read picture files

iss/tools/tools.py

@@ -2,6 +2,9 @@
 
 import PIL
 import os
+import numpy as np
+from keras_preprocessing.image.utils import load_img
+
 
 class Tools:
 
@@ -29,4 +32,48 @@ class Tools:
 			os.makedirs(path)
 		return path
 
+	@staticmethod
+	def encoded_pictures_from_generator(generator, model):
+
+		predictions_tuple = tuple([model.get_encoded_prediction(imgs) for imgs in generator])
+		predictions = np.concatenate(predictions_tuple, axis = 0)
+
+		return predictions
+
+	@staticmethod
+	def read_np_picture(path, target_size = None, scale = 1):
+		# img = PIL.Image.open(filename)
+		img = load_img(path, target_size = target_size)
+		img_np = np.asarray(img, dtype = 'uint8')
+		img_np = img_np * scale
+		return img_np
+
+	@staticmethod
+	def list_directory_filenames(path):
+		filenames = os.listdir(path)
+		filenames = [path + f for f in filenames]
+
+		return filenames
+
+	@staticmethod
+	def generator_np_picture_from_filenames(filenames, target_size = None, scale = 1, batch = 124, nb_batch = None):
+
+		max_n = len(filenames)
+		div = np.divmod(max_n, batch)
+
+		if nb_batch is None:
+			nb_batch = div[0] + 1 * (div[1] != 0)
+
+		for i in range(nb_batch):
+		# for i in [75, 76]:
+			i_debut = i*batch
+			i_fin = min(i_debut + batch, max_n)
+			print("i_debut:" + str(i_debut))
+			print("i_fin:" + str(i_fin))
+			yield np.array([Tools.read_np_picture(f, target_size, scale) for f in filenames[i_debut:i_fin]])
+
+
+
+
+