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Zero
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from typing import Iterable
import cv2
import numpy as np
def get_target_size_and_crop(target_size, image_size):
if target_size is None:
return None, None
elif not isinstance(target_size, Iterable): # Resize the shorter side to target size and center crop to get a square
if image_size[0] <= image_size[1]:
aspect_ratio = image_size[0] / image_size[1]
resize_to = (target_size, int(round(target_size / aspect_ratio)))
if resize_to[1] > target_size:
crop = ((resize_to[1] - target_size) // 2, 0, target_size, target_size)
else:
crop = None
else:
aspect_ratio = image_size[1] / image_size[0]
resize_to = (int(round(target_size / aspect_ratio)), target_size)
if resize_to[0] > target_size:
crop = (0, (resize_to[0] - target_size) // 2, target_size, target_size)
else:
crop = None
elif isinstance(target_size, Iterable) and len(target_size) == 2: # Resize to target size and center crop
target_aspect_ratio = target_size[1] / target_size[0]
image_aspect_ratio = image_size[1] / image_size[0]
if target_aspect_ratio > image_aspect_ratio:
resize_to = (int(round(target_size[1] / image_aspect_ratio)), target_size[1])
crop = (0, (resize_to[0] - target_size[0]) // 2, target_size[1], target_size[0])
else:
resize_to = (target_size[0], int(round(target_size[0] * image_aspect_ratio)))
crop = ((resize_to[1] - target_size[1]) // 2, 0, target_size[1], target_size[0])
# print(f"Resizing to {resize_to} and cropping to {crop}")
return resize_to, crop
def process_img(
img: np.array, target_size=None, crop=None
):
if target_size is not None and (target_size[0] != img.shape[0] or target_size[1] != img.shape[1]):
img = cv2.resize(
img,
(target_size[1], target_size[0]),
interpolation=cv2.INTER_LINEAR,
)
if crop is not None:
img = img[crop[1]:crop[1]+crop[3], crop[0]:crop[0]+crop[2], :]
img = np.transpose(img, (2, 0, 1))
return img
def process_depth(depth: np.array, target_size=None, crop=None):
if target_size is not None and (target_size[0] != depth.shape[0] or target_size[1] != depth.shape[1]):
depth = cv2.resize(
depth,
(target_size[1], target_size[0]),
interpolation=cv2.INTER_NEAREST,
)
depth = depth[..., None]
if crop is not None:
depth = depth[crop[1]:crop[1]+crop[3], crop[0]:crop[0]+crop[2], :]
depth = np.transpose(depth, (2, 0, 1))
return depth
def process_flow(flow: np.array, target_size=None, crop=None):
flow = flow.transpose(1, 2, 0)
if target_size is not None and (target_size[0] != flow.shape[0] or target_size[1] != flow.shape[1]):
x_scale = target_size[1] / flow.shape[1]
y_scale = target_size[0] / flow.shape[0]
flow = cv2.resize(
flow,
(target_size[1], target_size[0]),
interpolation=cv2.INTER_NEAREST,
)
flow = flow * np.array([x_scale, y_scale], dtype=np.float32).reshape(1, 1, 2)
if crop is not None:
flow = flow[crop[1]:crop[1]+crop[3], crop[0]:crop[0]+crop[2], :]
flow = np.transpose(flow, (2, 0, 1))
return flow
def process_proj(proj: np.array, original_size, target_size=None, crop=None):
proj = proj.copy()
proj[0, :] *= 2.0 / original_size[1]
proj[1, :] *= 2.0 / original_size[0]
proj[0, 2] = proj[0, 2] - 1.0
proj[1, 2] = proj[1, 2] - 1.0
#if crop is not None:
# raise NotImplementedError()
#if target_size is not None and (target_size[0] != original_size[0] or target_size[1] != original_size[1]):
# x_scale = target_size[1] / original_size[1]
# y_scale = target_size[0] / original_size[0]
# proj[0, :] *= x_scale
# proj[1, :] *= y_scale
#if crop is not None:
# proj[0, 2] -= crop[0]
# proj[1, 2] -= crop[1]
return proj
def flow_selector_2(ids):
return ((ids[0], True),), ((ids[1], False),)
def flow_selector_3(ids):
return ((ids[0], False), (ids[0], True)), ((ids[1], True), (ids[2], False))
def flow_selector_seq(ids):
fwd = [(i, True) for i in ids[:-1]]
bwd = [(i, False) for i in ids[1:]]
return fwd, bwd
def get_flow_selector(frame_count, is_sequential=False):
if not is_sequential:
if frame_count == 2:
flow_selector = flow_selector_2
elif frame_count == 3:
flow_selector = flow_selector_3
else:
raise ValueError(f"Unknown frame count: {frame_count}")
else:
flow_selector = flow_selector_seq
return flow_selector
def index_selector_pair(id, frame_count, dilation, left_offset):
ids = [i
for i in range(
id - left_offset,
id - left_offset + frame_count * dilation,
dilation,
)
if i != id
]
return ids
def index_selector_seq(id, frame_count, dilation, left_offset):
ids = [id + i * dilation - left_offset for i in range(frame_count)]
return ids
def get_index_selector(is_sequential=False):
if not is_sequential:
index_selector = index_selector_pair
else:
index_selector = index_selector_seq
return index_selector
def sequence_sampler_crop(seq, seq_len, left_offset, sub_seq_len):
if seq_len < sub_seq_len:
return []
datapoints = [(seq, i + left_offset) for i in range(seq_len - sub_seq_len)]
return datapoints
def sequence_sampler_full(seq, seq_len, left_offset, sub_seq_len):
datapoints = [(seq, i) for i in range(seq_len - 1)]
return datapoints
def get_sequence_sampler(crop=True):
if crop:
sequence_sampler = sequence_sampler_crop
else:
sequence_sampler = sequence_sampler_full
return sequence_sampler
def get_ids_for_sequence(dataset, sequence):
seq_id_datapoints = [(i, dp) for i, dp in enumerate(dataset._datapoints) if dp[0] == sequence]
seq_ids = [i for i, _ in seq_id_datapoints]
seq_datapoints = [dp for _, dp in seq_id_datapoints]
return seq_ids, seq_datapoints
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