def get_predictions():
batch_size = 100
n = 0
results = {"prediction": [], "error": []}
num_images = len(images_paths)
while True:
if (n * batch_size) >= num_images:
break
next_batch_list = []
error_indices = []
for index_in_batch, i in enumerate(range(n*batch_size, min((n + 1)*batch_size, num_images))):
img_path = images_paths[i]
try:
preprocssed_img = utils.preprocess_img(os.path.join(image_folder_path, img_path), model_input_shape, preprocessing)
next_batch_list.append(preprocssed_img)
except IOError as e:
print("Cannot read the image '{}', skipping it. Error: {}".format(img_path, e))
error_indices.append(index_in_batch)
next_batch = np.array(next_batch_list)
prediction_batch = model.predict(next_batch).tolist()
error_batch = [0] * len(prediction_batch)
for err_index in error_indices:
prediction_batch.insert(err_index, None)
error_batch.insert(err_index, 1)
results["prediction"].extend(prediction_batch)
results["error"].extend(error_batch)
n+=1
print("{} images treated, out of {}".format(min(n * batch_size, num_images), num_images))
return results
def augmentation_generator(df_imgs, image_folder_path, batch_size,
n_augmentation, input_shape, labels, preprocessing,
TrainImageGen):
nb_imgs = df_imgs.shape[0]
batch_size_adapted = int(batch_size / n_augmentation)
nb_batch = int(math.ceil(nb_imgs * 1.0 / batch_size_adapted))
while True:
for num_batch in range(nb_batch):
df_imgs_batch = df_imgs.iloc[num_batch *
batch_size_adapted:(num_batch + 1) *
batch_size_adapted, :]
nb_imgs_batch = df_imgs_batch.shape[0]
X_batch_list = []
y_batch_list = []
for num_img in range(nb_imgs_batch):
row = df_imgs_batch.iloc[num_img, :]
img_filename = row[constants.FILENAME]
img_path = utils.get_file_path(image_folder_path, img_filename)
label = row[constants.LABEL]
label_index = labels.index(label)
try:
x = utils.preprocess_img(img_path, input_shape,
preprocessing)
x = np.tile(x, (n_augmentation, 1, 1, 1))
# TrainImageGen returns infinite loop, each of which yields batch data
for batch in TrainImageGen.flow(x,
batch_size=n_augmentation):
X_batch_list.append(batch)
y_batch_list.extend([label_index] * n_augmentation)
break
except IOError as e:
print("Cannot read the image '{}', skipping it. Error: {}".
format(img_filename, e))
X_batch = np.concatenate(X_batch_list)
actual_batch_size = X_batch.shape[0]
y_batch = np.zeros((actual_batch_size, n_classes))
y_batch[range(actual_batch_size), y_batch_list] = 1
yield (X_batch, y_batch)
def no_augmentation_generator(df_imgs, image_folder_path, batch_size,
input_shape, labels, preprocessing):
nb_imgs = df_imgs.shape[0]
nb_batch = int(math.ceil(nb_imgs * 1.0 / batch_size))
while True:
for num_batch in range(nb_batch):
df_imgs_batch = df_imgs.iloc[num_batch *
batch_size:(num_batch + 1) *
batch_size, :]
nb_imgs_batch = df_imgs_batch.shape[0]
X_batch_list = []
y_batch_list = []
for num_img in range(nb_imgs_batch):
row = df_imgs_batch.iloc[num_img, :]
img_filename = row[constants.FILENAME]
img_path = utils.get_file_path(image_folder_path, img_filename)
label = row[constants.LABEL]
label_index = labels.index(label)
try:
x = utils.preprocess_img(img_path, input_shape,
preprocessing)
X_batch_list.append(x)
y_batch_list.append(label_index)
except IOError as e:
print("Cannot read the image '{}', skipping it. Error: {}".
format(img_filename, e))
X_batch = np.array(X_batch_list)
actual_batch_size = X_batch.shape[0]
y_batch = np.zeros((actual_batch_size, n_classes))
y_batch[range(actual_batch_size), y_batch_list] = 1
yield (X_batch, y_batch)