Update README.md

README.md

@@ -145,15 +145,53 @@ import tensorflow as tf
 import cv2
 import numpy as np
 
-model = tf.keras.models.load_model("model_path", compile=False)
+# -------- Load model --------
+model = tf.keras.models.load_model(
+    "model_path",
+    compile=False
+)
+
+# -------- Load & preprocess image --------
+img_path = "image_path"
+
+orig = cv2.imread(img_path)
+orig = cv2.cvtColor(orig, cv2.COLOR_BGR2RGB)
+
+img = cv2.resize(orig, (256, 256))
+img_norm = img / 255.0
+img_input = np.expand_dims(img_norm, axis=0)
+
+# -------- Predict --------
+pred = model.predict(img_input)
+mask = np.argmax(pred[0], axis=-1)  # (256, 256)
+
+# -------- Create color mask --------
+# Class mapping:
+# 0 = background, 1 = road, 2 = lane
+
+color_mask = np.zeros((256, 256, 3), dtype=np.uint8)
+
+color_mask[mask == 1] = (255, 0, 0)   # Road -> Red
+color_mask[mask == 2] = (0, 255, 0)   # Lane -> Green
+
+# -------- Overlay full segmentation --------
+overlay_full = cv2.addWeighted(img.astype(np.uint8), 0.6, color_mask, 0.4, 0)
+
+# -------- Lane-only overlay --------
+lane_mask = np.zeros_like(color_mask)
+lane_mask[mask == 2] = (0, 255, 0)
+
+overlay_lane = cv2.addWeighted(img.astype(np.uint8), 0.7, lane_mask, 0.3, 0)
+
+# -------- Show results --------
+cv2.imshow("Original", cv2.cvtColor(img.astype(np.uint8), cv2.COLOR_RGB2BGR))
+cv2.imshow("Segmentation Overlay (Road + Lane)", cv2.cvtColor(overlay_full, cv2.COLOR_RGB2BGR))
+cv2.imshow("Lane Only Overlay", cv2.cvtColor(overlay_lane, cv2.COLOR_RGB2BGR))
+
+cv2.waitKey(0)
+cv2.destroyAllWindows()
 
-img = cv2.imread("road.jpg")
-img = cv2.resize(img, (INPUT_W, INPUT_H))
-img = img / 255.0
-img = np.expand_dims(img, axis=0)
 
-pred = model.predict(img)
-mask = np.argmax(pred[0], axis=-1)
 ```
 
 For full pipelines including video processing and lane visualization,