"""
aerial-segmentation
Proof of concept showing effectiveness of a fine tuned instance segmentation model for detecting trees.
"""
import os
import gradio as gr

import cv2
os.system("pip install 'git+https://github.com/facebookresearch/detectron2.git'")
from transformers import DetrFeatureExtractor, DetrForSegmentation
from PIL import Image
import gradio as gr
import numpy as np
import torch
import torchvision
import detectron2
import json

# import some common detectron2 utilities
import itertools
import seaborn as sns
from detectron2 import model_zoo
from detectron2.engine import DefaultPredictor
from detectron2.config import get_cfg
from detectron2.utils.visualizer import Visualizer
from detectron2.utils.visualizer import ColorMode
from detectron2.data import MetadataCatalog, DatasetCatalog
from detectron2.checkpoint import DetectionCheckpointer
from detectron2.utils.visualizer import ColorMode
from detectron2.structures import Instances


def list_pth_files_in_directory(directory, version="v1"):
    files = os.listdir(directory)
    version = version.split("v")[1]
    # return files that contains substring version and end with .pth
    pth_files = [f for f in files if version in f and f.endswith(".pth")]
    return pth_files

def get_version_cfg_yml(path):
    directory = path.split("/")[0]
    version = path.split("/")[1]
    files = os.listdir(directory)
    cfg_file = [f for f in files if (f.endswith(".yml") or f.endswith(".yaml")) and version in f]
    return directory + "/" + cfg_file[0]

def update_row_visibility(mode):
    visibility = {
        "tree": mode in ["Trees", "Both"],
        "building": mode in ["Buildings", "Both"]
    }
    tree_row, building_row = gr.Row(visible=visibility["tree"]), gr.Row(visible=visibility["building"])
    
    return tree_row, building_row

def update_path_options(version):
    if "tree" in version:
        directory = "tree_model_weights"
    else:
        directory = "building_model_weight"
    return gr.Dropdown(choices=list_pth_files_in_directory(directory, version), label=f"Select a {version.split('v')[0]} model file", visible=True, interactive=True)

# Model for trees
def tree_model(tree_version_dropdown, tree_pth_dropdown, tree_threshold, device="cpu"):
    tree_cfg = get_cfg()
    tree_cfg.merge_from_file(get_version_cfg_yml(f"tree_model_weights/{tree_version_dropdown}"))
    tree_cfg.MODEL.DEVICE=device
    tree_cfg.MODEL.WEIGHTS = f"tree_model_weights/{tree_pth_dropdown}"
    tree_cfg.MODEL.ROI_HEADS.NUM_CLASSES = 2  # TODO change this
    tree_cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST = tree_threshold
    tree_predictor = DefaultPredictor(tree_cfg)
    return tree_predictor

# Model for buildings
def building_model(building_version_dropdown, building_pth_dropdown, building_threshold, device="cpu"):
    building_cfg = get_cfg()
    building_cfg.merge_from_file(get_version_cfg_yml(f"building_model_weight/{building_version_dropdown}"))
    building_cfg.MODEL.DEVICE=device
    building_cfg.MODEL.WEIGHTS = f"building_model_weight/{building_pth_dropdown}"
    building_cfg.MODEL.ROI_HEADS.NUM_CLASSES = 8  # TODO change this
    building_cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST = building_threshold
    building_predictor = DefaultPredictor(building_cfg)
    return building_predictor

# A function that runs the buildings model on an given image and confidence threshold
def segment_building(im, building_predictor):
    outputs = building_predictor(im)
    building_instances = outputs["instances"].to("cpu")

    return building_instances

# A function that runs the trees model on an given image and confidence threshold
def segment_tree(im, tree_predictor):
    outputs = tree_predictor(im)
    tree_instances = outputs["instances"].to("cpu")

    return tree_instances

# Function to map strings to color mode
def map_color_mode(color_mode):
    if color_mode == "Black/white":
        return ColorMode.IMAGE_BW
    elif color_mode == "Random":
        return ColorMode.IMAGE
    elif color_mode == "Segmentation" or color_mode == None:
        return ColorMode.SEGMENTATION

def load_predictor(model, version, pth, threshold):
    return model(version, pth, threshold)

def load_instances(image, predictor, segment_function):
    return segment_function(image, predictor)

def combine_instances(tree_instances, building_instances):
    return Instances.cat([tree_instances, building_instances])

def get_metadata(dataset_name, coco_file):
    metadata = MetadataCatalog.get(dataset_name)
    with open(coco_file, "r") as f:
        coco = json.load(f)
    categories = coco["categories"]
    metadata.thing_classes = [c["name"] for c in categories]
    return metadata

def visualize_image(im, mode, tree_threshold, building_threshold, color_mode, tree_version, tree_pth, building_version, building_pth):
    im = np.array(im)
    color_mode = map_color_mode(color_mode)
    
    instances = None

    if mode in {"Trees", "Both"}:
        tree_predictor = load_predictor(tree_model, tree_version, tree_pth, tree_threshold)
        tree_instances = load_instances(im, tree_predictor, segment_tree)
        instances = tree_instances

    if mode in {"Buildings", "Both"}:
        building_predictor = load_predictor(building_model, building_version, building_pth, building_threshold)
        building_instances = load_instances(im, building_predictor, segment_building)
        instances = building_instances if mode == "Buildings" else combine_instances(instances, building_instances)

    # Assuming 'urban-small_train' is intended for both Trees and Buildings
    metadata = get_metadata("urban-small_train", "building_model_weight/_annotations.coco.json")
    visualizer = Visualizer(im[:, :, ::-1], metadata=metadata, scale=0.5, instance_mode=color_mode)

    output_image = visualizer.draw_instance_predictions(instances)

    return Image.fromarray(output_image.get_image()[:, :, ::-1])