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import numpy as np
import gradio as gr
from PIL import Image
import onnxruntime as ort
def resize_and_crop(image, image_size):
# Resize the image such that the shortest side is image_size
original_size = image.size
ratio = float(image_size) / min(original_size)
new_size = tuple([int(x * ratio) for x in original_size])
resized_image = image.resize(new_size, Image.LANCZOS)
# Calculate coordinates for center cropping
left = (resized_image.width - image_size) / 2
top = (resized_image.height - image_size) / 2
right = (resized_image.width + image_size) / 2
bottom = (resized_image.height + image_size) / 2
# Crop the image to image_size x image_size
cropped_image = resized_image.crop((left, top, right, bottom))
array = np.array(cropped_image)
array = np.transpose(array, (2, 0, 1))
array = np.expand_dims(array, axis=0)
array = (array/255).astype(np.float32)
return array
# Read class labels from a text file
def read_labels(file_path):
with open(file_path, 'r') as f:
labels = [line.strip() for line in f.readlines()]
return labels
# Load the class labels
class_labels = read_labels('vocab_formatted.txt')
taxon_included = read_labels('taxon_included.txt')
string_taxon_included = ', '.join(sorted(taxon_included))
taxon_not_included = read_labels('taxon_not_included.txt')
string_taxon_not_included = ', '.join(sorted(taxon_not_included))
# Load the ONNX model
onnx_model = ort.InferenceSession('convnext_tiny.onnx')
input_name = onnx_model.get_inputs()[0].name
output_name = onnx_model.get_outputs()[0].name
# Define the inference function
def classify_image(image):
input_array = resize_and_crop(image, 320)
outputs = onnx_model.run([output_name], {input_name: input_array})[0]
result = {taxon: prob for taxon, prob in zip(class_labels, outputs[0])}
return result
# Create the Gradio interface
iface = gr.Interface(
fn=classify_image,
inputs=gr.Image(type="pil"),
outputs=gr.Label(num_top_classes=5),
title="Billedgenkendelse af Danske Ferskvandsfisk",
description=f"**Upload et billede af en fisk for at identificere arten**.\n\nArter inkluderet (dansk navn):\n*{string_taxon_included}*\n\nArter ikke inkluderet (endnu!):\n*{string_taxon_not_included}*\n\nLavet af: Kenneth Thorø Martinsen ([email protected])",
)
# Launch the app
if __name__ == "__main__":
iface.launch() |