evaluate
y_pred = pipeline.predict(X_test)
acc = accuracy_score(y_test, y_pred)
print(f"Test Accuracy: {acc:.4f}")
print(classification_report(y_test, y_pred))
print("Confusion matrix:\n", confusion_matrix(y_test, y_pred))
return pipeline, (X_test, y_test, y_pred), grid_search
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