biomap/helper.py

import torch.multiprocessing
import torchvision.transforms as T
import numpy as np
from utils import transform_to_pil, compute_biodiv_score, plot_imgs_labels, plot_image
from utils_gee import get_image
from dateutil.relativedelta import relativedelta

from model import LitUnsupervisedSegmenter
import datetime
import matplotlib as mpl
from joblib import Parallel, cpu_count, delayed
import logging
from inference import inference
import streamlit as st
import cv2

@st.cache_data(hash_funcs={LitUnsupervisedSegmenter: lambda dt: dt.name})
def inference_on_location(model, longitude=2.98, latitude=48.81, start_date=2020, end_date=2022, how="year"):
    """Performe an inference on the latitude and longitude between the start date and the end date

    Args:
        latitude (float): the latitude of the landscape
        longitude (float): the longitude of the landscape
        start_date (str): the start date for our inference
        end_date (str): the end date for our inference
        model (_type_, optional): _description_. Defaults to model.

    Returns:            
        img, labeled_img,biodiv_score: the original landscape, the labeled landscape and the biodiversity score and the landscape
    """
    logging.info("Running Inference on location")
    logging.info(f"latitude : {latitude} & longitude : {longitude}")
    logging.info(f"start date : {start_date} & end_date : {end_date}")
    logging.info(f"Prediction on intervale : {how}")
    if how == "month":
        delta_month = 1
    elif how == "2months":
        delta_month = 2
    elif how == "year":
        delta_month = 11
    else:
        raise ValueError("Wrong interval")

    assert int(end_date) > int(start_date), "end date must be stricly higher than start date"
    location = [float(latitude), float(longitude)]
    
    # Extract img numpy from earth engine and transform it to PIL img
    dates = [datetime.datetime(start_date, 1, 1, 0, 0, 0)]
    while dates[-1] < datetime.datetime(int(end_date), 1, 1, 0, 0, 0):
        dates.append(dates[-1] + relativedelta(months=delta_month))
    
    dates = [d.strftime("%Y-%m-%d") for d in dates]

    all_image = Parallel(n_jobs=cpu_count(), prefer="threads")(delayed(get_image)(location, d1,d2) for d1, d2 in zip(dates[:-1],dates[1:]))
    # all_image = [cv2.imread("output/img.png") for i in range(len(dates))]
    outputs = inference(np.array(all_image), model)

    logging.info("Calculating Biodiversity Scores...")
    scores, scores_details = map(list, zip(*[compute_biodiv_score(output["linear_preds"].detach().numpy()) for output in outputs]))
    logging.info(f"Calculated Biodiversity Score : {scores}")

    imgs, labels, labeled_imgs = map(list, zip(*[transform_to_pil(output) for output in outputs]))
    
    images = [np.asarray(img) for img in imgs]
    labeled_imgs = [np.asarray(img) for img in labeled_imgs]
    title=f"TimeLapse at location {tuple(location)} between {start_date} and {end_date}"
    fig = plot_imgs_labels(dates, images, labeled_imgs, scores_details, scores, title=title)
    # fig.save("test.png")
    return fig

@st.cache_data(hash_funcs={LitUnsupervisedSegmenter: lambda dt: dt.name})
def inference_on_location_and_month(model, longitude = 2.98, latitude = 48.81, start_date = '2020-03-20'):
    """Performe an inference on the latitude and longitude between the start date and the end date

    Args:
        latitude (float): the latitude of the landscape
        longitude (float): the longitude of the landscape
        start_date (str): the start date for our inference
        end_date (str): the end date for our inference
        model (_type_, optional): _description_. Defaults to model.

    Returns:
        img, labeled_img,biodiv_score: the original landscape, the labeled landscape and the biodiversity score and the landscape
    """
    logging.info("Running Inference on location and month")
    logging.info(f"latitude : {latitude} & longitude : {longitude}")
    location = [float(latitude), float(longitude)]
    
    # Extract img numpy from earth engine and transform it to PIL img
    end_date = datetime.datetime.strptime(start_date, "%Y-%m-%d") + relativedelta(months=1)
    end_date = datetime.datetime.strftime(end_date, "%Y-%m-%d")

    img_test = get_image(location, start_date, end_date)
    outputs = inference(np.array([img_test]), model)

    logging.info("Calculating Biodiversity Score...")
    score, score_details = compute_biodiv_score(outputs[0]["linear_preds"].detach().numpy())
    logging.info(f"Calculated Biodiversity Score : {score}")
    img, label, labeled_img = transform_to_pil(outputs[0])
    
    title=f"Prediction at location {tuple(location)} at {start_date}"
    fig = plot_image([start_date], [np.asarray(img)], [np.asarray(labeled_img)], [score_details], [score],title=title)
    return fig


if __name__ == "__main__":
    import logging
    import hydra
    import sys
    from model import LitUnsupervisedSegmenter
    file_handler = logging.FileHandler(filename='biomap.log')
    stdout_handler = logging.StreamHandler(stream=sys.stdout)
    handlers = [file_handler, stdout_handler]

    logging.basicConfig(handlers=handlers, encoding='utf-8', level=logging.INFO, format="%(asctime)s - %(name)s - %(levelname)s - %(message)s")
    # Initialize hydra with configs
    hydra.initialize(config_path="configs", job_name="corine")
    cfg = hydra.compose(config_name="my_train_config.yml")
    logging.info(f"config : {cfg}")
    # Load the model

    nbclasses = cfg.dir_dataset_n_classes
    model = LitUnsupervisedSegmenter(nbclasses, cfg)
    logging.info(f"Model Initialiazed")
    
    model_path = "biomap/checkpoint/model/model.pt"
    saved_state_dict = torch.load(model_path, map_location=torch.device("cpu"))
    logging.info(f"Model weights Loaded")
    model.load_state_dict(saved_state_dict)

    logging.info(f"Model Loaded")
    # inference_on_location_and_month(model)
    inference_on_location(model)