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app_helpers.py
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import numpy as np
import os
import pandas as pd
import tensorflow as tf
from functools import partial
from models import UnetResidual
from PIL import Image
from preprocessing import CrfLabelRefiner
from pyproj import CRS, Geod
from shapely.geometry import Polygon
from shapely.ops import transform
def calculate_water(predicted_mask):
white = len(predicted_mask[predicted_mask >= 0.5])
black = len(predicted_mask[predicted_mask < 0.5])
water_percentage = white / (white+black)
return round(water_percentage, 5)
def ensemble_predict(models, raw_image):
model1, model2 = models
image = np.expand_dims(raw_image, axis=0)
y_pred_1 = model1.predict(image)
y_pred_2 = model2.predict(image)
combined_mask = _get_ensemble_mask(image, y_pred_1, y_pred_2)
return combined_mask
def get_bounding_box(df):
longitude = [
df["min_longitude"],
df["min_longitude"],
df["max_longitude"],
df["max_longitude"]
]
latitude = [
df["min_latitude"],
df["max_latitude"],
df["max_latitude"],
df["min_latitude"]
]
coordinates = [[lat, long] for lat, long in zip (latitude, longitude)]
polygon = Polygon(coordinates)
return polygon
def get_bounding_box_area(bounding_box):
crs_4326 = CRS('epsg:4326')
geod_wgs84 = crs_4326.get_geod()
polygon_area_m2, _ = geod_wgs84.geometry_area_perimeter(bounding_box)
polygon_area_km2 = polygon_area_m2 / 1000000.0
return polygon_area_km2
def get_water_land_per_year(fraction, area):
water_sqkm = area * fraction
land_sqkm = area - water_sqkm
return (water_sqkm, land_sqkm)
def load_image(image_path):
raw_image = Image.open(image_path)
raw_image = np.array(raw_image)
if raw_image.ndim == 2:
raw_image = np.stack((raw_image,) * 3, axis=-1)
else:
raw_image = raw_image[:, :, :3]
raw_image = raw_image / 255.0
return raw_image
def load_models():
model_name = 'unet-residual-large-dice'
model_file_name = 'unet-residual-large-dice.h5'
unet_residual = _load_model(model_name, model_file_name, version=2)
model_name = 'unet-residual-large-crf-dice'
model_file_name = 'unet-residual-large-crf-dice.h5'
unet_residual_crf = _load_model(model_name, model_file_name, version=2)
return (unet_residual, unet_residual_crf)
def get_image_path(df, lake, year):
country = df.loc[lake, "country"]
name = df.loc[lake, "name"].replace(" ", "_").lower()
folder = "assets/lakes"
image_path = f"{folder}/{country}_{name}_s2cloudless_{year}.jpg"
return image_path
def load_dataset(file_path):
df = pd.read_json(file_path).T
df["lat"] = (df['min_latitude'] + df['max_latitude']) / 2.0
df["lon"] = (df['min_longitude'] + df['max_longitude']) / 2.0
return df
def _get_ensemble_mask(raw_image, y_pred_1, y_pred_2):
crf_model = CrfLabelRefiner()
image = np.squeeze(raw_image, axis=0)
pred_1 = np.squeeze(y_pred_1, axis=0)
pred_2 = np.squeeze(y_pred_2, axis=0)
mask = np.maximum(pred_1, pred_2)
mask[mask >= 0.5] = 1
mask[mask < 0.5] = 0
image = image.copy(order='C')
mask = mask.copy(order='C')
mask = crf_model.refine(image, mask)
return mask
def _load_model(model_name, model_file_name, image_size=(256, 256), version=1):
model_file_path = f'saved_models/{model_file_name}'
unet_residual = UnetResidual(model_name, image_size, version=version)
unet_residual.restore(model_file_path)
return unet_residual