def __getitem__(self, idx):
assert self.train == True, 'getitem fuction only for training.'
image = self.images[idx]
# debug
debug = 0
if debug:
print(image.dtype)
print(np.array(image, dtype=np.float32).dtype)
print(image.shape)
tools.show_image(
np.array(image, dtype=np.int
)) # interfaces is not supported for multi-processing
#exit(0)
score = np.array((float(self.scores[idx])),
dtype=np.float32).reshape([1]) #IMPORTANT
sample = {'image': image, 'score': score}
if self.transform:
sample = self.transform(sample)
return sample
def show_result(processed_img: robot_finder.ProcessedImg):
show_image(
img=draw_points(
processed_img.img,
processed_img.blue_pts + processed_img.red_pts
),
win_name=PROC_WIN,
)
def post_screenshot(argv, response):
result = {}
target = argv[1] if len(argv) > 1 else None
fileName = save_fileInfo(response, target)
if not fileName:
saveFile = target if target else os.path.basename(source)
result['stderr'] = 'save %s error' % (saveFile)
else:
tools.show_image(fileName)
return result
def __init__(self, train_dir, test_path, batch_size=128, resize=None):
self.test_images, self.test_labels = Reader.analysis_singlefile(
test_path)
self.train_images, self.train_labels = Reader.analysis_dir(train_dir)
self.train_images, self.train_labels = shuffle_arrs(
self.train_images, self.train_labels)
self.test_images, self.test_labels = shuffle_arrs(
self.test_images, self.test_labels)
print('loading training image shape is ', np.shape(self.train_images),
' training label shape is ', np.shape(self.train_labels))
print('loading testing image shape is ', np.shape(self.test_images),
' testing label shape is ', np.shape(self.test_labels))
show_image(self.test_images[0], [100, 100])
self.train_generator = Generator(self.train_images,
self.train_labels,
batch_size,
epoch_num=-1,
resize=resize).next_batch()
self.test_generator = Generator(self.test_images,
self.test_labels,
batch_size,
epoch_num=1,
resize=resize).next_batch()
def display():
processed_imgs.wait_next()
show_result(processed_imgs.last)
show_image(selfies.last, SNAP_WIN)