data_hackathon.py

import sys
import os
import cv2
import glob
import numpy as np
import subprocess
from tensorflow import keras
from tensorflow.keras import layers
from tensorflow.keras.applications import InceptionV3
from tensorflow.keras.utils import to_categorical

def install(package):
    subprocess.check_call([sys.executable, "-m", "pip", "install", package])

try:
    from sklearn.preprocessing import LabelEncoder
except ImportError:
    install('scikit-learn')
    from sklearn.preprocessing import LabelEncoder

def load_images_and_labels(train_dir, lidar_dir, label_conversion_func):
    train_images = []
    train_depthmaps = []
    train_labels = []

    for label_dir in os.listdir(train_dir):
        for filename in os.listdir(os.path.join(train_dir, label_dir)):
            file_root, _ = os.path.splitext(filename)

            # Load and process the image
            img = cv2.imread(os.path.join(train_dir, label_dir, filename))
            img = cv2.resize(img, (150, 150))  
            img = img / 255.0  # Normalization

            train_images.append(img)
            train_labels.append(label_conversion_func(label_dir))

            # Load and process a corresponding depth map from LIDAR data
            try:
                depthmap = np.load(os.path.join(lidar_dir, file_root + ".npy"))
                depthmap = cv2.resize(depthmap, (150, 150))  
                depthmap = depthmap / np.max(depthmap)  # normalize to 0-1
                train_depthmaps.append(depthmap[..., np.newaxis])  
            except:
                train_depthmaps.append(np.zeros((150, 150, 1)))

    train_images = np.array(train_images)
    train_depthmaps = np.array(train_depthmaps)
    train_labels = np.array(train_labels)

    train_data = np.concatenate([train_images, train_depthmaps], axis=-1)  
    return train_data, train_labels 

train_dir = 'path_to_your_training_data_directory'
lidar_dir = 'path_to_your_LIDAR_data_directory'

train_images, train_labels = load_images_and_labels(train_dir, lidar_dir, your_label_conversion_func)

le = LabelEncoder()
train_labels = le.fit_transform(train_labels)
train_labels = to_categorical(train_labels)

base_model = InceptionV3(input_shape = (150, 150, 4), include_top = False, weights = None) 
for layer in base_model.layers:
    layer.trainable = False

x = layers.Flatten()(base_model.output)
x = layers.Dense(1024, activation='relu')(x)
x = layers.Dropout(0.2)(x) 
x = layers.Dense(train_labels.shape[1], activation='softmax')(x)

model = keras.models.Model(base_model.input, x)

model.compile(optimizer = keras.optimizers.RMSprop(lr=0.0001), 
              loss = 'categorical_crossentropy', 
              metrics = ['acc'])

history = model.fit(train_images,
                    train_labels, 
                    epochs=20)

model.save('food_model.h5')