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e06826c | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 | # image_processing.py
import cv2
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from skimage.feature import graycomatrix, graycoprops, local_binary_pattern
import joblib
import warnings
warnings.filterwarnings("ignore")
# Load model and preprocessing tools
model = joblib.load("model.pkl")
label_encoder = joblib.load("label_encoder.pkl")
scaler = joblib.load("scaler.pkl")
# Preprocessing: Resize, apply CLAHE, sharpen
def preprocessing(single_image, count=1):
single_image = cv2.resize(single_image, (256,256))
rgb_image = cv2.cvtColor(single_image, cv2.COLOR_BGR2RGB)
r, g, b = cv2.split(rgb_image)
clahe = cv2.createCLAHE(clipLimit=0.4, tileGridSize=(8, 8))
r_clahe = clahe.apply(r)
g_clahe = clahe.apply(g)
b_clahe = clahe.apply(b)
clahe_image = cv2.merge((r_clahe, g_clahe, b_clahe))
clahe_bgr = cv2.cvtColor(clahe_image, cv2.COLOR_RGB2BGR)
blurred = cv2.GaussianBlur(clahe_bgr, (5, 5), 1.5)
sharp = cv2.addWeighted(clahe_bgr, 1.5, blurred, -0.5, 0)
return rgb_image, clahe_image, cv2.cvtColor(sharp, cv2.COLOR_BGR2RGB)
# RGB histogram plotting
def plot_rgb_histogram(image):
color = ('b', 'g', 'r')
fig, ax = plt.subplots()
for i, col in enumerate(color):
hist = cv2.calcHist([image], [i], None, [256], [0,256])
ax.plot(hist, color=col)
ax.set_title("RGB Histogram")
ax.set_xlim([0, 256])
return fig
# Extract features: GLCM, LBP, color, edge, etc.
def feature_extraction(image, return_df=True):
gray = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
glcm = graycomatrix(gray, distances=[1], angles=[0], levels=256, symmetric=True, normed=True)
contrast = graycoprops(glcm, 'contrast')[0, 0]
correlation = graycoprops(glcm, 'correlation')[0, 0]
energy = graycoprops(glcm, 'energy')[0, 0]
homogeneity = graycoprops(glcm, 'homogeneity')[0, 0]
lbp = local_binary_pattern(gray, P=8, R=1, method='uniform')
lbp_mean = np.mean(lbp)
mean_r = np.mean(image[:, :, 0])
mean_g = np.mean(image[:, :, 1])
mean_b = np.mean(image[:, :, 2])
diff_black = ((1-(mean_r-255)/255) + (1-(mean_g-255)/255) + (1-(mean_b-255)/255))/3
sobelx = cv2.Sobel(gray, cv2.CV_64F, 1, 0, ksize=5)
sobely = cv2.Sobel(gray, cv2.CV_64F, 0, 1, ksize=5)
edge_count = np.sum(cv2.magnitude(sobelx, sobely) > 0)
features = [contrast, correlation, energy, homogeneity, lbp_mean,
mean_r, mean_g, mean_b, edge_count, diff_black]
if return_df:
df = pd.DataFrame([features], columns=[
"Contrast", "Correlation", "Energy", "Homogeneity", "LBP_Mean",
"Mean_R", "Mean_G", "Mean_B", "Edge_Count", "Black"
])
return df
else:
return np.array([features])
# Predict class and confidence from image
def predict_image_class_with_features(image):
_, _, sharp = preprocessing(image, count=0)
features_df = feature_extraction(sharp)
features_scaled = scaler.transform(features_df)
prediction = model.predict(features_scaled)
predicted_class = label_encoder.inverse_transform(prediction)[0]
confidence = np.max(model.predict_proba(features_scaled))
return features_df, predicted_class, confidence
# Segment image and classify each region
def segment_and_classify_regions(image, k_clusters=2):
rgb_img, _, sharp_img = preprocessing(image)
reshaped = sharp_img.reshape((-1, 3)).astype(np.float32)
criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 100, 0.2)
_, labels, centers = cv2.kmeans(reshaped, k_clusters, None, criteria, 10, cv2.KMEANS_RANDOM_CENTERS)
segmented = labels.flatten().reshape(sharp_img.shape[:2])
output = image.copy()
region_predictions = []
for i in range(k_clusters):
mask = (segmented == i).astype(np.uint8) * 255
region = cv2.bitwise_and(sharp_img, sharp_img, mask=mask)
contours, _ = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
if not contours:
continue
x, y, w, h = cv2.boundingRect(max(contours, key=cv2.contourArea))
region_crop = region[y:y+h, x:x+w]
if region_crop.size == 0:
continue
features_df = feature_extraction(region_crop)
features_scaled = scaler.transform(features_df)
prediction = model.predict(features_scaled)
predicted_class = label_encoder.inverse_transform(prediction)[0]
confidence = np.max(model.predict_proba(features_scaled))
region_predictions.append({
"class": predicted_class,
"confidence": confidence,
"bbox": (x, y, w, h)
})
cv2.rectangle(output, (x, y), (x+w, y+h), (0,255,0), 2)
cv2.putText(output, f"{predicted_class} ({confidence*100:.1f}%)", (x, y-10),
cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255,255,255), 1)
return output, region_predictions
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