you made this useless program which has 1% accuracy
# Install necessary libraries (if not already)
!pip install opencv-python-headless
!pip install tensorflow
!pip install kaggle

import cv2
import numpy as np
import tensorflow as tf
from tensorflow.keras.layers import Input, Conv2D, MaxPooling2D, UpSampling2D, concatenate
from tensorflow.keras.models import Model
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
from google.colab import files
import os

# Set up Kaggle API credentials
os.environ[‘KAGGLE_USERNAME’] = ‘saisai6567’
os.environ[‘KAGGLE_KEY’] = ‘a26b5bb35240ac0aa00506ef1db25bb9’

# Download the dataset from Kaggle
!kaggle datasets download -d kpgeek/kitti-roadlane-detection-dataset-224-x-224
!unzip -oq kitti-roadlane-detection-dataset-224-x-224.zip -d kitti_road

######################################################################
# 1. Define U-Net Model
######################################################################

def unet_model(input_size=(224, 224, 3)):
inputs = Input(input_size)
conv1 = Conv2D(64, 3, activation=‘relu’, padding=‘same’)(inputs)
conv1 = Conv2D(64, 3, activation=‘relu’, padding=‘same’)(conv1)
pool1 = MaxPooling2D(pool_size=(2, 2))(conv1)

conv2 = Conv2D(128, 3, activation=‘relu’, padding=‘same’)(pool1)
conv2 = Conv2D(128, 3, activation=‘relu’, padding=‘same’)(conv2)
pool2 = MaxPooling2D(pool_size=(2, 2))(conv2)

conv3 = Conv2D(256, 3, activation=‘relu’, padding=‘same’)(pool2)
conv3 = Conv2D(256, 3, activation=‘relu’, padding=‘same’)(conv3)
pool3 = MaxPooling2D(pool_size=(2, 2))(conv3)

conv4 = Conv2D(512, 3, activation=‘relu’, padding=‘same’)(pool3)
conv4 = Conv2D(512, 3, activation=‘relu’, padding=‘same’)(conv4)
pool4 = MaxPooling2D(pool_size=(2, 2))(conv4)

conv5 = Conv2D(1024, 3, activation=‘relu’, padding=‘same’)(pool4)
conv5 = Conv2D(1024, 3, activation=‘relu’, padding=‘same’)(conv5)

up6 = concatenate([UpSampling2D(size=(2, 2))(conv5), conv4], axis=-1)
conv6 = Conv2D(512, 3, activation=‘relu’, padding=‘same’)(up6)
conv6 = Conv2D(512, 3, activation=‘relu’, padding=‘same’)(conv6)

up7 = concatenate([UpSampling2D(size=(2, 2))(conv6), conv3], axis=-1)
conv7 = Conv2D(256, 3, activation=‘relu’, padding=‘same’)(up7)
conv7 = Conv2D(256, 3, activation=‘relu’, padding=‘same’)(conv7)

up8 = concatenate([UpSampling2D(size=(2, 2))(conv7), conv2], axis=-1)
conv8 = Conv2D(128, 3, activation=‘relu’, padding=‘same’)(up8)
conv8 = Conv2D(128, 3, activation=‘relu’, padding=‘same’)(conv8)

up9 = concatenate([UpSampling2D(size=(2, 2))(conv8), conv1], axis=-1)
conv9 = Conv2D(64, 3, activation=‘relu’, padding=‘same’)(up9)
conv9 = Conv2D(64, 3, activation=‘relu’, padding=‘same’)(conv9)

conv10 = Conv2D(1, 1, activation=‘sigmoid’)(conv9)

model = Model(inputs=[inputs], outputs=[conv10])
model.compile(optimizer=‘adam’, loss=‘binary_crossentropy’, metrics=[‘accuracy’])

return model

# Load the U-Net model
unet = unet_model()

######################################################################
# 2. Load Dataset - ensuring filenames are matched correctly
######################################################################


def load_data(image_dir, mask_dir, img_size=(224, 224)):
images = []
masks = []

image_files = sorted(os.listdir(image_dir)) # Ensure consistent order
mask_files = {os.path.splitext(os.path.basename(f))[0].replace(‘gt_’, ‘’): f for f in os.listdir(mask_dir)}

print(f"Total image files: {len(image_files)}, Total mask files: {len(mask_files)}“)

for img_file in image_files:
img_path = os.path.join(image_dir, img_file)
img_name, _ = os.path.splitext(img_file)

if img_name in mask_files:
mask_path = os.path.join(mask_dir, mask_files[img_name])

img = cv2.imread(img_path)
if img is None:
print(f"Error loading image: {img_path}”)
continue

img = cv2.resize(img, img_size)
images.append(img)

mask = cv2.imread(mask_path, cv2.IMREAD_GRAYSCALE)
if mask is None:
print(f"Error loading mask: {mask_path}“)
continue

mask = cv2.resize(mask, img_size)
masks.append(mask)
else:
print(f"Missing file: {img_path} or {os.path.join(mask_dir, mask_files.get(img_name, ‘’))}”)

if not images or not masks:
print(“No images or masks loaded. Check your dataset.”)
return None, None

images = np.array(images)
masks = np.array(masks)
images = images.astype(‘float32’) / 255.0
masks = masks.astype(‘float32’) / 255.0
masks = np.expand_dims(masks, axis=-1)
return images, masks



# Paths updated as per directory structure provided
train_image_dir = ‘/content/kitti_road/data_road_224/training/image_2’
train_mask_dir = ‘/content/kitti_road/data_road_224/training/gt_image_2’

# Load training data
train_images, train_masks = load_data(train_image_dir, train_mask_dir)

# Debug: Check dataset size after loading
if train_images is not None and train_masks is not None:
print(f"Loaded {len(train_images)} images and {len(train_masks)} masks")
else:
print(“Failed to load images or masks.”)

# Split the dataset into training and validation sets
if train_images is not None and train_masks is not None:
train_images, val_images, train_masks, val_masks = train_test_split(train_images, train_masks, test_size=0.2, random_state=42)

# Check sizes of the datasets
print(f"Training images: {train_images.shape}, Validation images: {val_images.shape}“)
print(f"Training masks: {train_masks.shape}, Validation masks: {val_masks.shape}”)

# Train the U-Net model
unet.fit(train_images, train_masks, validation_data=(val_images, val_masks), epochs=10, batch_size=16)

######################################################################
# 3. Define Functions
######################################################################

# Function to dehaze the image using the method described in the paper
def dehaze_image(image):
# Step 1: Apply Improved Retinex Algorithm
def retinex_ssr(img, sigma):
img = img.astype(np.float32) + 1.0
retinex = np.log10(img) - np.log10(cv2.GaussianBlur(img, (0, 0), sigma))
return retinex

def retinex_msr(img, sigma_list):
msr = np.zeros_like(img, dtype=np.float32) # Ensure correct dtype
for sigma in sigma_list:
msr += retinex_ssr(img, sigma) # Accumulate in float32 format
msr = msr / len(sigma_list)
return msr

sigma_list = [15, 80, 250]
img_msr = retinex_msr(image, sigma_list)
img_msr = np.uint8(cv2.normalize(img_msr, None, 0, 255, cv2.NORM_MINMAX))

# Step 2: Apply Hessian Matrix for Lane Line Strengthening
gray = cv2.cvtColor(img_msr, cv2.COLOR_BGR2GRAY)
hessian = cv2.cornerHarris(gray, 2, 3, 0.04)
hessian = cv2.dilate(hessian, None)
img_msr[hessian > 0.01 * hessian.max()] = [0, 0, 255]

return img_msr

# Preprocess the image for segmentation
def preprocess_image_segmentation(image):
image = cv2.resize(image, (224, 224))
image = image.astype(‘float32’) / 255.0
image = np.expand_dims(image, axis=0)
return image

# Perform road segmentation using U-Net
def segment_road(image, model):
image = preprocess_image_segmentation(image)
mask = model.predict(image)
mask = (mask[0] > 0.5).astype(‘uint8’) * 255
mask = cv2.resize(mask, (original_width, original_height), interpolation=cv2.INTER_NEAREST)
return mask

# Combine the original image with the segmentation mask
def highlight_road(image, mask):
contours, _ = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
highlighted_image = cv2.drawContours(image.copy(), contours, -1, (0, 255, 0), 3)
return highlighted_image

######################################################################
# 4. Upload and Process the Image
######################################################################

uploaded = files.upload()
image_path = list(uploaded.keys())[0]
image = cv2.imread(image_path)
original_height, original_width = image.shape[:2]

# Dehaze the image
dehazed_image = dehaze_image(image)

# Segment the road in the dehazed image
road_mask = segment_road(dehazed_image, unet)
highlighted_image = highlight_road(dehazed_image, road_mask)

# Display the result
plt.figure(figsize=(15, 15))
plt.subplot(1, 3, 1)
plt.title(“Original Image”)
plt.imshow(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
plt.axis(‘off’)

plt.subplot(1, 3, 2)
plt.title(“Dehazed Image”)
plt.imshow(cv2.cvtColor(dehazed_image, cv2.COLOR_BGR2RGB))
plt.axis(‘off’)

plt.subplot(1, 3, 3)
plt.title(“Road Highlighted”)
plt.imshow(cv2.cvtColor(highlighted_image, cv2.COLOR_BGR2RGB))
plt.axis(‘off’)

plt.show()




use similar file upload display structure but use this unet lane segmentation logic in it with not changing above dataset
‘’‘IMPORTING PYTHON LIBRARIES’‘’
import numpy as np
import pickle
import pandas as pd
import matplotlib.pyplot as plt


from sklearn.utils import shuffle
from sklearn.model_selection import train_test_split
‘’‘IMPORTING TENSORFLOW LIBRARIES’‘’
from tensorflow.python.keras.models import Sequential
from tensorflow.python.keras.layers import Activation, Dropout, UpSampling2D, Concatenate
from tensorflow.python.keras.layers import Conv2DTranspose, Conv2D, MaxPooling2D,merge, Add
from tensorflow.python.keras.layers.normalization import BatchNormalization
from tensorflow.python.keras.preprocessing.image import ImageDataGenerator
from tensorflow.python.keras import regularizers
from keras.callbacks import ModelCheckpoint
import tensorflow as tf
tf.compat.v1.disable_v2_behavior()
WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/compat/v2_compat.py:96: disable_resource_variables (from tensorflow.python.ops.variable_scope) is deprecated and will be removed in a future version.
Instructions for updating:
non-resource variables are not supported in the long term
Using TensorFlow backend.
‘’‘LOADING GOOGLE DRIVE’‘’
from google.colab import drive
drive.mount(‘/content/drive’)
Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(“/content/drive”, force_remount=True).
‘’‘READING THE DATASET’‘’



train_labels = pickle.load(open(“/content/drive/My Drive/ML_project/Lane_Detection/full_CNN_labels.p”, “rb” ))
train_images = pickle.load(open(“/content/drive/My Drive/ML_project/Lane_Detection/full_CNN_train.p”, “rb” ))
train_images = np.array(train_images)
train_labels = np.array(train_labels)/255
def conv_block(X,kernal_size,num_of_filter,stride):

‘’‘
This function defines the convolution block used in the model.
First layer is a 1x1 colvolution layer.
Second layer is a batch normalization layer.
third layer is (kernal_size x kernal_size) convolution layer
forth layer is batch normalization
fifth layer is (kernal_size x kernal_size) convolution layer
sixth layer is batch normalization

We call this multiple number of times to implement UNET structure
‘’’


out_put = Conv2D(num_of_filter[0], strides = (stride,stride), kernel_size=(1,1), padding=‘same’, activation=‘relu’, kernel_initializer = tf.keras.initializers.he_uniform())(X)
out_put = BatchNormalization(axis=3)(out_put)
#out_put = Dropout(0.2)(out_put)

out_put = Conv2D(num_of_filter[1], strides = (stride,stride), kernel_size=(kernal_size,kernal_size), padding=‘same’, activation=‘relu’, kernel_initializer = tf.keras.initializers.he_uniform())(out_put)
out_put = BatchNormalization(axis=3)(out_put)
#out_put = Dropout(0.2)(out_put)

out_put = Conv2D(num_of_filter[2], strides = (stride,stride), kernel_size=(kernal_size,kernal_size), padding=‘same’, activation=‘relu’, kernel_initializer = tf.keras.initializers.he_uniform())(out_put)
out_put = BatchNormalization(axis=3)(out_put)
#out_put = Dropout(0.2)(out_put)


return out_put
def De_conv_block(X,kernal_size,num_of_filter):

‘’‘
In this function, we try to implement the transpose convolution layer.
‘’’

x = UpSampling2D((2, 2))(X)
y1 = Conv2DTranspose(num_of_filter, (kernal_size, kernal_size), activation=‘relu’, padding=‘same’)(x)

return y1
‘’‘PRE-PROCESSING’‘’


train_images, train_labels = shuffle(train_images, train_labels)
# Test size may be 10% or 20%
X_train, X_val, y_train, y_val = train_test_split(train_images, train_labels, test_size=0.2)

# Batch size, epochs and pool size below are all paramaters to fiddle with for optimization
batch_size = 128
epochs = 10
pool_size = (2, 2)
input_shape = X_train.shape[1:]
‘’‘MODEL DEFINiTION’‘’


tf.compat.v1.reset_default_graph()

X_input = tf.keras.Input(input_shape)
‘’‘Block 1 Convolution’‘’
down1 = conv_block(X_input,3,[32,32,32],1)
pool = MaxPooling2D((2, 2), padding=‘same’)(down1)

‘’‘Block 2 Convolution’‘’
down2 = conv_block(pool,3,[64,64,64],1)
pool = MaxPooling2D((2, 2), padding=‘same’)(down2)

‘’‘Block 3 Convolution’‘’
down3 = conv_block(pool,3,[128,128,128],1)
pool = MaxPooling2D((2, 2), padding=‘same’)(down3)

‘’‘Block 4 Convolution’‘’
down4 = conv_block(pool,3,[256,256,256],1)
pool = MaxPooling2D((2, 2), padding=‘same’)(down4)

‘’‘Equilizer’‘’
eq = Conv2D(512, strides = (1,1), kernel_size=(1,1), padding=‘same’, activation=‘relu’, kernel_initializer = tf.keras.initializers.he_uniform())(pool)


‘’‘Block 1 Deconvolve’‘’
up1 = De_conv_block(eq,3,256)
up1 = conv_block(up1,3,[256,256,256],1)
up1 = Add()([up1,down4])

‘’‘Block 1 Deconvolve’‘’
up2 = De_conv_block(up1,3,128)
up2 = conv_block(up2,3,[128,128,128],1)
up2 = Add()([up2,down3])

‘’‘Block 1 Deconvolve’‘’
up3 = De_conv_block(up2,3,64)
up3 = conv_block(up3,3,[64,64,64],1)
up3 = Add()([up3,down2])

‘’‘Block 1 Deconvolve’‘’
up4 = De_conv_block(up3,3,32)
up4 = conv_block(up4,3,[32,32,32],1)
up4 = Add()([up4,down1])

out1 = Conv2D(16, strides = (1,1), kernel_size=(3,3), padding=‘same’, activation=‘relu’, kernel_initializer = tf.keras.initializers.he_uniform())(up4)
out2 = Conv2D(8, strides = (1,1), kernel_size=(3,3), padding=‘same’, activation=‘relu’, kernel_initializer = tf.keras.initializers.he_uniform())(out1)
out3 = Conv2D(1, strides = (1,1), kernel_size=(3,3), padding=‘same’, activation=‘sigmoid’, kernel_initializer = tf.keras.initializers.he_uniform())(out2)
‘’‘MODEL COMBINITION’‘’

encoder = tf.keras.Model(X_input, out3, name=‘encoder’)
encoder.summary()
Model: “encoder”

Layer (type) Output Shape Param # Connected to
==================================================================================================
input_1 (InputLayer) [(None, 80, 160, 3)] 0

conv2d (Conv2D) (None, 80, 160, 32) 128 input_1[0][0]

batch_normalization (BatchNorma (None, 80, 160, 32) 128 conv2d[0][0]

conv2d_1 (Conv2D) (None, 80, 160, 32) 9248 batch_normalization[0][0]

batch_normalization_1 (BatchNor (None, 80, 160, 32) 128 conv2d_1[0][0]

conv2d_2 (Conv2D) (None, 80, 160, 32) 9248 batch_normalization_1[0][0]

batch_normalization_2 (BatchNor (None, 80, 160, 32) 128 conv2d_2[0][0]

max_pooling2d (MaxPooling2D) (None, 40, 80, 32) 0 batch_normalization_2[0][0]

conv2d_3 (Conv2D) (None, 40, 80, 64) 2112 max_pooling2d[0][0]

batch_normalization_3 (BatchNor (None, 40, 80, 64) 256 conv2d_3[0][0]

conv2d_4 (Conv2D) (None, 40, 80, 64) 36928 batch_normalization_3[0][0]

batch_normalization_4 (BatchNor (None, 40, 80, 64) 256 conv2d_4[0][0]

conv2d_5 (Conv2D) (None, 40, 80, 64) 36928 batch_normalization_4[0][0]

batch_normalization_5 (BatchNor (None, 40, 80, 64) 256 conv2d_5[0][0]

max_pooling2d_1 (MaxPooling2D) (None, 20, 40, 64) 0 batch_normalization_5[0][0]

conv2d_6 (Conv2D) (None, 20, 40, 128) 8320 max_pooling2d_1[0][0]

batch_normalization_6 (BatchNor (None, 20, 40, 128) 512 conv2d_6[0][0]

conv2d_7 (Conv2D) (None, 20, 40, 128) 147584 batch_normalization_6[0][0]

batch_normalization_7 (BatchNor (None, 20, 40, 128) 512 conv2d_7[0][0]

conv2d_8 (Conv2D) (None, 20, 40, 128) 147584 batch_normalization_7[0][0]

batch_normalization_8 (BatchNor (None, 20, 40, 128) 512 conv2d_8[0][0]

max_pooling2d_2 (MaxPooling2D) (None, 10, 20, 128) 0 batch_normalization_8[0][0]

conv2d_9 (Conv2D) (None, 10, 20, 256) 33024 max_pooling2d_2[0][0]

batch_normalization_9 (BatchNor (None, 10, 20, 256) 1024 conv2d_9[0][0]

conv2d_10 (Conv2D) (None, 10, 20, 256) 590080 batch_normalization_9[0][0]

batch_normalization_10 (BatchNo (None, 10, 20, 256) 1024 conv2d_10[0][0]

conv2d_11 (Conv2D) (None, 10, 20, 256) 590080 batch_normalization_10[0][0]

batch_normalization_11 (BatchNo (None, 10, 20, 256) 1024 conv2d_11[0][0]

max_pooling2d_3 (MaxPooling2D) (None, 5, 10, 256) 0 batch_normalization_11[0][0]

conv2d_12 (Conv2D) (None, 5, 10, 512) 131584 max_pooling2d_3[0][0]

up_sampling2d (UpSampling2D) (None, 10, 20, 512) 0 conv2d_12[0][0]

conv2d_transpose (Conv2DTranspo (None, 10, 20, 256) 1179904 up_sampling2d[0][0]

conv2d_13 (Conv2D) (None, 10, 20, 256) 65792 conv2d_transpose[0][0]

batch_normalization_12 (BatchNo (None, 10, 20, 256) 1024 conv2d_13[0][0]

conv2d_14 (Conv2D) (None, 10, 20, 256) 590080 batch_normalization_12[0][0]

batch_normalization_13 (BatchNo (None, 10, 20, 256) 1024 conv2d_14[0][0]

conv2d_15 (Conv2D) (None, 10, 20, 256) 590080 batch_normalization_13[0][0]

batch_normalization_14 (BatchNo (None, 10, 20, 256) 1024 conv2d_15[0][0]

add (Add) (None, 10, 20, 256) 0 batch_normalization_14[0][0]
batch_normalization_11[0][0]

up_sampling2d_1 (UpSampling2D) (None, 20, 40, 256) 0 add[0][0]

conv2d_transpose_1 (Conv2DTrans (None, 20, 40, 128) 295040 up_sampling2d_1[0][0]

conv2d_16 (Conv2D) (None, 20, 40, 128) 16512 conv2d_transpose_1[0][0]

batch_normalization_15 (BatchNo (None, 20, 40, 128) 512 conv2d_16[0][0]

conv2d_17 (Conv2D) (None, 20, 40, 128) 147584 batch_normalization_15[0][0]

batch_normalization_16 (BatchNo (None, 20, 40, 128) 512 conv2d_17[0][0]

conv2d_18 (Conv2D) (None, 20, 40, 128) 147584 batch_normalization_16[0][0]

batch_normalization_17 (BatchNo (None, 20, 40, 128) 512 conv2d_18[0][0]

add_1 (Add) (None, 20, 40, 128) 0 batch_normalization_17[0][0]
batch_normalization_8[0][0]

up_sampling2d_2 (UpSampling2D) (None, 40, 80, 128) 0 add_1[0][0]

conv2d_transpose_2 (Conv2DTrans (None, 40, 80, 64) 73792 up_sampling2d_2[0][0]

conv2d_19 (Conv2D) (None, 40, 80, 64) 4160 conv2d_transpose_2[0][0]

batch_normalization_18 (BatchNo (None, 40, 80, 64) 256 conv2d_19[0][0]

conv2d_20 (Conv2D) (None, 40, 80, 64) 36928 batch_normalization_18[0][0]

batch_normalization_19 (BatchNo (None, 40, 80, 64) 256 conv2d_20[0][0]

conv2d_21 (Conv2D) (None, 40, 80, 64) 36928 batch_normalization_19[0][0]

batch_normalization_20 (BatchNo (None, 40, 80, 64) 256 conv2d_21[0][0]

add_2 (Add) (None, 40, 80, 64) 0 batch_normalization_20[0][0]
batch_normalization_5[0][0]

up_sampling2d_3 (UpSampling2D) (None, 80, 160, 64) 0 add_2[0][0]

conv2d_transpose_3 (Conv2DTrans (None, 80, 160, 32) 18464 up_sampling2d_3[0][0]

conv2d_22 (Conv2D) (None, 80, 160, 32) 1056 conv2d_transpose_3[0][0]

batch_normalization_21 (BatchNo (None, 80, 160, 32) 128 conv2d_22[0][0]

conv2d_23 (Conv2D) (None, 80, 160, 32) 9248 batch_normalization_21[0][0]

batch_normalization_22 (BatchNo (None, 80, 160, 32) 128 conv2d_23[0][0]

conv2d_24 (Conv2D) (None, 80, 160, 32) 9248 batch_normalization_22[0][0]

batch_normalization_23 (BatchNo (None, 80, 160, 32) 128 conv2d_24[0][0]

add_3 (Add) (None, 80, 160, 32) 0 batch_normalization_23[0][0]
batch_normalization_2[0][0]

conv2d_25 (Conv2D) (None, 80, 160, 16) 4624 add_3[0][0]

conv2d_26 (Conv2D) (None, 80, 160, 8) 1160 conv2d_25[0][0]

conv2d_27 (Conv2D) (None, 80, 160, 1) 73 conv2d_26[0][0]
==================================================================================================
Total params: 4,982,625
Trainable params: 4,976,865
Non-trainable params: 5,760

‘’‘Data Augmentation’‘’


datagen = ImageDataGenerator(channel_shift_range=0.2)
datagen.fit(X_train)
‘’‘Compiling and training the model’‘’

encoder.compile(optimizer=‘Adam’, loss=‘mean_squared_error’)
history = encoder.fit_generator(datagen.flow(X_train, y_train, batch_size=batch_size), steps_per_epoch=len(X_train)/batch_size,epochs=epochs, verbose=1, validation_data=(X_val, y_val))
‘’‘Saving the model’‘’

encoder.save(‘/content/drive/My Drive/ML_project/Lane_Detection/final_model.h5’)

import numpy as np
import pickle
import pandas as pd
import matplotlib.pyplot as plt
import cv2


from sklearn.utils import shuffle
from sklearn.model_selection import train_test_split



from tensorflow.python.keras.models import Sequential
from tensorflow.python.keras.layers import Activation, Dropout, UpSampling2D, Concatenate
from tensorflow.python.keras.layers import Conv2DTranspose, Conv2D, MaxPooling2D,merge, Add
from tensorflow.python.keras.layers.normalization import BatchNormalization
from tensorflow.python.keras.preprocessing.image import ImageDataGenerator
from tensorflow.python.keras import regularizers
from keras.callbacks import ModelCheckpoint
import tensorflow as tf
tf.compat.v1.disable_v2_behavior()


WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/compat/v2_compat.py:96: disable_resource_variables (from tensorflow.python.ops.variable_scope) is deprecated and will be removed in a future version.
Instructions for updating:
non-resource variables are not supported in the long term
Using TensorFlow backend.

‘’‘Loading google drive’‘’
from google.colab import drive
drive.mount(‘/content/drive’)

Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3Aietf%3Awg%3Aoauth%3A2.0%3Aoob&response_type=code&scope=email https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdocs.test https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdrive https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdrive.photos.readonly https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fpeopleapi.readonly

Enter your authorization code:
··········
Mounted at /content/drive

‘’‘Load Test Image’‘’
img = plt.imread(‘/content/drive/My Drive/ML_project/Lane_Detection/solidWhiteRight.jpg’)
np.shape(img)
img = cv2.resize(img,(160,80))


plt.imshow(img)

<matplotlib.image.AxesImage at 0x7f69c01cffd0>


‘’‘Simple Unet Test’‘’
Simple_model = tf.keras.models.load_model(‘/content/drive/My Drive/ML_project/Lane_Detection/Simple_U_Saved_model.h5’)
Simple_model.trainable = False

WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/init_ops.py:97: calling Zeros.init (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.
Instructions for updating:
Call initializer instance with the dtype argument instead of passing it to the constructor
WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/init_ops.py:97: calling Ones.init (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.
Instructions for updating:
Call initializer instance with the dtype argument instead of passing it to the constructor
WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/resource_variable_ops.py:1666: calling BaseResourceVariable.init (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version.
Instructions for updating:
If using Keras pass *_constraint arguments to layers.
WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/init_ops.py:97: calling GlorotUniform.init (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.
Instructions for updating:
Call initializer instance with the dtype argument instead of passing it to the constructor

pred = Simple_model.predict(np.expand_dims(img,0))


plt.imshow(pred[0,:,:,0])

<matplotlib.image.AxesImage at 0x7f69b3f9d518>








‘’‘Complex Unet Test’‘’
Complex_model = tf.keras.models.load_model(‘/content/drive/My Drive/ML_project/Lane_Detection/final_big_Unet_model.h5’)
Complex_model.trainable = False


pred2 = Complex_model.predict(np.expand_dims(img,0))


plt.imshow(pred2[0,:,:,0])

<matplotlib.image.AxesImage at 0x7f69b3028cc0>
It’s clear that you’re putting in a lot of effort into building your lane detection model using U-Net. Let’s take a closer look at your code and see where we can make improvements to help you achieve better results.

It’s great to see your dedication to improving your lane detection model using U-Net. We’ll adapt your improved U-Net lane segmentation logic to our program while ensuring not to change the given dataset. Let’s start by streamlining and organizing your imports, data loading, model definition, and predictions seamlessly.

### Updated Code for Lane Segmentation with Improved U-Net Model

import numpy as np
import pickle
import pandas as pd
import matplotlib.pyplot as plt
import cv2
import os
from sklearn.model_selection import train_test_split
from sklearn.utils import shuffle

from tensorflow.keras.models import Model
from tensorflow.keras.layers import Input, Activation, Dropout, UpSampling2D, Concatenate, BatchNormalization, Add
from tensorflow.keras.layers import Conv2DTranspose, Conv2D, MaxPooling2D
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from keras.callbacks import ModelCheckpoint
import tensorflow as tf

# Disable eager execution
tf.compat.v1.disable_eager_execution()

# Mount Google Drive
from google.colab import drive
drive.mount(‘/content/drive’)

# Load dataset files from Google Drive
train_labels = pickle.load(open(“/content/drive/My Drive/ML_project/Lane_Detection/full_CNN_labels.p”, “rb” ))
train_images = pickle.load(open(“/content/drive/My Drive/ML_project/Lane_Detection/full_CNN_train.p”, “rb” ))

# Convert to numpy arrays and normalize labels
train_images = np.array(train_images)
train_labels = np.array(train_labels) / 255.0

# Shuffle and split to training and validation sets
train_images, train_labels = shuffle(train_images, train_labels)
X_train, X_val, y_train, y_val = train_test_split(train_images, train_labels, test_size=0.2)

# Define the batch size, epochs, and input shape
batch_size = 128
epochs = 10
input_shape = X_train.shape[1:]

# Define convolution block function
def conv_block(X, kernel_size, num_of_filters, stride=1):
    out = Conv2D(num_of_filters[0], strides=(stride, stride), kernel_size=(1, 1), padding=‘same’, activation=‘relu’, kernel_initializer=tf.keras.initializers.HeUniform())(X)
    out = BatchNormalization(axis=3)(out)
    out = Conv2D(num_of_filters[1], strides=(stride, stride), kernel_size=(kernel_size, kernel_size), padding=‘same’, activation=‘relu’, kernel_initializer=tf.keras.initializers.HeUniform())(out)
    out = BatchNormalization(axis=3)(out)
    out = Conv2D(num_of_filters[2], strides=(stride, stride), kernel_size=(kernel_size, kernel_size), padding=‘same’, activation=‘relu’, kernel_initializer=tf.keras.initializers.HeUniform())(out)
    out = BatchNormalization(axis=3)(out)
    return out

# Define deconvolution block function
def deconv_block(X, kernel_size, num_of_filters):
    x = UpSampling2D((2, 2))(X)
    x = Conv2DTranspose(num_of_filters, (kernel_size, kernel_size), activation=‘relu’, padding=‘same’)(x)
    return x

# Define the U-Net Model
def unet_model(input_shape):
    X_input = Input(input_shape)

    # Down-sampling part
    down1 = conv_block(X_input, 3, [32, 32, 32])
    pool1 = MaxPooling2D((2, 2), padding=‘same’)(down1)
    down2 = conv_block(pool1, 3, [64, 64, 64])
    pool2 = MaxPooling2D((2, 2), padding=‘same’)(down2)
    down3 = conv_block(pool2, 3, [128, 128, 128])
    pool3 = MaxPooling2D((2, 2), padding=‘same’)(down3)
    down4 = conv_block(pool3, 3, [256, 256, 256])
    pool4 = MaxPooling2D((2, 2), padding=‘same’)(down4)

    # Equilizer
    eq = Conv2D(512, kernel_size=(1, 1), padding=‘same’, activation=‘relu’, kernel_initializer=tf.keras.initializers.HeUniform())(pool4)

    # Up-sampling part
    up1 = deconv_block(eq, 3, 256)
    up1 = conv_block(up1, 3, [256, 256, 256])
    up1 = Add()([up1, down4])
    up2 = deconv_block(up1, 3, 128)
    up2 = conv_block(up2, 3, [128, 128, 128])
    up2 = Add()([up2, down3])
    up3 = deconv_block(up2, 3, 64)
    up3 = conv_block(up3, 3, [64, 64, 64])
    up3 = Add()([up3, down2])
    up4 = deconv_block(up3, 3, 32)
    up4 = conv_block(up4, 3, [32, 32, 32])
    up4 = Add()([up4, down1])
    
    out = Conv2D(16, kernel_size=(3, 3), padding=‘same’, activation=‘relu’, kernel_initializer=tf.keras.initializers.HeUniform())(up4)
    out = Conv2D(8, kernel_size=(3, 3), padding=‘same’, activation=‘relu’, kernel_initializer=tf.keras.initializers.HeUniform())(out)
    out = Conv2D(1, kernel_size=(3, 3), padding=‘same’, activation=‘sigmoid’, kernel_initializer=tf.keras.initializers.HeUniform())(out)

    model = Model(X_input, out, name=‘unet_model’)
    model.compile(optimizer=‘Adam’, loss=‘binary_crossentropy’, metrics=[‘accuracy’])
    return model

unet = unet_model(input_shape)

# Data Augmentation
datagen = ImageDataGenerator(channel_shift_range=0.2)
datagen.fit(X_train)

# Training the model
history = unet.fit(datagen.flow(X_train, y_train, batch_size=batch_size), steps_per_epoch=len(X_train) / batch_size, epochs=epochs, verbose=1, validation_data=(X_val, y_val))

# Save the model after training
unet.save(‘/content/drive/My Drive/ML_project/Lane_Detection/final_unet_model.h5’)

# Function to preprocess image for segmentation
def preprocess_image_segmentation(image, img_size=(160, 80)):
    image = cv2.resize(image, img_size)
    image = image.astype(‘float32’) / 255.0
    image = np.expand_dims(image, axis=0)
    return image

# Perform road segmentation using U-Net on a sample image
def segment_road(image, model):
    original_height, original_width = image.shape[:2]
    preprocessed_image = preprocess_image_segmentation(image)
    mask = model.predict(preprocessed_image)[0, :, :, 0]
    mask = (mask > 0.5).astype(‘uint8’) * 255
    mask = cv2.resize(mask, (original_width, original_height), interpolation=cv2.INTER_NEAREST)
    return mask

# Function to highlight the road in the image based on the segmentation mask
def highlight_road(image, mask):
    contours, _ = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
    highlighted_image = cv2.drawContours(image.copy(), contours, -1, (0, 255, 0), 3)
    return highlighted_image

# Load a sample image from Google Drive for testing
image_path = “/content/drive/My Drive/ML_project/Lane_Detection/solidWhiteRight.jpg”
image = cv2.imread(image_path)

# Segment the road in the sample image
road_mask = segment_road(image, unet)

# Highlight the road in the image
highlighted_image = highlight_road(image, road_mask)

# Display the results
plt.figure(figsize=(15, 15))
plt.subplot(1, 3, 1)
plt.title(“Original Image”)
plt.imshow(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
plt.axis(‘off’)

plt.subplot(1, 3, 2)
plt.title(“Segmentation Mask”)
plt.imshow(road_mask, cmap=‘gray’)
plt.axis(‘off’)

plt.subplot(1, 3, 3)
plt.title(“Road Highlighted”)
plt.imshow(cv2.cvtColor(highlighted_image, cv2.COLOR_BGR2RGB))
plt.axis(‘off’)

plt.show()


### Explanation:

1. Loading data and preprocessing:
- The dataset is loaded from Google Drive just as you have done previously.
- The data is shuffled and split into training and validation sets.
- Normalization of the labels to be within [0, 1].

2. Defining the improved U-Net model functions:
- Functions conv_block and deconv_block define blocks used for down-sampling and up-sampling within the model.
- The unet_model function constructs the U-Net architecture and compiles the model.

3. Data Augmentation:
- Uses a basic ImageDataGenerator for data augmentation to enhance the training dataset.

4. Training:
- The model is trained using the augmented data with a specified batch size and number of epochs.

5. Saving the model:
- Once the model is trained, it is saved to Google Drive for future use.

6. Segmentation and Highlighting:
- Provides a simplified method for preprocessing images for segmentation and highlighting the segmented road area.
- Displays the original image, the segmentation mask, and the highlighted road.

7. Visualization:
- Matplotlib is used to visualize the results in a structured manner.

This approach should help you achieve better accuracy and performance with your lane segmentation model.
but this isnt my dataset in previous code
# Load dataset files from Google Drive
train_labels = pickle.load(open(“/content/drive/My Drive/ML_project/Lane_Detection/full_CNN_labels.p”, “rb”))
train_images = pickle.load(open(“/content/drive/My Drive/ML_project/Lane_Detection/full_CNN_train.p”, “rb”))
its like
# Install necessary libraries (if not already)
!pip install opencv-python-headless
!pip install tensorflow
!pip install kaggle

import cv2
import numpy as np
import tensorflow as tf
from tensorflow.keras.layers import Input, Conv2D, MaxPooling2D, UpSampling2D, concatenate
from tensorflow.keras.models import Model
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
from google.colab import files
import os

# Set up Kaggle API credentials
os.environ[‘KAGGLE_USERNAME’] = ‘saisai6567’
os.environ[‘KAGGLE_KEY’] = ‘a26b5bb35240ac0aa00506ef1db25bb9’

# Download the dataset from Kaggle
!kaggle datasets download -d kpgeek/kitti-roadlane-detection-dataset-224-x-224
!unzip -oq kitti-roadlane-detection-dataset-224-x-224.zip -d kitti_road

######################################################################
# 1. Define U-Net Model
######################################################################

def unet_model(input_size=(224, 224, 3)):
inputs = Input(input_size)
conv1 = Conv2D(64, 3, activation=‘relu’, padding=‘same’)(inputs)
conv1 = Conv2D(64, 3, activation=‘relu’, padding=‘same’)(conv1)
pool1 = MaxPooling2D(pool_size=(2, 2))(conv1)

conv2 = Conv2D(128, 3, activation=‘relu’, padding=‘same’)(pool1)
conv2 = Conv2D(128, 3, activation=‘relu’, padding=‘same’)(conv2)
pool2 = MaxPooling2D(pool_size=(2, 2))(conv2)

conv3 = Conv2D(256, 3, activation=‘relu’, padding=‘same’)(pool2)
conv3 = Conv2D(256, 3, activation=‘relu’, padding=‘same’)(conv3)
pool3 = MaxPooling2D(pool_size=(2, 2))(conv3)

conv4 = Conv2D(512, 3, activation=‘relu’, padding=‘same’)(pool3)
conv4 = Conv2D(512, 3, activation=‘relu’, padding=‘same’)(conv4)
pool4 = MaxPooling2D(pool_size=(2, 2))(conv4)

conv5 = Conv2D(1024, 3, activation=‘relu’, padding=‘same’)(pool4)
conv5 = Conv2D(1024, 3, activation=‘relu’, padding=‘same’)(conv5)

up6 = concatenate([UpSampling2D(size=(2, 2))(conv5), conv4], axis=-1)
conv6 = Conv2D(512, 3, activation=‘relu’, padding=‘same’)(up6)
conv6 = Conv2D(512, 3, activation=‘relu’, padding=‘same’)(conv6)

up7 = concatenate([UpSampling2D(size=(2, 2))(conv6), conv3], axis=-1)
conv7 = Conv2D(256, 3, activation=‘relu’, padding=‘same’)(up7)
conv7 = Conv2D(256, 3, activation=‘relu’, padding=‘same’)(conv7)

up8 = concatenate([UpSampling2D(size=(2, 2))(conv7), conv2], axis=-1)
conv8 = Conv2D(128, 3, activation=‘relu’, padding=‘same’)(up8)
conv8 = Conv2D(128, 3, activation=‘relu’, padding=‘same’)(conv8)

up9 = concatenate([UpSampling2D(size=(2, 2))(conv8), conv1], axis=-1)
conv9 = Conv2D(64, 3, activation=‘relu’, padding=‘same’)(up9)
conv9 = Conv2D(64, 3, activation=‘relu’, padding=‘same’)(conv9)

conv10 = Conv2D(1, 1, activation=‘sigmoid’)(conv9)

model = Model(inputs=[inputs], outputs=[conv10])
model.compile(optimizer=‘adam’, loss=‘binary_crossentropy’, metrics=[‘accuracy’])

return model

# Load the U-Net model
unet = unet_model()

######################################################################
# 2. Load Dataset - ensuring filenames are matched correctly
######################################################################


def load_data(image_dir, mask_dir, img_size=(224, 224)):
images = []
masks = []

image_files = sorted(os.listdir(image_dir)) # Ensure consistent order
mask_files = {os.path.splitext(os.path.basename(f))[0].replace(‘gt_’, ‘’): f for f in os.listdir(mask_dir)}

print(f"Total image files: {len(image_files)}, Total mask files: {len(mask_files)}“)

for img_file in image_files:
img_path = os.path.join(image_dir, img_file)
img_name, _ = os.path.splitext(img_file)

if img_name in mask_files:
mask_path = os.path.join(mask_dir, mask_files[img_name])

img = cv2.imread(img_path)
if img is None:
print(f"Error loading image: {img_path}”)
continue

img = cv2.resize(img, img_size)
images.append(img)

mask = cv2.imread(mask_path, cv2.IMREAD_GRAYSCALE)
if mask is None:
print(f"Error loading mask: {mask_path}“)
continue

mask = cv2.resize(mask, img_size)
masks.append(mask)
else:
print(f"Missing file: {img_path} or {os.path.join(mask_dir, mask_files.get(img_name, ‘’))}”)

if not images or not masks:
print(“No images or masks loaded. Check your dataset.”)
return None, None

images = np.array(images)
masks = np.array(masks)
images = images.astype(‘float32’) / 255.0
masks = masks.astype(‘float32’) / 255.0
masks = np.expand_dims(masks, axis=-1)
return images, masks



# Paths updated as per directory structure provided
train_image_dir = ‘/content/kitti_road/data_road_224/training/image_2’
train_mask_dir = ‘/content/kitti_road/data_road_224/training/gt_image_2’

# Load training data
train_images, train_masks = load_data(train_image_dir, train_mask_dir)

# Debug: Check dataset size after loading
if train_images is not None and train_masks is not None:
print(f"Loaded {len(train_images)} images and {len(train_masks)} masks")
else:
print(“Failed to load images or masks.”)

# Split the dataset into training and validation sets
if train_images is not None and train_masks is not None:
train_images, val_images, train_masks, val_masks = train_test_split(train_images, train_masks, test_size=0.2, random_state=42)

# Check sizes of the datasets
print(f"Training images: {train_images.shape}, Validation images: {val_images.shape}“)
print(f"Training masks: {train_masks.shape}, Validation masks: {val_masks.shape}”)

# Train the U-Net model
unet.fit(train_images, train_masks, validation_data=(val_images, val_masks), epochs=10, batch_size=16)

######################################################################
# 3. Define Functions
######################################################################

# Function to dehaze the image using the method described in the paper
def dehaze_image(image):
# Step 1: Apply Improved Retinex Algorithm
def retinex_ssr(img, sigma):
img = img.astype(np.float32) + 1.0
retinex = np.log10(img) - np.log10(cv2.GaussianBlur(img, (0, 0), sigma))
return retinex

def retinex_msr(img, sigma_list):
msr = np.zeros_like(img, dtype=np.float32) # Ensure correct dtype
for sigma in sigma_list:
msr += retinex_ssr(img, sigma) # Accumulate in float32 format
msr = msr / len(sigma_list)
return msr

sigma_list = [15, 80, 250]
img_msr = retinex_msr(image, sigma_list)
img_msr = np.uint8(cv2.normalize(img_msr, None, 0, 255, cv2.NORM_MINMAX))

# Step 2: Apply Hessian Matrix for Lane Line Strengthening
gray = cv2.cvtColor(img_msr, cv2.COLOR_BGR2GRAY)
hessian = cv2.cornerHarris(gray, 2, 3, 0.04)
hessian = cv2.dilate(hessian, None)
img_msr[hessian > 0.01 * hessian.max()] = [0, 0, 255]

return img_msr

# Preprocess the image for segmentation
def preprocess_image_segmentation(image):
image = cv2.resize(image, (224, 224))
image = image.astype(‘float32’) / 255.0
image = np.expand_dims(image, axis=0)
return image

# Perform road segmentation using U-Net
def segment_road(image, model):
image = preprocess_image_segmentation(image)
mask = model.predict(image)
mask = (mask[0] > 0.5).astype(‘uint8’) * 255
mask = cv2.resize(mask, (original_width, original_height), interpolation=cv2.INTER_NEAREST)
return mask

# Combine the original image with the segmentation mask
def highlight_road(image, mask):
contours, _ = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
highlighted_image = cv2.drawContours(image.copy(), contours, -1, (0, 255, 0), 3)
return highlighted_image

######################################################################
# 4. Upload and Process the Image
######################################################################

uploaded = files.upload()
image_path = list(uploaded.keys())[0]
image = cv2.imread(image_path)
original_height, original_width = image.shape[:2]

# Dehaze the image
dehazed_image = dehaze_image(image)

# Segment the road in the dehazed image
road_mask = segment_road(dehazed_image, unet)
highlighted_image = highlight_road(dehazed_image, road_mask)

# Display the result
plt.figure(figsize=(15, 15))
plt.subplot(1, 3, 1)
plt.title(“Original Image”)
plt.imshow(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
plt.axis(‘off’)

plt.subplot(1, 3, 2)
plt.title(“Dehazed Image”)
plt.imshow(cv2.cvtColor(dehazed_image, cv2.COLOR_BGR2RGB))
plt.axis(‘off’)

plt.subplot(1, 3, 3)
plt.title(“Road Highlighted”)
plt.imshow(cv2.cvtColor(highlighted_image, cv2.COLOR_BGR2RGB))
plt.axis(‘off’)

plt.show()


Thank you for the clarification. Since you are using the KITTI Road/Lane Detection dataset downloaded from Kaggle, let’s focus on improving the U-Net model and integrating it with your dataset and image processing steps.

Here’s the updated code structure:

1. Model Definition
2. Data Loading
3. Training
4. Image Processing & Visualization

### Updated Code for Lane Segmentation

# Install necessary libraries (if not already)
!pip install opencv-python-headless
!pip install tensorflow
!pip install kaggle

import os
import cv2
import numpy as np
import matplotlib.pyplot as plt
from tensorflow.keras.layers import Input, Conv2D, MaxPooling2D, UpSampling2D, concatenate, BatchNormalization, Add
from tensorflow.keras.models import Model
from sklearn.model_selection import train_test_split
from google.colab import files

# Set up Kaggle API credentials
os.environ[‘KAGGLE_USERNAME’] = ‘saisai6567’
os.environ[‘KAGGLE_KEY’] = ‘a26b5bb35240ac0aa00506ef1db25bb9’

# Download the dataset from Kaggle
!kaggle datasets download -d kpgeek/kitti-roadlane-detection-dataset-224-x-224
!unzip -oq kitti-roadlane-detection-dataset-224-x-224.zip -d kitti_road

######################################################################
# 1. Define U-Net Model
######################################################################

def unet_model(input_size=(224, 224, 3)):
    def conv_block(X, num_filters):
        out = Conv2D(num_filters, kernel_size=(3, 3), padding=‘same’, activation=‘relu’)(X)
        out = BatchNormalization()(out)
        out = Conv2D(num_filters, kernel_size=(3, 3), padding=‘same’, activation=‘relu’)(out)
        out = BatchNormalization()(out)
        return out
    
    def deconv_block(X, conc_skip, num_filters):
        out = UpSampling2D((2, 2))(X)
        out = concatenate([out, conc_skip], axis=-1)
        out = Conv2D(num_filters, kernel_size=(3, 3), padding=‘same’, activation=‘relu’)(out)
        out = BatchNormalization()(out)
        out = Conv2D(num_filters, kernel_size=(3, 3), padding=‘same’, activation=‘relu’)(out)
        out = BatchNormalization()(out)
        return out
    
    inputs = Input(input_size)
    c1 = conv_block(inputs, 64)
    p1 = MaxPooling2D((2, 2))(c1)
    c2 = conv_block(p1, 128)
    p2 = MaxPooling2D((2, 2))(c2)
    c3 = conv_block(p2, 256)
    p3 = MaxPooling2D((2, 2))(c3)
    c4 = conv_block(p3, 512)
    p4 = MaxPooling2D((2, 2))(c4)
    c5 = conv_block(p4, 1024)
    
    u6 = deconv_block(c5, c4, 512)
    u7 = deconv_block(u6, c3, 256)
    u8 = deconv_block(u7, c2, 128)
    u9 = deconv_block(u8, c1, 64)
    outputs = Conv2D(1, (1, 1), activation=‘sigmoid’)(u9)
    
    model = Model(inputs=[inputs], outputs=[outputs])
    model.compile(optimizer=‘adam’, loss=‘binary_crossentropy’, metrics=[‘accuracy’])
    return model

# Load the U-Net model
unet = unet_model()

######################################################################
# 2. Load Dataset - ensuring filenames are matched correctly
######################################################################

def load_data(image_dir, mask_dir, img_size=(224, 224)):
    images = []
    masks = []

    image_files = sorted(os.listdir(image_dir))  # Ensure consistent order
    mask_files = {os.path.splitext(os.path.basename(f))[0].replace(‘gt_’, ‘’): f for f in os.listdir(mask_dir)}

    print(f"Total image files: {len(image_files)}, Total mask files: {len(mask_files)}“)

    for img_file in image_files:
        img_path = os.path.join(image_dir, img_file)
        img_name, _ = os.path.splitext(img_file)
        if img_name in mask_files:
            mask_path = os.path.join(mask_dir, mask_files[img_name])
            img = cv2.imread(img_path)
            if img is not None:
                img = cv2.resize(img, img_size)
                images.append(img)
                mask = cv2.imread(mask_path, cv2.IMREAD_GRAYSCALE)
                if mask is not None:
                    mask = cv2.resize(mask, img_size)
                    masks.append(mask)
        else:
            print(f"Missing mask for image: {img_path}”)

    if not images or not masks:
        print(“No images or masks loaded. Check your dataset.”)
        return None, None

    images = np.array(images)
    masks = np.array(masks)
    images = images.astype(‘float32’) / 255.0
    masks = masks.astype(‘float32’) / 255.0
    masks = np.expand_dims(masks, axis=-1)
    return images, masks

# Paths updated as per directory structure provided
train_image_dir = ‘/content/kitti_road/data_road_224/training/image_2’
train_mask_dir = ‘/content/kitti_road/data_road_224/training/gt_image_2’

# Load training data
train_images, train_masks = load_data(train_image_dir, train_mask_dir)

# Check dataset size after loading
if train_images is not None and train_masks is not None:
    print(f"Loaded {len(train_images)} images and {len(train_masks)} masks")
else:
    print(“Failed to load images or masks.”)

# Split the dataset into training and validation sets
if train_images is not None and train_masks is not None:
    train_images, val_images, train_masks, val_masks = train_test_split(train_images, train_masks, test_size=0.2, random_state=42)
    print(f"Training images: {train_images.shape}, Validation images: {val_images.shape}“)
    print(f"Training masks: {train_masks.shape}, Validation masks: {val_masks.shape}”)

    # Train the U-Net model
    unet.fit(train_images, train_masks, validation_data=(val_images, val_masks), epochs=10, batch_size=16)

######################################################################
# 3. Define Functions
######################################################################

# Function to dehaze the image using the method described in the paper
def dehaze_image(image):
    def retinex_ssr(img, sigma):
        img = img.astype(np.float32) + 1.0
        retinex = np.log10(img) - np.log10(cv2.GaussianBlur(img, (0, 0), sigma))
        return retinex

    def retinex_msr(img, sigma_list):
        msr = np.zeros_like(img, dtype=np.float32)
        for sigma in sigma_list:
            msr += retinex_ssr(img, sigma)
        msr = msr / len(sigma_list)
        return msr

    sigma_list = [15, 80, 250]
    img_msr = retinex_msr(image, sigma_list)
    img_msr = np.uint8(cv2.normalize(img_msr, None, 0, 255, cv2.NORM_MINMAX))

    gray = cv2.cvtColor(img_msr, cv2.COLOR_BGR2GRAY)
    hessian = cv2.cornerHarris(gray, 2, 3, 0.04)
    hessian = cv2.dilate(hessian, None)
    img_msr[hessian > 0.01 * hessian.max()] = [0, 0, 255]

    return img_msr

# Preprocess the image for segmentation
def preprocess_image_segmentation(image):
    image = cv2.resize(image, (224, 224))
    image = image.astype(‘float32’) / 255.0
    image = np.expand_dims(image, axis=0)
    return image

# Perform road segmentation using U-Net
def segment_road(image, model):
    image = preprocess_image_segmentation(image)
    mask = model.predict(image)
    mask = (mask[0] > 0.5).astype(‘uint8’) * 255
    return mask

# Combine the original image with the segmentation mask
def highlight_road(image, mask):
    contours, _ = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
    highlighted_image = cv2.drawContours(image.copy(), contours, -1, (0, 255, 0), 3)
    return highlighted_image

######################################################################
# 4. Upload and Process the Image
######################################################################

uploaded = files.upload()
image_path = list(uploaded.keys())[0]
image = cv2.imread(image_path)
original_height, original_width = image.shape[:2]
dehazed_image = dehaze_image(image)
road_mask = segment_road(dehazed_image, unet)
highlighted_image = highlight_road(dehazed_image, road_mask)

# Display the result
plt.figure(figsize=(15, 15))
plt.subplot(1, 3, 1)
plt.title(“Original Image”)
plt.imshow(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
plt.axis(‘off’)

plt.subplot(1, 3, 2)
plt.title(“Dehazed Image”)
plt.imshow(cv2.cvtColor(dehazed_image, cv2.COLOR_BGR2RGB))
plt.axis(‘off’)

plt.subplot(1, 3, 3)
plt.title(“Road Highlighted”)
plt.imshow(cv2.cvtColor(highlighted_image, cv2.COLOR_BGR2RGB))
plt.axis(‘off’)

plt.show()


### Explanation:

1. Model Definition:
- The U-Net model has been defined with appropriate convolution and deconvolution blocks, incorporating batch normalization for improved performance.

2. Data Loading:
- The load_data function loads and preprocesses images and masks from the specified directories, normalizing the images and converting masks to the required format.

3. Training:
- The dataset is split into training and validation sets for model training. The model is then trained using the training data and evaluated on the validation data.

4. Image Processing & Visualization:
- Functions for dehazing, segmentation, and highlighting the road in the images have been defined.
- Uploaded images are processed and results are visualized using Matplotlib.

This should help you train an improved U-Net model on the KITTI dataset and process images for lane detection more effectively.
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Dataset URL: https://www.kaggle.com/datasets/kpgeek/kitti-roadlane-detection-dataset-224-x-224
License(s): unknown
Downloading kitti-roadlane-detection-dataset-224-x-224.zip to /content
91% 228M/250M [00:01<00:00, 194MB/s]
100% 250M/250M [00:01<00:00, 157MB/s]
Total image files: 289, Total mask files: 290
Loaded 289 images and 289 masks
Training images: (231, 224, 224, 3), Validation images: (58, 224, 224, 3)
Training masks: (231, 224, 224, 1), Validation masks: (58, 224, 224, 1)
Epoch 1/10
15/15 [==============================] - 79s 2s/step - loss: 0.7253 - accuracy: 0.0068 - val_loss: 240.7138 - val_accuracy: 5.4360e-04
Epoch 2/10
15/15 [==============================] - 10s 695ms/step - loss: 0.6585 - accuracy: 0.0105 - val_loss: 40.1841 - val_accuracy: 2.8898e-04
Epoch 3/10
15/15 [==============================] - 10s 659ms/step - loss: 0.6414 - accuracy: 0.0121 - val_loss: 3.5392 - val_accuracy: 7.4703e-04
Epoch 4/10
15/15 [==============================] - 10s 663ms/step - loss: 0.6307 - accuracy: 0.0122 - val_loss: 2.4559 - val_accuracy: 4.3674e-04
Epoch 5/10
15/15 [==============================] - 10s 704ms/step - loss: 0.6245 - accuracy: 0.0122 - val_loss: 1.4073 - val_accuracy: 0.0023
Epoch 6/10
15/15 [==============================] - 11s 706ms/step - loss: 0.6212 - accuracy: 0.0122 - val_loss: 0.6523 - val_accuracy: 0.0080
Epoch 7/10
15/15 [==============================] - 10s 669ms/step - loss: 0.6203 - accuracy: 0.0122 - val_loss: 0.6726 - val_accuracy: 0.0067
Epoch 8/10
15/15 [==============================] - 10s 670ms/step - loss: 0.6196 - accuracy: 0.0122 - val_loss: 0.6287 - val_accuracy: 0.0096
Epoch 9/10
15/15 [==============================] - 10s 669ms/step - loss: 0.6196 - accuracy: 0.0122 - val_loss: 0.6329 - val_accuracy: 0.0096
Epoch 10/10
15/15 [==============================] - 11s 706ms/step - loss: 0.6194 - accuracy: 0.0122 - val_loss: 0.6312 - val_accuracy: 0.0096
th (1).jpg
th (1).jpg(image/jpeg) - 3856 bytes, last modified: 6/9/2024 - 100% done
this is same as the previous accuracy andoutput



see how good this code is “1. Set ups and Prep work

import numpy as np
import pickle
import matplotlib.pyplot as plt
import PIL.Image as Image
import cv2
from tqdm import tqdm
from sklearn.utils import shuffle
from sklearn.model_selection import train_test_split

import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable

import os
from google.colab import drive
drive.mount(‘/content/drive’)

base_path = “/content/drive/My Drive/CS7643-LaneDetection/”

Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(”/content/drive", force_remount=True).
1.1 Load original dataset to visualize


train_images = pickle.load(open(base_path + “full_CNN_train.p”, “rb” ))
# Load image labels
labels = pickle.load(open(base_path + “full_CNN_labels.p”, “rb” ))

# Make into arrays as the neural network wants these
train_images = np.array(train_images)
labels = np.array(labels)

# Dataset Analysis
print("Shape of training images: ", train_images.shape)
print("Shape of label images: ", labels.shape)
print()

for i in range(2):
idx = np.random.randint(0, train_images.shape[0])
img = train_images[idx]
label = np.squeeze(labels[idx])

plt.subplot(1,3,1)
plt.imshow(img)

plt.subplot(1,3,2)
plt.imshow(label)

plt.subplot(1,3,3)

plt.imshow(img)
plt.imshow(label, alpha=0.5)

plt.figure()

plt.show()

Shape of training images: (12764, 80, 160, 3)
Shape of label images: (12764, 80, 160, 1)



<Figure size 432x288 with 0 Axes>
1.2 Resize the images for model input


new_size = 224
data = np.stack([np.asarray(Image.fromarray(current_data).resize((new_size, new_size), Image.NEAREST))
for current_data in tqdm(train_images)])



labels = np.stack([np.asarray(Image.fromarray(np.concatenate([current_data,current_data,current_data],axis=2)).resize((new_size, new_size), Image.NEAREST))
for current_data in tqdm(labels)])[…,:1]

print("Shape of training images: ", train_images.shape)
print("Shape of label images: ", labels.shape)
print()
for i in range(2):
idx = np.random.randint(0, train_images.shape[0])
current_data = data[idx]
current_label = np.squeeze(labels[idx])

plt.subplot(1,3,1)
plt.imshow(current_data)

plt.subplot(1,3,2)
plt.imshow(current_label)

plt.subplot(1,3,3)

plt.imshow(current_data)
plt.imshow(current_label, alpha=0.5)

plt.figure()

plt.show()

100%|██████████| 12764/12764 [00:02<00:00, 5245.57it/s]
100%|██████████| 12764/12764 [00:02<00:00, 5536.91it/s]
Shape of training images: (12764, 80, 160, 3)
Shape of label images: (12764, 224, 224, 1)



<Figure size 432x288 with 0 Axes>
1.3 Split the training, validation, and testing dataset


# split the training, validation, and test dataset
train_test_ratio = 0.8
train_val_ratio = 0.8
train_imgs, test_imgs, train_labels, test_labels = train_test_split(data, labels, train_size=train_test_ratio)
train_imgs, val_imgs, train_labels, val_labels = train_test_split(train_imgs, train_labels, train_size=train_val_ratio)
train_size = train_imgs.shape[0]
val_size = val_imgs.shape[0]
test_size = test_imgs.shape[0]
print("Training dataset size: ", train_size)
print("Validation dataset size: ", val_size)
print("Testing dataset size: ", test_size)

Training dataset size: 8168
Validation dataset size: 2043
Testing dataset size: 2553
1.4 Split the dataset into batch size


def sample_dataset_minibatch(X, y, batch_size):
split_size = len(X)

mask = np.random.choice(X.shape[0], batch_size)
X, y = X[mask], y[mask]
X = np.transpose(X,(0,3,1,2))
y = np.transpose(y,(0,3,1,2))

return X, y

2. Model Implementation

class UNet(nn.Module):
def contracting_block(self, in_channels, out_channels, kernel_size=3):
block = torch.nn.Sequential(
torch.nn.Conv2d(kernel_size=kernel_size, in_channels=in_channels, out_channels=out_channels, padding=(1,1)),
torch.nn.ReLU(),
torch.nn.BatchNorm2d(out_channels),
torch.nn.Conv2d(kernel_size=kernel_size, in_channels=out_channels, out_channels=out_channels,padding=(1,1)),
torch.nn.ReLU(),
torch.nn.BatchNorm2d(out_channels),
)
return block

def expansive_block(self, in_channels, mid_channel, out_channels, kernel_size=3):
block = torch.nn.Sequential(
torch.nn.Conv2d(kernel_size=kernel_size, in_channels=in_channels, out_channels=mid_channel, padding=(1,1)),
torch.nn.ReLU(),
torch.nn.BatchNorm2d(mid_channel),
torch.nn.Conv2d(kernel_size=kernel_size, in_channels=mid_channel, out_channels=mid_channel, padding=(1,1)),
torch.nn.ReLU(),
torch.nn.BatchNorm2d(mid_channel),
torch.nn.ConvTranspose2d(in_channels=mid_channel, out_channels=out_channels, kernel_size=3, stride=2, padding=1, output_padding=1)
)
return block

def final_block(self, in_channels, mid_channel, out_channels, kernel_size=3):
block = torch.nn.Sequential(
torch.nn.Conv2d(kernel_size=kernel_size, in_channels=in_channels, out_channels=mid_channel, padding=(1,1)),
torch.nn.ReLU(),
torch.nn.BatchNorm2d(mid_channel),
torch.nn.Conv2d(kernel_size=kernel_size, in_channels=mid_channel, out_channels=mid_channel, padding=(1,1)),
torch.nn.ReLU(),
torch.nn.BatchNorm2d(mid_channel),
torch.nn.Conv2d(kernel_size=kernel_size, in_channels=mid_channel, out_channels=out_channels, padding=1),
torch.nn.ReLU(),
torch.nn.BatchNorm2d(out_channels),
)
return block

def bottle(self):
block = torch.nn.Sequential(
torch.nn.Conv2d(kernel_size=3, in_channels=256, out_channels=512, padding=(1,1)),
torch.nn.ReLU(),
torch.nn.BatchNorm2d(512),
torch.nn.Conv2d(kernel_size=3, in_channels=512, out_channels=512, padding=(1,1)),
torch.nn.ReLU(),
torch.nn.BatchNorm2d(512),
torch.nn.ConvTranspose2d(in_channels=512, out_channels=256, kernel_size=3, stride=2, padding=1, output_padding=1)
)
return block

def init(self, in_channel, out_channel):
super(UNet, self).init()
#Encode
self.conv_encode1 = self.contracting_block(in_channels=in_channel, out_channels=64)
self.conv_maxpool1 = torch.nn.MaxPool2d(kernel_size=2)
self.conv_encode2 = self.contracting_block(64, 128)
self.conv_maxpool2 = torch.nn.MaxPool2d(kernel_size=2)
self.conv_encode3 = self.contracting_block(128, 256)
self.conv_maxpool3 = torch.nn.MaxPool2d(kernel_size=2)
# Bottleneck
self.bottleneck = self.bottle()
# Decode
self.conv_decode3 = self.expansive_block(512, 256, 128)
self.conv_decode2 = self.expansive_block(256, 128, 64)
self.final_layer = self.final_block(128, 64, out_channel)

def crop_and_concat(self, upsampled, bypass, crop=False):
if crop:
c = (bypass.size()[2] - upsampled.size()[2]) // 2
bypass = F.pad(bypass, (-c, -c, -c, -c))
return torch.cat((upsampled, bypass), 1)

def forward(self, x):
# Encode
# print("x size: ", x.size())
encode_block1 = self.conv_encode1(x)
# print("encode block 1 size: ", encode_block1.size())
encode_pool1 = self.conv_maxpool1(encode_block1)
# print("encode block 1 pool size: ", encode_pool1.size())

encode_block2 = self.conv_encode2(encode_pool1)
# print("encode block 2 size: ", encode_block2.size())

encode_pool2 = self.conv_maxpool2(encode_block2)
# print("encode block 2 pool size: ", encode_pool2.size())

encode_block3 = self.conv_encode3(encode_pool2)
encode_pool3 = self.conv_maxpool3(encode_block3)
# print("encode block 3 size: ", encode_block3.size())
# print("encode block 3 pool size: ", encode_pool3.size())


# Bottleneck
bottleneck1 = self.bottleneck(encode_pool3)
# print("bottleneck size: ", bottleneck1.size())
# Decode
decode_block3 = self.crop_and_concat(bottleneck1, encode_block3, crop=True)
# print("decode block 3 size: ", decode_block3.size())
cat_layer2 = self.conv_decode3(decode_block3)
# print("cat layer 2 size: ", cat_layer2.size())
decode_block2 = self.crop_and_concat(cat_layer2, encode_block2, crop=True)
# print("decode block 2 size: ", decode_block2.size())
cat_layer1 = self.conv_decode2(decode_block2)
# print("cat layer 1 size: ", cat_layer1.size())
decode_block1 = self.crop_and_concat(cat_layer1, encode_block1, crop=True)
# print("last layer before final layer: ", decode_block1.size())
final_layer = self.final_layer(decode_block1)
return final_layer


# Dice loss
class DiceLoss(torch.nn.Module):
def init(self):
super(diceLoss, self).init()
def forward(self,pred, target):
smooth = 1.
iflat = pred.contiguous().view(-1)
tflat = target.contiguous().view(-1)
intersection = (iflat * tflat).sum()
A_sum = torch.sum(iflat * iflat)
B_sum = torch.sum(tflat * tflat)
return 1 - ((2. * intersection + smooth) / (A_sum + B_sum + smooth) )


# Training Process
in_channel = 3
out_channel = 1

batch_size = 16 # batch size
batch_iterations = int(train_size / batch_size + 0.5)
num_epochs = 50 # number of training epochs
learning_rate = 5e-3
momentum = 0.99
lr_decay_step_size = 500
lr_decay = 0.995

print_every = 10 # determines window for printing average loss
save_every = 500 #determine window for saving state point of model

model_path = base_path + “/UNet_50epochs_16batch_5e-3lr_0.995lrd.pt”
training_path = base_path + “/training_process2.txt”
device = torch.device(“cuda” if torch.cuda.is_available() else “cpu”)

model = UNet(in_channel, out_channel)
if os.path.exists(model_path):
model.load_state_dict(torch.load(model_path))

# Move models to GPU if CUDA is available.
model.to(device)

# Define the loss function.
criterion = DiceLoss()

optimizer = torch.optim.SGD(model.parameters(), lr = learning_rate, momentum=0.99)
scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=lr_decay_step_size, gamma=lr_decay)

# training_file = open(training_path, ‘w’)

iterations_per_epoch = max(train_size // batch_size, 1)
num_iterations = num_epochs * iterations_per_epoch

for i in range(num_iterations):
X,y = sample_dataset_minibatch(train_imgs, train_labels, batch_size)

model.zero_grad()

X, y = torch.from_numpy(X).float().to(device), torch.from_numpy(y).long().to(device)

outputs = model(X)
# print("outputs size: ", outputs.size())
# print("y size: ", y.size())
# outputs = outputs.view(outputs.size(0), -1)
# y = y.view(y.size()[0], -1)

train_loss = criterion(outputs, y)
# print(train_loss)
# print(tag_score, post_type)
# print(“loss of {} type discourse: {}”.format(tag, loss))
train_loss.backward()

# Update the parameters in the optimizer.
optimizer.step()

if i % print_every == 0:
with torch.no_grad():
# print(“start testing…”)
X, y = sample_dataset_minibatch(val_imgs, val_labels, batch_size)
X, y = torch.from_numpy(X).float().to(device), torch.from_numpy(y).long().to(device)
# print("X size: ", X.size())

outputs = model(X)

validation_loss = criterion(outputs, y)

stats = ‘Iterations [%d/%d], Train Loss: %f, Validation Loss: %f’ <br/> % (i, num_iterations, train_loss, validation_loss)

print(stats)

if i % save_every == 0:
torch.save(model.state_dict(), model_path)
# Save the model
torch.save(model.state_dict(), model_path)

Iterations [0/25500], Train Loss: 1.001415, Validation Loss: 1.000758
Iterations [10/25500], Train Loss: 0.998528, Validation Loss: 0.997252
Iterations [20/25500], Train Loss: 0.992480, Validation Loss: 0.992196
Iterations [30/25500], Train Loss: 0.991629, Validation Loss: 0.990416
Iterations [40/25500], Train Loss: 0.990094, Validation Loss: 0.990619
Iterations [50/25500], Train Loss: 0.989266, Validation Loss: 0.989227
Iterations [60/25500], Train Loss: 0.988408, Validation Loss: 0.988803
Iterations [70/25500], Train Loss: 0.987195, Validation Loss: 0.988527
Iterations [80/25500], Train Loss: 0.987635, Validation Loss: 0.987067
Iterations [90/25500], Train Loss: 0.987023, Validation Loss: 0.986028
Iterations [100/25500], Train Loss: 0.986044, Validation Loss: 0.983820
Iterations [110/25500], Train Loss: 0.985096, Validation Loss: 0.984802
Iterations [120/25500], Train Loss: 0.982886, Validation Loss: 0.984128
Iterations [130/25500], Train Loss: 0.983011, Validation Loss: 0.982048
Iterations [140/25500], Train Loss: 0.982049, Validation Loss: 0.980642
Iterations [150/25500], Train Loss: 0.980495, Validation Loss: 0.980431
Iterations [160/25500], Train Loss: 0.978368, Validation Loss: 0.978440
Iterations [170/25500], Train Loss: 0.979501, Validation Loss: 0.978323
Iterations [180/25500], Train Loss: 0.976303, Validation Loss: 0.976652
Iterations [190/25500], Train Loss: 0.976831, Validation Loss: 0.977106
Iterations [200/25500], Train Loss: 0.974495, Validation Loss: 0.974790
Iterations [210/25500], Train Loss: 0.972828, Validation Loss: 0.971797
Iterations [220/25500], Train Loss: 0.972253, Validation Loss: 0.971838
Iterations [230/25500], Train Loss: 0.968789, Validation Loss: 0.968548
Iterations [240/25500], Train Loss: 0.967266, Validation Loss: 0.971119
Iterations [250/25500], Train Loss: 0.967201, Validation Loss: 0.969082
Iterations [260/25500], Train Loss: 0.965777, Validation Loss: 0.964332
Iterations [270/25500], Train Loss: 0.964722, Validation Loss: 0.962304
Iterations [280/25500], Train Loss: 0.964087, Validation Loss: 0.961414
Iterations [290/25500], Train Loss: 0.961182, Validation Loss: 0.957968
Iterations [300/25500], Train Loss: 0.958749, Validation Loss: 0.957719
Iterations [310/25500], Train Loss: 0.954490, Validation Loss: 0.957817
Iterations [320/25500], Train Loss: 0.955727, Validation Loss: 0.955683
Iterations [330/25500], Train Loss: 0.951738, Validation Loss: 0.953797
Iterations [340/25500], Train Loss: 0.949830, Validation Loss: 0.949916
Iterations [350/25500], Train Loss: 0.948715, Validation Loss: 0.948050
Iterations [360/25500], Train Loss: 0.946962, Validation Loss: 0.948251
Iterations [370/25500], Train Loss: 0.943539, Validation Loss: 0.944696
Iterations [380/25500], Train Loss: 0.941907, Validation Loss: 0.945795
Iterations [390/25500], Train Loss: 0.943258, Validation Loss: 0.942916
Iterations [400/25500], Train Loss: 0.938447, Validation Loss: 0.940663
Iterations [410/25500], Train Loss: 0.937721, Validation Loss: 0.938788
Iterations [420/25500], Train Loss: 0.935316, Validation Loss: 0.936105
Iterations [430/25500], Train Loss: 0.934164, Validation Loss: 0.935157
Iterations [440/25500], Train Loss: 0.933793, Validation Loss: 0.933126
Iterations [450/25500], Train Loss: 0.931071, Validation Loss: 0.930726
Iterations [460/25500], Train Loss: 0.931786, Validation Loss: 0.929147
Iterations [470/25500], Train Loss: 0.930707, Validation Loss: 0.934010
Iterations [480/25500], Train Loss: 0.923950, Validation Loss: 0.925525
Iterations [490/25500], Train Loss: 0.925226, Validation Loss: 0.925738
Iterations [500/25500], Train Loss: 0.922742, Validation Loss: 0.926352
Iterations [510/25500], Train Loss: 0.922719, Validation Loss: 0.924478
Iterations [520/25500], Train Loss: 0.920884, Validation Loss: 0.921621
Iterations [530/25500], Train Loss: 0.917721, Validation Loss: 0.915321
Iterations [540/25500], Train Loss: 0.916347, Validation Loss: 0.916372
Iterations [550/25500], Train Loss: 0.912455, Validation Loss: 0.917798
Iterations [560/25500], Train Loss: 0.910619, Validation Loss: 0.913194
Iterations [570/25500], Train Loss: 0.911515, Validation Loss: 0.909353
Iterations [580/25500], Train Loss: 0.908658, Validation Loss: 0.903517
Iterations [590/25500], Train Loss: 0.906376, Validation Loss: 0.908590
Iterations [600/25500], Train Loss: 0.905269, Validation Loss: 0.902613
Iterations [610/25500], Train Loss: 0.899009, Validation Loss: 0.903781
Iterations [620/25500], Train Loss: 0.900163, Validation Loss: 0.899559
Iterations [630/25500], Train Loss: 0.895104, Validation Loss: 0.896630
Iterations [640/25500], Train Loss: 0.898172, Validation Loss: 0.896622
Iterations [650/25500], Train Loss: 0.896541, Validation Loss: 0.890602
Iterations [660/25500], Train Loss: 0.893894, Validation Loss: 0.891580
Iterations [670/25500], Train Loss: 0.891320, Validation Loss: 0.889343
Iterations [680/25500], Train Loss: 0.885918, Validation Loss: 0.893421
Iterations [690/25500], Train Loss: 0.889601, Validation Loss: 0.886430
Iterations [700/25500], Train Loss: 0.883970, Validation Loss: 0.886535
Iterations [710/25500], Train Loss: 0.887586, Validation Loss: 0.883502
Iterations [720/25500], Train Loss: 0.886504, Validation Loss: 0.878003
Iterations [730/25500], Train Loss: 0.875698, Validation Loss: 0.877921
Iterations [740/25500], Train Loss: 0.876795, Validation Loss: 0.877746
Iterations [750/25500], Train Loss: 0.873124, Validation Loss: 0.872957
Iterations [760/25500], Train Loss: 0.871355, Validation Loss: 0.871775
Iterations [770/25500], Train Loss: 0.866813, Validation Loss: 0.876059
Iterations [780/25500], Train Loss: 0.866622, Validation Loss: 0.869551
Iterations [790/25500], Train Loss: 0.866419, Validation Loss: 0.867896
Iterations [800/25500], Train Loss: 0.864720, Validation Loss: 0.864070
Iterations [810/25500], Train Loss: 0.861053, Validation Loss: 0.863504
Iterations [820/25500], Train Loss: 0.863504, Validation Loss: 0.864640
Iterations [830/25500], Train Loss: 0.865903, Validation Loss: 0.861097
Iterations [840/25500], Train Loss: 0.854717, Validation Loss: 0.862301
Iterations [850/25500], Train Loss: 0.852942, Validation Loss: 0.849945
Iterations [860/25500], Train Loss: 0.860333, Validation Loss: 0.858950
Iterations [870/25500], Train Loss: 0.855623, Validation Loss: 0.852298
Iterations [880/25500], Train Loss: 0.853778, Validation Loss: 0.845443
Iterations [890/25500], Train Loss: 0.844888, Validation Loss: 0.848998
Iterations [900/25500], Train Loss: 0.847030, Validation Loss: 0.847346
Iterations [910/25500], Train Loss: 0.842563, Validation Loss: 0.847236
Iterations [920/25500], Train Loss: 0.838834, Validation Loss: 0.847730
Iterations [930/25500], Train Loss: 0.843941, Validation Loss: 0.844506
Iterations [940/25500], Train Loss: 0.840931, Validation Loss: 0.847986
Iterations [950/25500], Train Loss: 0.828829, Validation Loss: 0.844120
Iterations [960/25500], Train Loss: 0.836639, Validation Loss: 0.843000
Iterations [970/25500], Train Loss: 0.835505, Validation Loss: 0.831071
Iterations [980/25500], Train Loss: 0.829922, Validation Loss: 0.838577
Iterations [990/25500], Train Loss: 0.824656, Validation Loss: 0.827309
Iterations [1000/25500], Train Loss: 0.829029, Validation Loss: 0.830436
Iterations [1010/25500], Train Loss: 0.830369, Validation Loss: 0.823619
Iterations [1020/25500], Train Loss: 0.828949, Validation Loss: 0.821403
Iterations [1030/25500], Train Loss: 0.817921, Validation Loss: 0.834396
Iterations [1040/25500], Train Loss: 0.819673, Validation Loss: 0.825840
Iterations [1050/25500], Train Loss: 0.808802, Validation Loss: 0.821751
Iterations [1060/25500], Train Loss: 0.827879, Validation Loss: 0.810530
Iterations [1070/25500], Train Loss: 0.815915, Validation Loss: 0.808721
Iterations [1080/25500], Train Loss: 0.807783, Validation Loss: 0.812226
Iterations [1090/25500], Train Loss: 0.809703, Validation Loss: 0.812858
Iterations [1100/25500], Train Loss: 0.805951, Validation Loss: 0.814463
Iterations [1110/25500], Train Loss: 0.810647, Validation Loss: 0.808269
Iterations [1120/25500], Train Loss: 0.806271, Validation Loss: 0.803568
Iterations [1130/25500], Train Loss: 0.804559, Validation Loss: 0.815610
Iterations [1140/25500], Train Loss: 0.803571, Validation Loss: 0.804211
Iterations [1150/25500], Train Loss: 0.799263, Validation Loss: 0.804329
Iterations [1160/25500], Train Loss: 0.794025, Validation Loss: 0.799808
Iterations [1170/25500], Train Loss: 0.801612, Validation Loss: 0.792426
Iterations [1180/25500], Train Loss: 0.798970, Validation Loss: 0.803619
Iterations [1190/25500], Train Loss: 0.800130, Validation Loss: 0.794604
Iterations [1200/25500], Train Loss: 0.795070, Validation Loss: 0.795670
Iterations [1210/25500], Train Loss: 0.781316, Validation Loss: 0.795988
Iterations [1220/25500], Train Loss: 0.784442, Validation Loss: 0.777666
Iterations [1230/25500], Train Loss: 0.785256, Validation Loss: 0.780056
Iterations [1240/25500], Train Loss: 0.781623, Validation Loss: 0.782104
Iterations [1250/25500], Train Loss: 0.787525, Validation Loss: 0.783527
Iterations [1260/25500], Train Loss: 0.774987, Validation Loss: 0.779685
Iterations [1270/25500], Train Loss: 0.775719, Validation Loss: 0.774661
Iterations [1280/25500], Train Loss: 0.781894, Validation Loss: 0.774337
Iterations [1290/25500], Train Loss: 0.778969, Validation Loss: 0.778569
Iterations [1300/25500], Train Loss: 0.772830, Validation Loss: 0.771022
Iterations [1310/25500], Train Loss: 0.782832, Validation Loss: 0.777614
Iterations [1320/25500], Train Loss: 0.766670, Validation Loss: 0.767450
Iterations [1330/25500], Train Loss: 0.770751, Validation Loss: 0.762404
Iterations [1340/25500], Train Loss: 0.766738, Validation Loss: 0.766444
Iterations [1350/25500], Train Loss: 0.764677, Validation Loss: 0.768203
Iterations [1360/25500], Train Loss: 0.761602, Validation Loss: 0.755034
Iterations [1370/25500], Train Loss: 0.754794, Validation Loss: 0.765732
Iterations [1380/25500], Train Loss: 0.761141, Validation Loss: 0.757560
Iterations [1390/25500], Train Loss: 0.758188, Validation Loss: 0.760741
Iterations [1400/25500], Train Loss: 0.748338, Validation Loss: 0.764968
Iterations [1410/25500], Train Loss: 0.753124, Validation Loss: 0.753602
Iterations [1420/25500], Train Loss: 0.751593, Validation Loss: 0.746691
Iterations [1430/25500], Train Loss: 0.747962, Validation Loss: 0.747349
Iterations [1440/25500], Train Loss: 0.758303, Validation Loss: 0.741774
Iterations [1450/25500], Train Loss: 0.747653, Validation Loss: 0.745931
Iterations [1460/25500], Train Loss: 0.740469, Validation Loss: 0.752550
Iterations [1470/25500], Train Loss: 0.739582, Validation Loss: 0.732044
Iterations [1480/25500], Train Loss: 0.743059, Validation Loss: 0.735170
Iterations [1490/25500], Train Loss: 0.733223, Validation Loss: 0.735288
Iterations [1500/25500], Train Loss: 0.734280, Validation Loss: 0.747557
Iterations [1510/25500], Train Loss: 0.742416, Validation Loss: 0.750559
Iterations [1520/25500], Train Loss: 0.741535, Validation Loss: 0.745072
Iterations [1530/25500], Train Loss: 0.728175, Validation Loss: 0.731711
Iterations [1540/25500], Train Loss: 0.723454, Validation Loss: 0.734543
Iterations [1550/25500], Train Loss: 0.725519, Validation Loss: 0.726647
Iterations [1560/25500], Train Loss: 0.724139, Validation Loss: 0.726478
Iterations [1570/25500], Train Loss: 0.721897, Validation Loss: 0.726023
Iterations [1580/25500], Train Loss: 0.726611, Validation Loss: 0.723157
Iterations [1590/25500], Train Loss: 0.725736, Validation Loss: 0.710659
Iterations [1600/25500], Train Loss: 0.706979, Validation Loss: 0.719642
Iterations [1610/25500], Train Loss: 0.722824, Validation Loss: 0.713137
Iterations [1620/25500], Train Loss: 0.718515, Validation Loss: 0.728375
Iterations [1630/25500], Train Loss: 0.709873, Validation Loss: 0.729986
Iterations [1640/25500], Train Loss: 0.717700, Validation Loss: 0.708194
Iterations [1650/25500], Train Loss: 0.709671, Validation Loss: 0.710792
Iterations [1660/25500], Train Loss: 0.707857, Validation Loss: 0.716745
Iterations [1670/25500], Train Loss: 0.700411, Validation Loss: 0.712865
Iterations [1680/25500], Train Loss: 0.702469, Validation Loss: 0.723395
Iterations [1690/25500], Train Loss: 0.707584, Validation Loss: 0.708002
Iterations [1700/25500], Train Loss: 0.707867, Validation Loss: 0.707385
Iterations [1710/25500], Train Loss: 0.703254, Validation Loss: 0.703195
Iterations [1720/25500], Train Loss: 0.709821, Validation Loss: 0.697046
Iterations [1730/25500], Train Loss: 0.706737, Validation Loss: 0.701247
Iterations [1740/25500], Train Loss: 0.701005, Validation Loss: 0.698912
Iterations [1750/25500], Train Loss: 0.694665, Validation Loss: 0.702650
Iterations [1760/25500], Train Loss: 0.694060, Validation Loss: 0.695857
Iterations [1770/25500], Train Loss: 0.705571, Validation Loss: 0.698809
Iterations [1780/25500], Train Loss: 0.694456, Validation Loss: 0.691155
Iterations [1790/25500], Train Loss: 0.693445, Validation Loss: 0.691954
Iterations [1800/25500], Train Loss: 0.682827, Validation Loss: 0.677526
Iterations [1810/25500], Train Loss: 0.693459, Validation Loss: 0.685225
Iterations [1820/25500], Train Loss: 0.689997, Validation Loss: 0.694963
Iterations [1830/25500], Train Loss: 0.675161, Validation Loss: 0.685287
Iterations [1840/25500], Train Loss: 0.691809, Validation Loss: 0.669864
Iterations [1850/25500], Train Loss: 0.671216, Validation Loss: 0.677505
Iterations [1860/25500], Train Loss: 0.679584, Validation Loss: 0.677384
Iterations [1870/25500], Train Loss: 0.698926, Validation Loss: 0.678232
Iterations [1880/25500], Train Loss: 0.678921, Validation Loss: 0.661749
Iterations [1890/25500], Train Loss: 0.681780, Validation Loss: 0.668892
Iterations [1900/25500], Train Loss: 0.683663, Validation Loss: 0.678931
Iterations [1910/25500], Train Loss: 0.672444, Validation Loss: 0.682069
Iterations [1920/25500], Train Loss: 0.671555, Validation Loss: 0.662019
Iterations [1930/25500], Train Loss: 0.659893, Validation Loss: 0.661445
Iterations [1940/25500], Train Loss: 0.663132, Validation Loss: 0.655953
Iterations [1950/25500], Train Loss: 0.676570, Validation Loss: 0.663770
Iterations [1960/25500], Train Loss: 0.664683, Validation Loss: 0.667253
Iterations [1970/25500], Train Loss: 0.647923, Validation Loss: 0.665669
Iterations [1980/25500], Train Loss: 0.649580, Validation Loss: 0.665453
Iterations [1990/25500], Train Loss: 0.656636, Validation Loss: 0.656759
Iterations [2000/25500], Train Loss: 0.666297, Validation Loss: 0.653703
Iterations [2010/25500], Train Loss: 0.658082, Validation Loss: 0.661743
Iterations [2020/25500], Train Loss: 0.664705, Validation Loss: 0.655932
Iterations [2030/25500], Train Loss: 0.653648, Validation Loss: 0.644349
Iterations [2040/25500], Train Loss: 0.643286, Validation Loss: 0.669065
Iterations [2050/25500], Train Loss: 0.657286, Validation Loss: 0.655151
Iterations [2060/25500], Train Loss: 0.653816, Validation Loss: 0.654569
Iterations [2070/25500], Train Loss: 0.665213, Validation Loss: 0.645352
Iterations [2080/25500], Train Loss: 0.640545, Validation Loss: 0.647439
Iterations [2090/25500], Train Loss: 0.657004, Validation Loss: 0.648717
Iterations [2100/25500], Train Loss: 0.636458, Validation Loss: 0.638987
Iterations [2110/25500], Train Loss: 0.635379, Validation Loss: 0.645398
Iterations [2120/25500], Train Loss: 0.627017, Validation Loss: 0.626380
Iterations [2130/25500], Train Loss: 0.628531, Validation Loss: 0.618575
Iterations [2140/25500], Train Loss: 0.629639, Validation Loss: 0.626774
Iterations [2150/25500], Train Loss: 0.615023, Validation Loss: 0.633419
Iterations [2160/25500], Train Loss: 0.630769, Validation Loss: 0.637238
Iterations [2170/25500], Train Loss: 0.616327, Validation Loss: 0.643751
Iterations [2180/25500], Train Loss: 0.630932, Validation Loss: 0.630576
Iterations [2190/25500], Train Loss: 0.616400, Validation Loss: 0.614964
Iterations [2200/25500], Train Loss: 0.625338, Validation Loss: 0.622839
Iterations [2210/25500], Train Loss: 0.615525, Validation Loss: 0.624764
Iterations [2220/25500], Train Loss: 0.617821, Validation Loss: 0.610042
Iterations [2230/25500], Train Loss: 0.643119, Validation Loss: 0.622385
Iterations [2240/25500], Train Loss: 0.590132, Validation Loss: 0.619214
Iterations [2250/25500], Train Loss: 0.602187, Validation Loss: 0.626755
Iterations [2260/25500], Train Loss: 0.613033, Validation Loss: 0.603031
Iterations [2270/25500], Train Loss: 0.608040, Validation Loss: 0.598993
Iterations [2280/25500], Train Loss: 0.621132, Validation Loss: 0.609972
Iterations [2290/25500], Train Loss: 0.611319, Validation Loss: 0.617923
Iterations [2300/25500], Train Loss: 0.612709, Validation Loss: 0.618762
Iterations [2310/25500], Train Loss: 0.601929, Validation Loss: 0.602740
Iterations [2320/25500], Train Loss: 0.596439, Validation Loss: 0.614874
Iterations [2330/25500], Train Loss: 0.608829, Validation Loss: 0.597295
Iterations [2340/25500], Train Loss: 0.606857, Validation Loss: 0.608794
Iterations [2350/25500], Train Loss: 0.610599, Validation Loss: 0.598216
Iterations [2360/25500], Train Loss: 0.603956, Validation Loss: 0.603002
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3. Model Evaluation
3.1 Test dataset evaluation - run model on test dataset, see the loss, and visualize


in_channel = 3
out_channel = 1

batch_size = 8 # batch size
iterations = int(test_size / batch_size + 0.5)

model_path = base_path + “/UNet_50epochs_16batch_5e-3lr_0.995lrd.pt”

device = torch.device(“cuda” if torch.cuda.is_available() else “cpu”)

model = UNet(in_channel, out_channel)
if os.path.exists(model_path):
model.load_state_dict(torch.load(model_path))

# Move models to GPU if CUDA is available.
model.to(device)

# Define the loss function.
criterion = DiceLoss()

X, y = sample_dataset_minibatch(test_imgs, test_labels, batch_size)
tensor_X, tensor_y = torch.from_numpy(X).float().to(device), torch.from_numpy(y).long().to(device)
# print("X size: “, X.size())

outputs = model(tensor_X)

loss = criterion(outputs, tensor_y)
print(“Loss: “, loss.item())
print()
print(”%15s, %10s, %15s”%(“Input Images”, “Labels”, “Model Outputs”))
# visualization
ori_imgs = np.transpose(X, (0,2,3,1))
ori_labels = np.squeeze(np.transpose(y, (0,2,3,1)))
model_outputs = np.squeeze(np.transpose(outputs.cpu().detach().numpy(), (0,2,3,1)))
for i in range(batch_size):
plt.subplot(1,3,1)
plt.imshow(ori_imgs[i])

plt.subplot(1,3,2)
plt.imshow(ori_labels[i])

plt.subplot(1,3,3)
plt.imshow(model_outputs[i])

plt.figure()

plt.show()


Loss: 0.0058106184005737305

Input Images, Labels, Model Outputs








<Figure size 432x288 with 0 Axes>
3.2 Visualize the training loss and validation loss of training process


# Plot the training processes
import matplotlib.pyplot as plt

def plot(x, y, x_label, y_label, title):
# plot x and y
plt.plot(x, y)
plt.xlabel(x_label)
plt.ylabel(y_label)
plt.title(title)
plt.show()

iters = []
train_losses = []
val_losses = []
with open(base_path + ‘training_outputs.txt’, ‘r’) as f:
for line in f.readlines():
words = line.split()
iters.append(int(words[1].split(‘/’)[0][1:]))
train_losses.append(float(words[4][:-1]))
val_losses.append(float(words[-1]))

plot(iters, train_losses, “Iterations”, “Train Loss”, “Training Loss”)
plot(iters, val_losses, “Iterations”, “Validation Loss”, “Validation Loss”)



4. Testing using real-life videos
4.1 Load video and visualize as plot


import pylab
import imageio

video1 = base_path + “Real_Life_Video_1.mp4”

video = imageio.get_reader(video1, ‘ffmpeg’)
total_frames = video.get_length()
frame_shape = video.get_data(0).shape
fps = video.get_meta_data()[‘fps’]

print(“There are total {} frames in the video”.format(total_frames))
print(“Every frame’s size is {}”.format(frame_shape))
print(“FPS of the video is {}”.format(fps))
print()
# visualize frames in the video
num_frame = 10
image = video.get_data(num_frame)
fig = pylab.figure()
fig.suptitle(‘image #{}’.format(num_frame), fontsize=20)
pylab.imshow(image)
pylab.show()

There are total 1107 frames in the video
Every frame’s size is (1080, 1920, 3)
FPS of the video is 29.83


4.2 Resize every frames of video to model input size


new_size = 224
data = np.stack([np.asarray(Image.fromarray(current_data).resize((new_size, new_size), Image.NEAREST))
for current_data in tqdm(video)])


plt.imshow(data[10])

plt.show()

100%|██████████| 1107/1107 [00:11<00:00, 99.21it/s]

4.3 Feed every frame to the model


in_channel = 3
out_channel = 1

batch_size = 8
iterations = int(total_frames / batch_size + 0.5)

model_path = base_path + “/UNet_50epochs_16batch_5e-3lr_0.995lrd.pt”

device = torch.device(“cuda” if torch.cuda.is_available() else “cpu”)

model = UNet(in_channel, out_channel)
if os.path.exists(model_path):
model.load_state_dict(torch.load(model_path))

# Move models to GPU if CUDA is available.
model.to(device)

output_file = imageio.get_writer(base_path + “Real_Life_Video_1_output.mp4”, fps=fps)

for i in range(iterations):
labelled_imgs = []
# Feed the images to the model and generate lanes
if i == iterations - 1:
X = data[ibatch_size:]
else:
X = data[ibatch_size:(i+1)*batch_size]
X = np.transpose(X,(0,3,1,2))
tensor_X = torch.from_numpy(X).float().to(device)

outputs = model(tensor_X)

ori_imgs = np.transpose(X, (0,2,3,1))
model_outputs = np.squeeze(np.transpose(outputs.cpu().detach().numpy(), (0,2,3,1)))
# visualization
for i in range(batch_size):
img = ori_imgs[i]
output = model_outputs[i]

plt.subplot(1,4,1)
plt.imshow(img)

plt.subplot(1,4,2)
plt.imshow(output)

plt.subplot(1,4,3)
plt.imshow(img)
plt.imshow(output, alpha=0.3)

# Process the images to denoise
output[:100,…] = 0
kernel = kernel = np.ones((10,10), np.uint8)
erode = cv2.erode(output, kernel, iterations=1)
dilate_erode = cv2.dilate(erode, kernel, iterations=1)

plt.subplot(1,4,4)
plt.imshow(img)
plt.imshow(dilate_erode, alpha=0.3)

# Generate fake R & B color dimensions, stack with G
lanes = np.expand_dims(dilate_erode, axis=2).astype(np.uint8)
blanks = np.zeros_like(lanes).astype(np.uint8)
lane_drawn = np.dstack((blanks, lanes, blanks))
# combine original images and output from the model
labelled_img = cv2.addWeighted(img, 1, lane_drawn, 1, 1)

labelled_imgs.append(labelled_img)

plt.figure()
plt.show()

# # Resize to video size
# print(frame_shape[:2])
# lane_drawn_frames = np.stack([np.asarray(Image.fromarray(current_data).resize(frame_shape[:2], Image.NEAREST))
# for current_data in tqdm(labelled_imgs)])

# write to the video
for frame in labelled_imgs:
output_file.append_data(frame)

output_file.close()”

i want like this
Got it. You want to build a similar pipeline using TensorFlow and U-Net for lane detection, working with KITTI dataset. Let’s create an organized version of your script with setup, data loading, model definition, training, and evaluation steps. The approach will be structured like the PyTorch example you provided but using TensorFlow.

Here’s the plan:
1. Setup and Preparatory Work - Import necessary libraries and mount Google Drive if needed.
2. Data Loading - Load the KITTI dataset and perform resizing.
3. Model Definition - Define the U-Net model using TensorFlow.
4. Training - Train the model.
5. Evaluation - Evaluate the trained model on test data.
6. Real-life Video Testing - Process a real-life video for lane detection, similar to the PyTorch example.

### Setup and Preparatory Work

# Install necessary libraries (if not already)
!pip install opencv-python-headless
!pip install tensorflow
!pip install kaggle

import os
import numpy as np
import pickle
import matplotlib.pyplot as plt
import cv2
from tqdm import tqdm
from sklearn.model_selection import train_test_split

import tensorflow as tf
from tensorflow.keras.layers import Input, Conv2D, MaxPooling2D, UpSampling2D, concatenate, BatchNormalization, Add
from tensorflow.keras.models import Model
from google.colab import files, drive

# Connect to Google Drive
drive.mount(‘/content/drive’)

base_path = ‘/content/drive/My Drive/ML_project/’

# Set up Kaggle API credentials
os.environ['KAG
you got inturrupted