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# -*- coding: utf-8 -*-
import os
from pathlib import Path
import gradio as gr
from PIL import Image, ImageDraw
from doc_ufcn import models
from doc_ufcn.main import DocUFCN
from config import parse_configurations
# Load the config
config = parse_configurations(Path("config.json"))
# Download the model
model_path, parameters = models.download_model(name=config["model_name"])
# Store classes_colors list
classes_colors = config["classes_colors"]
# Store classes
classes = parameters["classes"]
# Check that the number of colors is equal to the number of classes -1
assert len(classes) - 1 == len(
classes_colors
), f"The parameter classes_colors was filled with the wrong number of colors. {len(classes)-1} colors are expected instead of {len(classes_colors)}."
# Check that the paths of the examples are valid
for example in config["examples"]:
assert os.path.exists(example), f"The path of the image '{example}' does not exist."
# Load the model
model = DocUFCN(
no_of_classes=len(classes),
model_input_size=parameters["input_size"],
device="cpu",
)
model.load(model_path=model_path, mean=parameters["mean"], std=parameters["std"])
def query_image(image):
"""
Draws the predicted polygons with the color provided by the model on an image
:param image: An image to predict
:return: Image, an image with the predictions
"""
# Make a prediction with the model
detected_polygons, probabilities, mask, overlap = model.predict(
input_image=image, raw_output=True, mask_output=True, overlap_output=True
)
# Load image
image = Image.fromarray(image)
# Make a copy of the image to keep the source and also to be able to use Pillow's blend method
img2 = image.copy()
# Create the polygons on the copy of the image for each class with the corresponding color
# We do not draw polygons of the background channel (channel 0)
for channel in range(1, len(classes)):
for polygon in detected_polygons[channel]:
# Draw the polygons on the image copy.
# Loop through the class_colors list (channel 1 has color 0)
ImageDraw.Draw(img2).polygon(
polygon["polygon"], fill=classes_colors[channel - 1]
)
# Return the blend of the images
return Image.blend(image, img2, 0.5)
# Create an interface with the config
process_image = gr.Interface(
fn=query_image,
inputs=[gr.Image()],
outputs=[gr.Image()],
title=config["title"],
description=config["description"],
examples=config["examples"],
)
# Launch the application with the public mode (True or False)
process_image.launch()
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