jupyter training

transform_data.py

@@ -1,63 +0,0 @@
-import os
-import numpy as np
-import matplotlib.pyplot as plt
-
-import cv2 as cv
-import rasterio as rio
-
-RASTERS_DIR = './data/data/train_features'
-IMAGES_DIR = './images/'
-FC_DIR = "fc"
-
-
-def load_img(path, expand_dim=False):
-    img = cv.imread(path)
-    img = img / 255.0
-    if expand_dim:
-        img = np.expand_dims(img, axis=0)
-
-    return img
-
-
-def create_folder(name, path):
-    n = os.path.join(path, name)
-    if not os.path.exists(n):
-        os.makedirs(n)
-    return n
-
-
-def scale(band):
-    return band/np.max(band)
-
-
-def convert_raster_to_image(rasters_dir,
-                            rgb_path=None,
-                            false_color_path=None):
-
-    b2 = scale(rio.open(rasters_dir+'/B02.tif').read().reshape(512, 512, 1))
-    b3 = scale(rio.open(rasters_dir+'/B03.tif').read().reshape(512, 512, 1))
-    b4 = scale(rio.open(rasters_dir+'/B04.tif').read().reshape(512, 512, 1))
-    b8 = scale(rio.open(rasters_dir+'/B08.tif').read().reshape(512, 512, 1))
-
-    file_name = rasters_dir.split(os.sep)[-1]
-
-    rgb = np.dstack([b4, b3, b2])
-
-    plt.imsave(fname=rgb_path + f'/{file_name}.jpeg',
-               arr=rgb)
-
-    fc = np.dstack([b8, b3, b2])
-
-    plt.imsave(fname=false_color_path + f'/{file_name}.jpeg',
-               arr=fc)
-
-
-def transform_photo(raster_dir):
-    dp = create_folder(raster_dir, IMAGES_DIR)
-    fc = create_folder(FC_DIR, dp)
-    convert_raster_to_image(os.path.join(RASTERS_DIR, raster_dir), dp, fc)
-
-
-if __name__ == "__main__":
-    for d in os.listdir(RASTERS_DIR):
-        transform_photo(d)