from keras.models import Sequential
from keras.layers import Conv2D, MaxPooling2D, Flatten, Dense
import numpy as np
from keras.utils import Sequence
from keras.preprocessing.image import load_img, img_to_array
class DataGenerator(Sequence):
def __init__(self, image_filenames, labels, batch_size, img_size):
self.image_filenames = image_filenames
self.labels = labels
self.batch_size = batch_size
self.img_size = img_size
def __len__(self):
return int(np.ceil(len(self.image_filenames) / self.batch_size))
def __getitem__(self, idx):
batch_x = self.image_filenames[idx * self.batch_size:(idx + 1) * self.batch_size]
batch_y = self.labels[idx * self.batch_size:(idx + 1) * self.batch_size]
images = [img_to_array(load_img(file_name, target_size=self.img_size)) for file_name in batch_x]
return np.array(images), np.array(batch_y)
# Load dataset
import pandas as pd
def read_training_data(path_to_labels_csv):
global model
data = pd.read_csv(path_to_labels_csv)
image_filenames = data['filename'].values
labels = data['count'].values
# Create data generator batch size < number of pics
train_gen = DataGenerator(image_filenames, labels, batch_size=32, img_size=(128, 128))
# Train the model
model.fit(train_gen, epochs=10, validation_data=validation_gen)
return f"Finished Training""
# Function to preprocess and predict the count of people in a new image
def predict_people_count(model, image_path):
# Load and preprocess the image
image = load_img(image_path, target_size=(128, 128))
image_array = img_to_array(image)
image_array = np.expand_dims(image_array, axis=0) # Add batch dimension
image_array = preprocess_input(image_array)
# Make prediction
prediction = model.predict(image_array)
count = int(np.round(prediction[0][0])) # Round and convert to integer
return count
# Example usage
def count_people(image_path):
people_count = predict_people_count(model, image_path)
return f'I see {people_count} people in the image.'