def show_gtbs(base_dir,
test_files,
global_gtbs,
num_classes,
cols=1,
figsize=(12, 8),
font_size=12):
orig_images = []
orig_gtbs = []
for file in test_files:
img = load_img(base_dir + file)
orig_images.append(img)
gtbs = global_gtbs[file].copy()
# get rid of is_difficult col (the last one)
gtbs = gtbs[:, :-1]
orig_gtbs.append(gtbs)
show_predictions(orig_images,
orig_gtbs,
num_classes,
cols=cols,
conf_threshold=None,
figsize=figsize,
font_size=font_size)
def get(self, img_name):
for img_dir in self.img_dirs:
img_full_path = os.path.join(img_dir, img_name)
if os.path.exists(img_full_path):
img = imaging.load_img(img_full_path).astype(np.float32)
return img
raise ValueError("No {} exists in dirs {}".format(
img_name, self.img_dirs))
def make_prediction(model, target_img_size, base_dir, test_files):
orig_images = []
preprocessed_images = []
for file in test_files:
img = load_img(base_dir + file)
orig_images.append(img)
preprocessed_images.append(preprocess_img(img, target_img_size))
preprocessed_images = np.array(preprocessed_images)
y_pred = model.predict(preprocessed_images)
return orig_images, preprocessed_images, y_pred
def images_to_hdf5(img_dir, hdf5_path, img_files=None, target_img_size=None):
# datasets/voc/VOCtrainval_06-Nov-2007/JPEGImages
if not img_files:
img_files = os.listdir(img_dir)
print("Found '{}' files in dir '{}'".format(len(img_files), img_dir))
with h5py.File(hdf5_path, mode='w') as hdf5_file:
for img_file in img_files:
img_full_path = os.path.join(img_dir, img_file)
img = imaging.load_img(img_full_path)
if target_img_size:
img = imaging.resize_img(img, target_img_size)
hdf5_file[img_file] = img.astype(np.uint8)
def images_to_pickle(img_dir,
pickle_path,
img_files=None,
target_img_size=None):
# datasets/voc/VOCtrainval_06-Nov-2007/JPEGImages
if not img_files:
img_files = os.listdir(img_dir)
print("Found '{}' files in dir '{}'".format(len(img_files), img_dir))
with open(pickle_path, 'wb') as f:
data = {}
for img_file in img_files:
img_full_path = os.path.join(img_dir, img_file)
img = imaging.load_img(img_full_path)
if target_img_size:
img = imaging.resize_img(img, target_img_size)
data[img_file] = img.astype(np.uint8)
pickle.dump(data, f)