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CutMix_Data_Augmentation_for_Image_Classification

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CutMix_Data_Augmentation_for_Image_Classification / app.py

harshaUwm163

changed the text on the keras example link

22115ef almost 2 years ago

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	import numpy as np
	import tensorflow as tf
	import gradio as gr
	from huggingface_hub import from_pretrained_keras
	import cv2
	# import matplotlib.pyplot as plt


	model = from_pretrained_keras("keras-io/CutMix_data_augmentation_for_image_classification")

	# functions for inference
	IMG_SIZE = 32

	class_names = [
	"Airplane",
	"Automobile",
	"Bird",
	"Cat",
	"Deer",
	"Dog",
	"Frog",
	"Horse",
	"Ship",
	"Truck",
	]

	# resize the image and it to a float between 0,1
	def preprocess_image(image, label):
	image = tf.image.resize(image, (IMG_SIZE, IMG_SIZE))
	image = tf.image.convert_image_dtype(image, tf.float32) / 255.0
	return image, label


	def read_image(image):
	image = tf.convert_to_tensor(image)
	image.set_shape([None, None, 3])
	print('$$$$$$$$$$$$$$$$$$$$$ in read image $$$$$$$$$$$$$$$$$$$$$$')
	print(image.shape)
	# plt.imshow(image)
	# plt.show()
	# image = tf.image.resize(images=image, size=[IMG_SIZE, IMG_SIZE])
	# image = image / 127.5 - 1
	image, _ = preprocess_image(image, 1) # 1 here is a temporary label
	return image

	def infer(input_image):
	print('#$$$$$$$$$$$$$$$$$$$$$$$$$ IN INFER $$$$$$$$$$$$$$$$$$$$$$$')
	image_tensor = read_image(input_image)
	print(image_tensor.shape)
	predictions = model.predict(np.expand_dims((image_tensor), axis=0))
	predictions = np.squeeze(predictions)
	predictions = np.argmax(predictions) # , axis=2
	predicted_label = class_names[predictions.item()]
	return str(predicted_label)


	# get the inputs
	input = gr.inputs.Image(shape=(IMG_SIZE, IMG_SIZE))
	# the app outputs two segmented images
	output = [gr.outputs.Label()]
	# it's good practice to pass examples, description and a title to guide users
	examples = [["./content/examples/Frog.jpg"], ["./content/examples/Truck.jpg"]]
	title = "Image classification"
	description = "Upload an image or select from examples to classify it. The allowed classes are - Airplane, Automobile, Bird, Cat, Deer, Dog, Frog, Horse, Ship, Truck <p><b>Space author: Harshavardhan</b> <br><b> Keras example author: <a href=\"https://twitter.com/sayannath2350\"> Sayan Nath </a> </b> <br> <a href=\"https://keras.io/examples/vision/cutmix/\">link to the original Keras example</a> </p>"

	gr_interface = gr.Interface(infer, input, output, examples=examples, allow_flagging=False, analytics_enabled=False, title=title, description=description).launch(enable_queue=True, debug=False)
	gr_interface.launch()