def load_image(image_path, model=None):
"""Loads and rectifies an image from file.
Args:
image_path (str): path to an image from the dataset.
model (camera_model.CameraModel): if supplied, model will be used to undistort image.
Returns:
numpy.ndarray: demosaiced and optionally undistorted image
"""
if model:
camera = model.camera
else:
camera = re.search('(stereo|mono_(left|right|rear))',
image_path).group(0)
if camera == 'stereo':
pattern = BAYER_STEREO
else:
pattern = BAYER_MONO
img = Image.open(image_path)
img = demosaic(img, pattern)
if model:
img = model.undistort(img)
return img
def load_image(image_path, model=None):
"""Loads and rectifies an image from file.
Args:
image_path (str): path to an image from the dataset.
model (camera_model.CameraModel): if supplied, model will be used to undistort image.
Returns:
numpy.ndarray: demosaiced and optionally undistorted image
"""
if model:
camera = model.camera
else:
camera = re.search('(stereo|mono_(left|right|rear))', image_path).group(0)
if camera == 'stereo':
pattern = BAYER_STEREO
else:
pattern = BAYER_MONO
img = Image.open(image_path)
img = demosaic(img, pattern)
if model:
img = model.undistort(img)
return img
def load_image(image_path, model=None):
"""Loads and rectifies an image from file.
Args:
image_path (str): path to an image from the dataset.
model (camera_model.CameraModel): if supplied, model will be used to undistort image.
Returns:
numpy.ndarray: demosaiced and optionally undistorted image
"""
if model:
camera = model.camera
else:
camera = re.search('(stereo|mono_(left|right|rear))',
image_path).group(0)
if camera == 'stereo':
pattern = BAYER_STEREO
else:
pattern = BAYER_MONO
if model:
img = demosaic(Image.open(image_path), pattern)
img = model.undistort(img)
img = rgb_2_grey(img)
else:
img = non_demosaic_load(image_path)
assert isinstance(
img, np.ndarray) and img.dtype == np.uint8 and img.flags.contiguous
return img
def load_image(image_path):
img = Image.open(image_path)
img = demosaic(img, 'gbrg')
cvimage = np.array(img, dtype=np.uint8)
cvimage = cv2.cvtColor(cvimage, cv2.COLOR_RGB2BGR)
cvgray = cv2.cvtColor(cvimage, cv2.COLOR_BGR2GRAY)
lbp = feature.local_binary_pattern(cvgray, 8, 1, method='default')
im = Image.fromarray(np.uint8(lbp))
return im
def load_stereo(image_path: str, model: CameraModel = None):
""" Loads an image and processes it with a model (if given)
:param image_path: The path to an image
:type image_path: String
:param model: A camera model path to undistort the image and apply LUT
:type model: CameraModel class
:returns cv2 image
"""
pattern = 'gbrg'
img = Image.open(str(image_path))
img = demosaic(img, pattern)
if model:
# Apply the model
img = model.undistort(img)
# print("Applying model")
return img
def load_image(img_file):
img_height = args.height
img_width = args.width
img = Image.open(img_file)
img = demosaic(img, 'gbrg')
if 'left' in img_file:
img = args.left_model.undistort(img)
if 'right' in img_file:
img = args.right_model.undistort(img)
img = np.array(img).astype(np.uint8)
img = img[crop[0]:crop[1], crop[2]:crop[3]]
orig_img_height = img.shape[0]
orig_img_width = img.shape[1]
zoom_y = img_height / orig_img_height
zoom_x = img_width / orig_img_width
img = np.array(
Image.fromarray(img).resize((img_width, img_height),
resample=Image.ANTIALIAS))
return img, zoom_x, zoom_y, orig_img_width, orig_img_height
def load_stereo(image_path: str, model: CameraModel = None):
""" Loads an image and processes it with a model (if given)
:param image_path: The path to an image
:type image_path: String
:param model: A camera model path to undistort the image and apply LUT
:type model: CameraModel class
:param view: A pass-through variable
:param outName: Another pass-through variable
:type outName: str
:returns cv2 image
"""
pattern = 'gbrg'
with Image.open(str(image_path)) as img:
img = demosaic(img, pattern)
if model:
# Apply the model
img = model.undistort(img)
return img
def load_image_demosaic(filename):
BAYER_STEREO = 'gbrg'
BAYER_MONO = 'rggb'
camera = re.search('(stereo|mono_(left|right|rear))', filename).group(0)
if camera == 'stereo':
pattern = BAYER_STEREO
else:
pattern = BAYER_MONO
img = Image.open(filename)
print(img)
img = demosaic(img, pattern)
#img = cv2.imread(filename)
print(img.shape)
#img = cv2.resize(img,(256,256))
#img = demosaic(img, pattern)
cv2.imshow('image',img)
cv2.waitKey(0)
return np.array(img).astype(np.uint8)
def load_image(image_path):
img = Image.open(image_path)
img = demosaic(img, 'gbrg')
im = Image.fromarray(np.uint8(img))
return im
img_suffix = '.png'
dump_bits = 6
img_name = listdir(pic_file)
img_postfix = img_name[0][:dump_bits]
img_timeStamp = [int(x[dump_bits:].strip(img_suffix)) for x in img_name]
img_timeStamp = sorted(img_timeStamp)
sorted_imgname = [
img_postfix + str(x) + img_suffix for x in np.array(img_timeStamp)[idx]
]
for k, each in enumerate(sorted_imgname):
img = join(pic_file, each)
if exists(img):
img = Image.open(img)
img = demosaic(img, 'GBRG')
img = cv2.cvtColor(img.astype(np.uint8), cv2.COLOR_RGB2BGR)
img = cv2.resize(img, (224, 224))
cv2.imwrite(save_file + str(k) + img_suffix, img)
else:
print(img + 'is not found')
break
if k % 10 == 0:
print('%d / %d' % (k, len(sorted_imgname)))
# save ground truth
np.save(root + 'gps_' + data_name + '.npy', gps)
def __call__(self, img):
if random.uniform(0, 1) > self.probability:
return img
img = demosaic(img, 'gbrg')
return Image.fromarray(img.astype('uint8')).convert('RGB')