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| # -*- coding: utf-8 -*- | |
| import json | |
| import os | |
| from pathlib import Path | |
| import gradio as gr | |
| import numpy as np | |
| from doc_ufcn import models | |
| from doc_ufcn.main import DocUFCN | |
| from PIL import Image, ImageDraw | |
| 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 and dict, an image with the predictions and a | |
| dictionary mapping an object idx (starting from 1) to a dictionary describing the detected object: | |
| - `polygon` key : list, the coordinates of the points of the polygon, | |
| - `confidence` key : float, confidence of the model, | |
| - `channel` key : str, the name of the predicted class. | |
| """ | |
| # 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() | |
| # Initialize the dictionary which will display the json on the application | |
| predict = [] | |
| # 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 i, polygon in enumerate(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] | |
| ) | |
| # Build the dictionary | |
| # Add an index to dictionary keys to differentiate predictions of the same class | |
| predict.append( | |
| { | |
| # The list of coordinates of the points of the polygon. | |
| # Use np.asarray to change list to ndarray to turn int64 to int32 with np.ndarray.tolist | |
| "polygon": np.asarray(polygon["polygon"]).tolist(), | |
| # Confidence that the model predicts the polygon in the right place | |
| "confidence": polygon["confidence"], | |
| # The channel on which the polygon is predicted | |
| "channel": classes[channel], | |
| } | |
| ) | |
| # Return the blend of the images and the dictionary formatted in json | |
| return Image.blend(image, img2, 0.5), json.dumps(predict, indent=20) | |
| with gr.Blocks() as process_image: | |
| # Create app title | |
| gr.Markdown(f"# {config['title']}") | |
| # Create app description | |
| gr.Markdown(config["description"]) | |
| # Create a first row of blocks | |
| with gr.Row(): | |
| # Create a column on the left | |
| with gr.Column(): | |
| # Generates an image that can be uploaded by a user | |
| image = gr.Image() | |
| # Create a row under the image | |
| with gr.Row(): | |
| # Generate a button to clear the inputs and outputs | |
| clear_button = gr.Button("Clear", variant="secondary") | |
| # Generates a button to submit the prediction | |
| submit_button = gr.Button("Submit", variant="primary") | |
| # Create a row under the buttons | |
| with gr.Row(): | |
| # Generate example images that can be used as input image | |
| examples = gr.Examples(inputs=image, examples=config["examples"]) | |
| # Create a column on the right | |
| with gr.Column(): | |
| # Generates an output image that does not support upload | |
| image_output = gr.Image(interactive=False) | |
| # Create a row under the predicted image | |
| with gr.Row(): | |
| # Create a column so that the JSON output doesn't take the full size of the page | |
| with gr.Column(): | |
| # Create a collapsible region | |
| with gr.Accordion("JSON"): | |
| # Generates a json with the model predictions | |
| json_output = gr.JSON() | |
| # Create the button to clear the inputs and outputs | |
| clear_button.click( | |
| lambda x, y, z: (None, None, None), | |
| inputs=[image, image_output, json_output], | |
| outputs=[image, image_output, json_output], | |
| ) | |
| # Create the button to submit the prediction | |
| submit_button.click(query_image, inputs=image, outputs=[image_output, json_output]) | |
| # Launch the application | |
| process_image.launch() | |