inference_tab/inference_setup.py

import gradio as gr
import cv2
import numpy as np
from PIL import Image
import os

TILE_SIZE = 1024  
TILE_FOLDER = "tiles"
os.makedirs(TILE_FOLDER, exist_ok=True)
tiles_cache = {"tiles": [], "selected_tile": None}


def make_tiles(image, tile_size=TILE_SIZE):
    h, w, _ = image.shape
    annotated = image.copy()
    tiles = []
    tile_id = 0

    for y in range(0, h, tile_size):
        for x in range(0, w, tile_size):
            tile = image[y:y+tile_size, x:x+tile_size]
            tiles.append(((x, y, x+tile_size, y+tile_size), tile))
            cv2.rectangle(annotated, (x, y), (x+tile_size, y+tile_size), (255,0,0), 2)
            cv2.putText(annotated, str(tile_id), (x+50, y+50),
                        cv2.FONT_HERSHEY_SIMPLEX, 2, (0,0,0), 5)
            tile_id += 1
    return annotated, tiles

def create_tiles(image_file):
    img = Image.open(image_file.name).convert("RGB")
    img = np.array(img)

    annotated, tiles = make_tiles(img, TILE_SIZE)
    tiles_cache["tiles"] = []

    for idx, (coords, tile) in enumerate(tiles):
        tile_path = os.path.join(TILE_FOLDER, f"tile_{idx}.png")
        Image.fromarray(tile).save(tile_path)
        tiles_cache["tiles"].append((coords, tile_path))  # store path instead of array

    tiles_cache["selected_tile"] = None
    return annotated, gr.update(interactive=False)

def select_tile(evt: gr.SelectData,state):
    # compute tile index
    if not tiles_cache["tiles"]:
        return None, gr.update(interactive=False), state

    num_tiles_x = (tiles_cache["tiles"][-1][0][2]) // TILE_SIZE
    tile_id = (evt.index[1] // TILE_SIZE) * num_tiles_x + (evt.index[0] // TILE_SIZE)

    if 0 <= tile_id < len(tiles_cache["tiles"]):
        coords, tile_path = tiles_cache["tiles"][tile_id]

        # store the path, not the array
        tiles_cache["selected_tile"] = {
            "tile_path": tile_path,
            "coords": coords
        }

        updated_state = {
            "tile_path": tile_path,
            "coords": coords
        }

        # load tile only for display
        tile_array = np.array(Image.open(tile_path))
        cv2.putText(tile_array, str(tile_id), (100, 100),
                    cv2.FONT_HERSHEY_SIMPLEX, 2, (0,0,0), 4, cv2.LINE_AA)

        return tile_array, gr.update(interactive=True),updated_state

    return None, gr.update(interactive=False), state


def enable_textbox(file):
    return gr.update(interactive=bool(file))


def get_inference_widgets(run_inference,georefImg,selected_tile_state):
    with gr.Row():
        # Left column
        with gr.Column(scale=1,min_width=500):
            annotated_out = gr.Image(
                type="numpy", label="City Map",
                height=500, width=500
            )
            
            image_input = gr.File(label="Select Image File")
            gcp_input = gr.File(label="Select GCP Points File", file_types=[".points"])

            city_name = gr.Textbox(label="Enter city name")
            user_crs = gr.Textbox(label="Enter CRS for the GCP",value="3395")

            create_btn = gr.Button("Create Tiles")
            georef_btn = gr.Button("Georeference Full Map")
           

        # Right column
        with gr.Column(scale=1):
            selected_tile = gr.Image(
                type="numpy", label="Selected Tile",
                height=500, width=500
            )
            score_th = gr.Textbox(label="Score threshold below which to annotate manually (OSM)",
                                  info="Computes fuzzy match of the detected street names with OSM street names within 100m buffer")
            
            # Historic dictionary of street names and matching score threshold
            hist_dic = gr.File(label="Upload csv with historic street names",file_types=[".csv"])
            hist_th = gr.Textbox(label="Score threshold below which to annotate manually (Directory)",
                                  info="Computes fuzzy match of the detected street names with the historic street names",
                                  interactive=False)
            hist_dic.change(enable_textbox, inputs=hist_dic, outputs=hist_th)



            run_button = gr.Button("Run Inference", interactive=False)
            output = gr.Textbox(label="Progress", lines=5, interactive=False)
            download_file = gr.File(label="Download CSV",
                                    file_types=[".csv"],
                                    type="filepath")

    # pass globally instead
    #selected_tile_state = gr.State()


    # Wire events
    create_btn.click(
        fn=create_tiles, inputs=image_input,
        outputs=[annotated_out, run_button]
    )
    annotated_out.select(
        fn=select_tile, inputs=[selected_tile_state],
        outputs=[selected_tile, run_button, selected_tile_state]
    )
    run_button.click(
        fn=run_inference,
        inputs=[selected_tile_state, gcp_input,user_crs, city_name, score_th, hist_th,hist_dic],
        outputs=[output, download_file]
    )

    georef_btn.click(
        fn=georefImg,
        inputs=[image_input, gcp_input,user_crs],
        outputs=[output]
    )


    return image_input, gcp_input, city_name, user_crs, score_th, hist_th,hist_dic, run_button, output, download_file