def execute(self, arff_data):
attributes = [attribute[0] for attribute in arff_data['attributes']]
dataset = arff_data['data']
X = (arff_data['X'].T.tolist() if 'X' in arff_data else [])
base_height = np.array(dataset[:, attributes.index('baseHeight')],
dtype='float64')
target_height = np.array(dataset[:,
attributes.index('targetHeight')],
dtype='float64')
base_width = np.array(dataset[:, attributes.index('baseWidth')],
dtype='float64')
target_width = np.array(dataset[:, attributes.index('targetWidth')],
dtype='float64')
X.append(
np.minimum(base_height * base_width, target_height * target_width))
base_x = np.array(dataset[:, attributes.index('baseX')],
dtype='float64')
target_x = np.array(dataset[:, attributes.index('targetX')],
dtype='float64')
base_y = np.array(dataset[:, attributes.index('baseY')],
dtype='float64')
target_y = np.array(dataset[:, attributes.index('targetY')],
dtype='float64')
X.append(
np.sqrt(
np.power(np.abs(base_x - target_x), 2) +
np.power(np.abs(base_y - target_y), 2)))
X.append(
np.abs((base_height * base_width) -
(target_height * target_width)) / np.maximum(
np.minimum(base_height * base_width, target_height *
target_width), np.ones(len(base_height))))
X.append(dataset[:, attributes.index('chiSquared')])
arff_data['X'] = np.array(X, dtype='float64').T
prev_features = (arff_data['features']
if 'features' in arff_data else [])
arff_data['features'] = prev_features + [
'area', 'displacement', 'sdr', 'chisquared'
]
arff_data['y'] = np.array(
arff_data['data'][:, attributes.index(self._class_attr)],
dtype='int16')
return arff_data
def create(self):
self._ratio = 28
self._input_path = '../dataset/short/00001_00_0.1s.ARW'
self._pattern = 0
def pack_raw(raw):
# pack Bayer image to 4 channels
im = raw.raw_image_visible.astype(np.float32)
im = np.maximum(im - 512, 0) / (16383 - 512) # subtract the black level
im = np.expand_dims(im, axis=2)
img_shape = im.shape
H = img_shape[0]
W = img_shape[1]
out = np.concatenate((im[0:H:2, 0:W:2, :],
im[0:H:2, 1:W:2, :],
im[1:H:2, 1:W:2, :],
im[1:H:2, 0:W:2, :]), axis=2)
return out
# 读入图像
raw = rawpy.imread(self._input_path)
input_full = np.expand_dims(pack_raw(raw), axis=0) * self._ratio
input_full = np.minimum(input_full, 1.0)
self._pattern = input_full
return self._pattern
def load_raw(img_path):
ratio = 300
raw = rawpy.imread(img_path)
input_full = np.expand_dims(pack_raw(raw), axis=0) * ratio
input_full = np.minimum(input_full, 1.0)
return input_full
def load_raw2(in_name, input_dir, ratio):
img_path = os.path.join(input_dir, in_name)
raw = rawpy.imread(img_path)
input_full = np.expand_dims(pack_raw(raw), axis=0) * ratio
input_full = np.minimum(input_full, 1.0)
# # keras 中是CHWN
# channel_fist_img = covnert_to_channel_first(input_full)
return input_full
def load_raw(in_name, gt_dir, input_dir):
ratio, img_path, = get_ratio(in_name, gt_dir, input_dir)
raw = rawpy.imread(img_path)
input_full = np.expand_dims(pack_raw(raw), axis=0) * ratio
input_full = np.minimum(input_full, 1.0)
# # keras 中是CHWN
# channel_fist_img = covnert_to_channel_first(input_full)
return input_full
def execute(self, arff_data, attributes, X):
X.append(arff_data['data'][:, attributes.index('phash')])
X.append(arff_data['data'][:, attributes.index('chiSquared')])
image_diff = np.array(arff_data['data'][:, attributes.index('imageDiff')], dtype='float64')
base_width = np.array(arff_data['data'][:, attributes.index('baseWidth')], dtype='float64')
target_width = np.array(arff_data['data'][:, attributes.index('targetWidth')], dtype='float64')
base_height = np.array(arff_data['data'][:, attributes.index('baseHeight')], dtype='float64')
target_height = np.array(arff_data['data'][:, attributes.index('targetHeight')], dtype='float64')
min_area = np.minimum(base_width * base_height, target_width * target_height)
X.append(image_diff / (np.maximum(min_area, 1) * 255))
arff_data['features'] = arff_data['features'] + ['phash', 'chiSquared', 'imageDiff']
return X
def execute(self, arff_data, attributes, X):
nrows = len(X) if len(X) > 0 else 1
base_x = np.array(arff_data['data'][:, attributes.index('baseX')], dtype='float64')
base_y = np.array(arff_data['data'][:, attributes.index('baseY')], dtype='float64')
base_height = np.array(arff_data['data'][:, attributes.index('baseHeight')], dtype='float64')
base_width = np.array(arff_data['data'][:, attributes.index('baseWidth')], dtype='float64')
target_x = np.array(arff_data['data'][:, attributes.index('targetX')], dtype='float64')
target_y = np.array(arff_data['data'][:, attributes.index('targetY')], dtype='float64')
target_height = np.array(arff_data['data'][:, attributes.index('targetHeight')], dtype='float64')
target_width = np.array(arff_data['data'][:, attributes.index('targetWidth')], dtype='float64')
base_parent_x = np.array(arff_data['data'][:, attributes.index('baseParentX')], dtype='float64')
base_parent_y = np.array(arff_data['data'][:, attributes.index('baseParentY')], dtype='float64')
target_parent_x = np.array(arff_data['data'][:, attributes.index('targetParentX')], dtype='float64')
target_parent_y = np.array(arff_data['data'][:, attributes.index('targetParentY')], dtype='float64')
base_p_sibling_x = np.array(arff_data['data'][:, attributes.index('basePreviousSiblingLeft')], dtype='float64')
base_p_sibling_y = np.array(arff_data['data'][:, attributes.index('basePreviousSiblingTop')], dtype='float64')
target_p_sibling_x = np.array(arff_data['data'][:, attributes.index('targetPreviousSiblingLeft')], dtype='float64')
target_p_sibling_y = np.array(arff_data['data'][:, attributes.index('targetPreviousSiblingTop')], dtype='float64')
base_n_sibling_x = np.array(arff_data['data'][:, attributes.index('baseNextSiblingLeft')], dtype='float64')
base_n_sibling_y = np.array(arff_data['data'][:, attributes.index('baseNextSiblingTop')], dtype='float64')
target_n_sibling_x = np.array(arff_data['data'][:, attributes.index('targetNextSiblingLeft')], dtype='float64')
target_n_sibling_y = np.array(arff_data['data'][:, attributes.index('targetNextSiblingTop')], dtype='float64')
X.append(np.abs((base_x - base_parent_x) - (target_x - target_parent_x)) / np.minimum(target_width, base_width))
X.append(np.abs((base_y - base_parent_y) - (target_y - target_parent_y)) / np.minimum(target_height, base_height))
X.append(np.abs((base_x - base_p_sibling_x) - (target_x - target_p_sibling_x)) /
np.minimum(target_width, base_width))
X.append(np.abs((base_y - base_p_sibling_y) - (target_y - target_p_sibling_y)) /
np.minimum(target_height, base_height))
X.append(np.abs((base_x - base_n_sibling_x) - (target_x - target_n_sibling_x)) /
np.minimum(target_width, base_width))
X.append(np.abs((base_y - base_n_sibling_y) - (target_y - target_n_sibling_y)) /
np.minimum(target_height, base_height))
arff_data['features'] = arff_data['features'] + ['parent_x', 'parent_y',
'previous_sibling_x', 'previous_sibling_y',
'next_sibling_x', 'next_sibling_y']
return X