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import kagglehub
import numpy as np
import os
from pathlib import Path
import cv2
import pandas as pd
from sklearn.preprocessing import LabelEncoder
from sklearn.metrics import accuracy_score, classification_report
from sklearn.model_selection import train_test_split
from skimage.feature import hog
from skimage.filters import sobel
import xgboost as xgb
# Download dataset
path = kagglehub.dataset_download("lsind18/gemstones-images")
print("Path to dataset files:", path)
# Set paths
train_dataset_path = Path(path) / "train"
test_dataset_path = Path(path) / "test"
# ------------------- Preprocessing -------------------
def preprocess_image(image_path):
image = cv2.imread(str(image_path))
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
image = cv2.GaussianBlur(image, (5, 5), 0)
lab = cv2.cvtColor(image, cv2.COLOR_RGB2LAB)
l, a, b = cv2.split(lab)
clahe = cv2.createCLAHE(clipLimit=5.0, tileGridSize=(10, 10))
l = clahe.apply(l)
lab = cv2.merge((l, a, b))
image = cv2.cvtColor(lab, cv2.COLOR_LAB2RGB)
image = cv2.resize(image, (128, 128))
return image
# ------------------- Gabor Kernel Bank -------------------
def build_gabor_kernels():
kernels = []
ksize = 31
for theta in np.arange(0, np.pi, np.pi / 4):
for sigma in (1, 3):
for lamda in (np.pi / 4, np.pi / 2):
kernel = cv2.getGaborKernel((ksize, ksize), sigma, theta, lamda, 0.5, 0, ktype=cv2.CV_32F)
kernels.append(kernel)
return kernels
gabor_kernels = build_gabor_kernels()
# ------------------- Feature Extraction -------------------
sift = cv2.SIFT_create()
def extract_features(image):
features = []
image_resized = cv2.resize(image, (128, 128))
# Color histograms
for i in range(3):
hist = cv2.calcHist([image_resized], [i], None, [64], [0, 256])
hist = hist.flatten() / (np.sum(hist) + 1e-5)
features.extend(hist)
# HOG
hog_feat = hog(image_resized, orientations=9, pixels_per_cell=(8, 8),
cells_per_block=(2, 2), visualize=False, channel_axis=2)
features.extend(hog_feat)
# Harris Corners
gray = cv2.cvtColor(image_resized, cv2.COLOR_RGB2GRAY)
corners = cv2.cornerHarris(np.float32(gray), 2, 3, 0.04)
corner_count = np.sum(corners > 0.01 * corners.max())
features.append(corner_count)
# Sobel Edge Mean
sobel_img = sobel(gray)
features.append(np.mean(sobel_img))
# SIFT (mean + std of descriptors)
kp, des = sift.detectAndCompute(gray, None)
if des is not None:
des_mean = np.mean(des, axis=0)
des_std = np.std(des, axis=0)
features.extend(des_mean)
features.extend(des_std)
else:
features.extend([0]*128) # mean
features.extend([0]*128) # std
# Gabor filter responses (mean)
for kernel in gabor_kernels:
fimg = cv2.filter2D(gray, cv2.CV_8UC3, kernel)
features.append(np.mean(fimg))
return features
# ------------------- Load Dataset -------------------
def load_dataset(dataset_path):
all_features = []
all_labels = []
for gemstone_folder in os.listdir(dataset_path):
gemstone_path = dataset_path / gemstone_folder
if gemstone_path.is_dir():
image_files = list(gemstone_path.glob("**/*.jpg"))
print(f"š {gemstone_folder}: {len(image_files)} images")
for image_path in image_files:
try:
image = preprocess_image(image_path)
features = extract_features(image)
all_features.append(features)
all_labels.append(gemstone_folder)
except Exception as e:
print(f"ā ļø Error processing {image_path}: {e}")
return all_features, all_labels
print("\nš„ Loading training data...")
train_features, train_labels = load_dataset(train_dataset_path)
print("\nš„ Loading test data...")
test_features, test_labels = load_dataset(test_dataset_path)
# ------------------- Encode Labels -------------------
le = LabelEncoder()
y_train = le.fit_transform(train_labels)
y_test = le.transform(test_labels)
# ------------------- Create DataFrames -------------------
X_train = pd.DataFrame(train_features)
X_test = pd.DataFrame(test_features)
print("\nš Features DataFrame shape:", X_train.shape)
# ------------------- Optional Train/Val Split -------------------
X_tr, X_val, y_tr, y_val = train_test_split(X_train, y_train, test_size=0.2, random_state=42)
# ------------------- Train XGBoost -------------------
clf = xgb.XGBClassifier(
objective='multi:softmax',
num_class=len(le.classes_),
eval_metric='mlogloss',
use_label_encoder=False,
max_depth=6,
subsample=0.8,
colsample_bytree=0.8,
n_estimators=100,
learning_rate=0.1,
random_state=42
)
print("\nāļø Training XGBoost...")
clf.fit(X_tr, y_tr) # No early stopping due to version limit
# ------------------- Evaluate -------------------
y_pred = clf.predict(X_test)
acc = accuracy_score(y_test, y_pred)
print(f"\nā
Accuracy on test set: {acc * 100:.2f}%")
print("\nš Classification Report:\n", classification_report(le.inverse_transform(y_test), le.inverse_transform(y_pred)))
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