logo-detection: prototype for JSON input, preprocess with Keras, and return JSON output
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	kevinbazira
	Mar 26 2024, 10:13 AM

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logo-detection: prototype for JSON input, preprocess with Keras, and return JSON output
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	# !pip install keras==3.0.4
	# !pip install keras-cv==0.8.1

	import os
	import requests
	import json
	import tempfile
	import shutil
	os.environ["KERAS_BACKEND"] = "tensorflow" # Set the Keras backend environment variable to "tensorflow"
	import keras
	import keras_cv
	import numpy as np

	BATCH_SIZE = 64
	IMAGE_SIZE = (224, 224)
	target = "logo" # Predicted target class: "album", "book", "logo", "screenshot"
	label_mode = "binary" # Model type: "binary" for binary classification, "categorical" for multiclass
	model_path = "/content/logo_detection/model/logo_max_all.keras" # Path to a trained Keras model with ".keras" extension

	# Load the model
	model = keras.models.load_model(model_path)

	# Input data in JSON format, each containing filename, URL, and target class
	input_data = [
	{
	"filename": "Cambia_logo.png",
	"url": "http://www.kevinbazira.com/images/kevin-bazira-playing-basketball.jpg",
	"target": "logo"
	},
	{
	"filename": "Blooming_bush_(14248894271).jpg",
	"url": "http://www.kevinbazira.com/images/kevin-bazira-playing-basketball.jpg",
	"target": "logo"
	}
	]

	# Create a temporary directory to store images
	temp_dir = tempfile.mkdtemp()
	# Create subdirectories for "logo" and "out_of_domain"
	logo_dir = os.path.join(temp_dir, "logo")
	out_of_domain_dir = os.path.join(temp_dir, "out_of_domain")
	os.makedirs(logo_dir)
	os.makedirs(out_of_domain_dir)

	# Download images from URLs and save them to appropriate directories
	for idx, data in enumerate(input_data):
	image_url = data["url"]
	image_filename = os.path.join(temp_dir, data["target"], data["filename"])
	with open(image_filename, "wb") as f:
	response = requests.get(image_url)
	f.write(response.content)

	# Use keras.utils.image_dataset_from_directory to create test_set
	test_set = keras.utils.image_dataset_from_directory(
	temp_dir,
	labels="inferred",
	label_mode=label_mode,
	class_names=["out_of_domain", "logo"],
	batch_size=BATCH_SIZE,
	image_size=IMAGE_SIZE,
	shuffle=False,
	)

	predictions_response = []
	# Iterate through the test set and make predictions
	for images, labels in test_set:
	predictions = model(images) # Pass images directly to the model
	for i in range(len(predictions)):
	file_path = test_set.file_paths[i]
	file_name = os.path.basename(file_path)
	rounded_predictions = np.around(predictions[i].numpy(), decimals=2).astype(float)
	prediction = {
	"filename": file_name,
	"target": target,
	"prediction": {
	"logo": rounded_predictions[1],
	"out_of_domain": rounded_predictions[0]
	}
	}
	predictions_response.append(prediction)

	# Output response in JSON format
	print(json.dumps(predictions_response, indent=4))

	# Delete the temporary directory after use
	shutil.rmtree(temp_dir)

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logo-detection: prototype for JSON input, preprocess with Keras, and return JSON output
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logo-detection: prototype for JSON input, preprocess with Keras, and return JSON output
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