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# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License.
import random
import numpy as np
import random
import cv2
from typing import List
from PIL import Image
from dynamic_utils import (extend_key_frame_to_all,
sample_key_frames)
import imutils
import math
from scipy.ndimage import gaussian_filter1d
from glob import glob
class RandomRegionSampler(object):
def __init__(self,
num_rois: int,
scales: tuple,
ratios: tuple,
scale_jitter: float):
""" Randomly sample several RoIs
Args:
num_rois (int): number of sampled RoIs per image
scales (tuple): scales of candidate bounding boxes
ratios (tuple): aspect ratios of candidate bounding boxes
scale_jitter (float): scale jitter factor, positive number
"""
self.num_rois = num_rois
self.scale_jitter = scale_jitter
scales = np.array(scales, np.float32)
ratios = np.array(ratios, np.float32)
widths = scales.reshape(1, -1) * np.sqrt(ratios).reshape(-1, 1)
heights = scales.reshape(1, -1) / np.sqrt(ratios).reshape(-1, 1)
self.anchors = np.concatenate((widths.reshape(-1, 1),
heights.reshape(-1, 1)), axis=-1)
def sample(self, data: List[np.ndarray]) -> np.ndarray:
""" Sample boxes.
Args:
data (list): image list, each element is a numpy.ndarray
in shape of [H, W, 3]
Returns:
boxes (np.ndarray): the sampled bounding boxes. in shape of
[self.num_rois, 4], represented in (x1, y1, x2, y2).
"""
h, w = data[0].shape[0:2]
# random sample box shapes
anchor_inds = np.random.randint(0, len(self.anchors),
size=(self.num_rois, ))
box_shapes = self.anchors[anchor_inds].copy()
if self.scale_jitter is not None:
scale_factors = np.random.uniform(-self.scale_jitter,
self.scale_jitter,
size=(self.num_rois, 2))
box_shapes = box_shapes * np.exp(scale_factors)
box_shapes[:, 0] = np.clip(box_shapes[:, 0], 1, w - 1)
box_shapes[:, 1] = np.clip(box_shapes[:, 1], 1, h - 1)
#print("box shapes",box_shapes,box_shapes.shape)
# random sample box x1, y1
x1 = np.random.uniform(0, w - box_shapes[:, 0])
y1 = np.random.uniform(0, h - box_shapes[:, 1])
#print("x1, y1",x1,y1)
boxes = np.concatenate((x1.reshape(-1, 1),
y1.reshape(-1, 1),
(x1 + box_shapes[:, 0]).reshape(-1, 1),
(y1 + box_shapes[:, 1]).reshape(-1, 1)),
axis=1)
#print("sampled initial boxes",boxes)
return boxes
def sample_box_shapes(self, data: List[np.ndarray]) -> np.ndarray:
""" Sample boxes.
Args:
data (list): image list, each element is a numpy.ndarray
in shape of [H, W, 3]
Returns:
boxes (np.ndarray): the sampled bounding boxes. in shape of
[self.num_rois, 4], represented in (x1, y1, x2, y2).
"""
h, w = data[0].shape[0:2]
# random sample box shapes
anchor_inds = np.random.randint(0, len(self.anchors),
size=(self.num_rois, ))
box_shapes = self.anchors[anchor_inds].copy()
if self.scale_jitter is not None:
scale_factors = np.random.uniform(-self.scale_jitter,
self.scale_jitter,
size=(self.num_rois, 2))
box_shapes = box_shapes * np.exp(scale_factors)
box_shapes[:, 0] = np.clip(box_shapes[:, 0], 1, w - 1)
box_shapes[:, 1] = np.clip(box_shapes[:, 1], 1, h - 1)
#print(" gaussian box shapes",box_shapes)
return box_shapes
class PatchMask(object):
def __init__(self,
use_objects: bool,
objects_path: str,
region_sampler: dict,
key_frame_probs: list,
loc_velocity: float,
rot_velocity: float,
size_velocity: float,
label_prob: float,
patch_transformation: str,
motion_type: str):
""" Core transformation in Catch-the-Patch.
Args:
region_sampler (dict): region sampler setting, it will be used to
construct a RandomRegionSampler object.
key_frame_probs (list): probabilities of sampling how many key
frames. The sum of this list should be 1.
loc_velocity (float): the maximum patch movement speed. (pix per
frame).
size_velocity (float): the maximum size change ratios between two
neighbouring frames.
label_prob (float): how many percentages of frames will be
modified. Note that even the frame is not modified, we still
force the model to infer the patch positions. (see MRM module
in the paper).
"""
self.region_sampler = RandomRegionSampler(**region_sampler)
self.key_frame_probs = key_frame_probs
self.loc_velocity = loc_velocity
self.rot_velocity = rot_velocity
self.size_velocity = size_velocity
self.label_prob = label_prob
if motion_type is not None:
self.motion_type = motion_type
self.patch_transformation = patch_transformation
self.use_objects = use_objects
if self.use_objects:
#self.object_list = glob("/ibex/user/jianl0b/Dataset/Fida_file_1/video_images/micheal_objects/cleaned/images/*/*")
self.object_list = glob(objects_path+"/*/*")
#self.object_list = glob("/ibex/project/c2134/Fida/micheal_objects_big/cleaned_big/images/*/*")
print(self.object_list[0:10],len(self.object_list))
def paste_objects(self, data, traj_rois, boxes):
objects_list = []
label_list = []
for i in range(len(boxes)):
objects, crop_index = self.pick_objects(data, traj_rois[i])
labels = np.random.uniform(0, 1, size=(len(data), ))
labels[crop_index] = 0.0
labels[0] = 0.0
labels = labels <= self.label_prob
objects_list.append(objects)
label_list.append(labels)
return objects_list, None, label_list
def paste_patches(self, data, traj_rois, boxes):
patches_list = []
alphas_list = []
label_list = []
for i in range(len(boxes)):
patches, crop_index = self.pick_patches(data, traj_rois[i])
alphas = self.pick_alphas(data, traj_rois[i], crop_index)
labels = np.random.uniform(0, 1, size=(len(data), ))
labels[crop_index] = 0.0
labels[0] = 0.0
labels = labels <= self.label_prob
patches_list.append(patches)
alphas_list.append(alphas)
label_list.append(labels)
return patches_list, alphas_list, label_list
def pick_patches(self,
data: List[np.ndarray],
traj_rois: np.ndarray) -> tuple:
""" Pick image patches from the raw video frame.
We just randomly select a frame index, and crop the frame according to
the trajectory rois. This cropped patch will be resized into the
suitable size specified by the traj_rois.
Args:
data (List[np.ndarray]): list of images, each element is in shape
of [H, W, 3]
traj_rois (np.ndarray): the generated trajectories, in shape of
[N_frames, 4]. (x1, y1, x2, y2)
Returns:
patches (List[np.ndarray]): the cropped patches
select_idx (int): the frame index which the source patch
cropped from.
"""
traj_sizes = traj_rois[..., 2:4] - traj_rois[..., 0:2]
num = len(traj_sizes)
select_idx = random.randint(0, num - 1)
x1, y1, x2, y2 = traj_rois[select_idx]
traj_rois_H = y2 - y1
traj_rois_W = x2 - x1
img = data[select_idx]
img_H, img_W, _ = img.shape
if img_W - traj_rois_W - 1 >= 0 and img_H - traj_rois_H - 1 >= 0:
new_x1 = random.randint(0, img_W - traj_rois_W - 1)
new_y1 = random.randint(0, img_H - traj_rois_H - 1)
new_x2 = new_x1 + traj_rois_W
new_y2 = new_y1 + traj_rois_H
img = img[new_y1:new_y2, new_x1:new_x2, :]
else:
img = img
patches = [cv2.resize(img, (traj_sizes[i, 0], traj_sizes[i, 1]))
for i in range(traj_rois.shape[0])]
return patches, select_idx
def pick_objects(self,
data: List[np.ndarray],
traj_rois: np.ndarray) -> tuple:
""" Pick image patches from the raw video frame.
We just randomly select a frame index, and crop the frame according to
the trajectory rois. This cropped patch will be resized into the
suitable size specified by the traj_rois.
Args:
data (List[np.ndarray]): list of images, each element is in shape
of [H, W, 3]
traj_rois (np.ndarray): the generated trajectories, in shape of
[N_frames, 4]. (x1, y1, x2, y2)
Returns:
patches (List[np.ndarray]): the cropped patches
select_idx (int): the frame index which the source patch
cropped from.
"""
traj_sizes = traj_rois[..., 2:4] - traj_rois[..., 0:2]
num = len(traj_sizes)
select_idx = random.randint(0, num - 1)
#print(len(data),traj_rois.shape)
x1, y1, x2, y2 = traj_rois[select_idx]
#print(x1, y1, x2, y2)
object_ind = random.randint(0, len(self.object_list)- 1)
object_img = Image.open(self.object_list[object_ind])
object_img = object_img.resize((x2-x1,y2-y1))
objects = [object_img.resize((traj_sizes[i, 0], traj_sizes[i, 1]))
for i in range(traj_rois.shape[0])]
return objects, select_idx
def pick_alphas(self,
data,
traj_rois: np.ndarray,
crop_index: int):
""" Generate the alpha masks for merging the patches into the raw
frames:
out_frame = raw_frame * (1 - alpha) + patch * alpha.
Despite the transparency, the alpha values are also used to mask the
patches into some predefined shapes, like ellipse or rhombus.
There are many strange constants in this function. But we do not
conduct any ablation analysis on these constants. They should have
little impact to the final performances.
Args:
data (List[np.ndarray]): list of images, each element is in shape
of [H, W, 3]
traj_rois (np.ndarray): the generated trajectories, in shape of
[N_frames, 4]. (x1, y1, x2, y2)
crop_index (int): the frame index which the source patch
cropped from.
Returns:
alphas (List[np.ndarray]): the generated alpha values
"""
traj_sizes = traj_rois[..., 2:4] - traj_rois[..., 0:2]
num_frames = traj_sizes.shape[0]
base_w, base_h = traj_sizes[crop_index]
base_x_grids, base_y_grids = np.meshgrid(
np.arange(base_w).astype(np.float32),
np.arange(base_h).astype(np.float32)
)
ctr_w = (base_w - 1) // 2
ctr_h = (base_h - 1) // 2
dist_to_ctr_x = np.abs(base_x_grids - ctr_w) / base_w
dist_to_ctr_y = np.abs(base_y_grids - ctr_h) / base_h
mask_type = int(np.random.choice(3, p=[0.5, 0.35, 0.15]))
if mask_type == 0:
dist_to_ctr = np.maximum(dist_to_ctr_x, dist_to_ctr_y)
base_alpha = np.ones((base_h, base_w), np.float32)
elif mask_type == 1:
dist_to_ctr = np.sqrt(dist_to_ctr_x ** 2 + dist_to_ctr_y ** 2)
base_alpha = np.where(dist_to_ctr < 0.5,
np.ones((base_h, base_w), np.float32),
np.zeros((base_h, base_w), np.float32))
elif mask_type == 2:
dist_to_ctr = (dist_to_ctr_x + dist_to_ctr_y)
base_alpha = np.where(dist_to_ctr < 0.5,
np.ones((base_h, base_w), np.float32),
np.zeros((base_h, base_w), np.float32))
else:
raise NotImplementedError
use_smooth_edge = random.uniform(0, 1) < 0.5
if use_smooth_edge:
turning_point = random.uniform(0.30, 0.45)
k = -1 / (0.5 - turning_point)
alpha_mul = k * dist_to_ctr - 0.5 * k
alpha_mul = np.clip(alpha_mul, 0, 1)
base_alpha = base_alpha * alpha_mul
# sample key frames
key_inds = sample_key_frames(num_frames, self.key_frame_probs)
frame_alphas = np.random.uniform(0.8, 1.0, size=(len(key_inds), 1))
frame_alphas = extend_key_frame_to_all(frame_alphas, key_inds)
alphas = []
for frame_idx in range(num_frames):
w, h = traj_sizes[frame_idx]
i_alpha = cv2.resize(base_alpha, (w, h))
i_alpha = i_alpha * frame_alphas[frame_idx]
alphas.append(i_alpha)
return alphas
def get_rotation_angles(self,
num_frames,
transform_param: dict):
key_frame_probs = transform_param['key_frame_probs']
loc_key_inds = sample_key_frames(num_frames, key_frame_probs)
rot_velocity = transform_param['rot_velocity']
rot_angles = np.zeros((transform_param['traj_rois'].shape[0],1))
#print("rotation angles original",rot_angles.shape,loc_key_inds)
rot_angles_list= [np.expand_dims(rot_angles, axis=0)]
for i in range(len(loc_key_inds) - 1):
if rot_velocity > 0:
index_diff = loc_key_inds[i + 1] - loc_key_inds[i]
shifts = np.random.uniform(low=-rot_velocity* index_diff,
high=rot_velocity* index_diff,
size=rot_angles.shape)
rot_angles = rot_angles + shifts
rot_angles_list.append(np.expand_dims(rot_angles, axis=0))
rot_angles = np.concatenate(rot_angles_list, axis=0)
rot_angles = extend_key_frame_to_all(rot_angles, loc_key_inds, 'random')
rot_angles = rot_angles.transpose((1, 0, 2))
return rot_angles
def get_shear_factors(self,
num_frames,
transform_param: dict):
key_frame_probs = transform_param['key_frame_probs']
loc_key_inds = sample_key_frames(num_frames, key_frame_probs)
#print("Loc key inds shear",loc_key_inds)
rot_velocity = transform_param['rot_velocity']
rot_angles = np.zeros((transform_param['traj_rois'].shape[0],1))
#print("rotation angles original",rot_angles.shape,loc_key_inds)
rot_angles_list= [np.expand_dims(rot_angles, axis=0)]
for i in range(len(loc_key_inds) - 1):
if rot_velocity > 0:
index_diff = loc_key_inds[i + 1] - loc_key_inds[i]
shifts = np.random.uniform(low=-rot_velocity* index_diff,
high=rot_velocity* index_diff,
size=rot_angles.shape)
#scales = np.exp(shifts)
#print("shifts shear", shifts)
#rot_angles = scales
rot_angles = rot_angles + shifts
rot_angles_list.append(np.expand_dims(rot_angles, axis=0))
rot_angles = np.concatenate(rot_angles_list, axis=0)
rot_angles = extend_key_frame_to_all(rot_angles, loc_key_inds, 'random')
rot_angles = rot_angles.transpose((1, 0, 2))
return rot_angles
def _apply_image(self,
data: List[np.ndarray],
transform_param: dict):
data_1 = data
# we sort the size and firstly paste the large patch
# this trick is because, if we paste the small patch first, it may
# be totally covered by a large one.
sizes = transform_param['traj_rois'][..., 2:4] - \
transform_param['traj_rois'][..., 0:2]
avg_sizes = np.prod(np.mean(sizes, axis=1), axis=1)
arg_rank = np.argsort(avg_sizes)[::-1]
width, height,_ = data_1[0].shape
#print(width,height)
if self.use_objects:
if transform_param['patch_transformation'] == 'rotation':
rot_angles = self.get_rotation_angles(len(data_1),transform_param)
transformed_data_1 = []
for frame_idx in range(len(data_1)):
i_rois = transform_param['traj_rois'][:, frame_idx, :]
img = data_1[frame_idx].copy()
for patch_idx in arg_rank:
if not transform_param['traj_labels'][patch_idx][frame_idx]:
continue
i_object = transform_param['patches'][patch_idx][frame_idx] # here patches are objects
i_object = np.array(i_object)
angle = int(rot_angles[patch_idx][frame_idx])
rotated_i_object = imutils.rotate_bound(i_object, angle)
rotated_i_alpha = rotated_i_object[..., -1]
rotated_i_alpha = rotated_i_alpha / 255.0
rotated_i_object = rotated_i_object[..., :3]
h_prime, w_prime, channels = rotated_i_object.shape
x1, y1, x2, y2 = i_rois[patch_idx]
h, w = y2 - y1, x2 - x1
if ((h_prime - h) % 2) == 0:
delta_h1 = delta_h2 = math.ceil((h_prime - h) / 2)
else:
delta_h1 = math.ceil((h_prime - h) / 2)
delta_h2 = math.floor((h_prime - h) / 2)
if ((w_prime - w) % 2) == 0:
delta_w1 = delta_w2 = math.ceil((w_prime - w) / 2)
else:
delta_w1 = math.ceil((w_prime - w) / 2)
delta_w2 = math.floor((w_prime - w) / 2)
x1_new, y1_new, x2_new, y2_new = x1 - delta_w1, y1 - delta_h1, x2 + delta_w2, y2 + delta_h2
if all(i >= 0 for i in [x1_new, y1_new, x2_new, y2_new]) and all(
i < width for i in [x1_new, y1_new, x2_new, y2_new]):
# in bound
i_patch = rotated_i_object
i_alpha = rotated_i_alpha[..., np.newaxis]
img[y1_new:y2_new, x1_new:x2_new, :] = img[y1_new:y2_new, x1_new:x2_new, :] * (1 - i_alpha) + i_patch * i_alpha
else:
# out of bound
img_H, img_W, C = img.shape
patch_H, patch_W, _ = rotated_i_object.shape
extended_img = np.zeros((img_H + 2 * patch_H, img_W + 2 * patch_W, C), dtype=img.dtype)
extended_img[patch_H:(img_H + patch_H), patch_W:(img_W + patch_W), :] = img
x1_new += patch_W
x2_new += patch_W
y1_new += patch_H
y2_new += patch_H
i_alpha = rotated_i_alpha[..., np.newaxis]
extended_img[y1_new:y2_new, x1_new:x2_new, :] = extended_img[y1_new:y2_new, x1_new:x2_new, :] * (1 - i_alpha) + rotated_i_object * i_alpha
img = extended_img[patch_H:(img_H + patch_H), patch_W:(img_W + patch_W), :]
img = np.array(img)
transformed_data_1.append(img)
return transformed_data_1
@staticmethod
def rectangle_movement(boxes: np.ndarray,
img_wh: tuple,
loc_velocity: float,
size_velocity: float,
num_frames: int,
key_frame_probs: List[float]) -> np.ndarray:
""" Simulate the object movement.
Args:
boxes (np.ndarray): in shpae of [N_boxes, 4]
img_wh (tuple): image width and image height
loc_velocity (float): max speed of the center point movement
size_velocity (float): max speed of size changes
num_frames (int): number of frames
key_frame_probs (float): probability distribution of how many key
frames will be sampled.
Returns
all_boxes (np.ndarray): the generated box trajectory, in shpae
of [N_traj, N_frame, 4].
"""
# Step 1, sample key frames for location changes
loc_key_inds = sample_key_frames(num_frames, key_frame_probs)
# Step 2, decide box locations in key frames
ctr_pts = (boxes[:, 0:2] + boxes[:, 2:4]) * 0.5
#print("center points original",ctr_pts)
box_sizes = (boxes[:, 2:4] - boxes[:, 0:2])
#print("box sizes = ",box_sizes,box_sizes.shape)
min_ctr_pts = box_sizes * 0.5
max_ctr_pts = np.array(img_wh[0:2]).reshape(1, 2) - box_sizes * 0.5
#print("initial center points ",ctr_pts,loc_key_inds)
ctr_pts_list = [np.expand_dims(ctr_pts, axis=0)]
#print("ctr pts list",ctr_pts_list)
for i in range(len(loc_key_inds) - 1):
if loc_velocity > 0:
index_diff = loc_key_inds[i + 1] - loc_key_inds[i]
shifts = np.random.uniform(low=-loc_velocity * index_diff,
high=loc_velocity * index_diff,
size=ctr_pts.shape)
#print("shifts",shifts)
ctr_pts = ctr_pts + shifts
ctr_pts = np.clip(ctr_pts, min_ctr_pts, max_ctr_pts)
ctr_pts_list.append(np.expand_dims(ctr_pts, axis=0))
ctr_pts = np.concatenate(ctr_pts_list, axis=0)
ctr_pts = extend_key_frame_to_all(ctr_pts, loc_key_inds, 'random')
#print("all center points ",ctr_pts,ctr_pts.shape)
# Step 3, sample key frames for shape changes
size_key_inds = sample_key_frames(num_frames, key_frame_probs)
# Step 4, setup shape in different key frames
box_sizes_list = [np.expand_dims(box_sizes, axis=0)]
for i in range(len(size_key_inds) - 1):
if size_velocity > 0:
index_diff = size_key_inds[i + 1] - size_key_inds[i]
scales = np.random.uniform(low=-size_velocity * index_diff,
high=size_velocity * index_diff,
size=box_sizes.shape)
scales = np.exp(scales)
box_sizes = box_sizes * scales
box_sizes_list.append(np.expand_dims(box_sizes, axis=0))
box_sizes = np.concatenate(box_sizes_list, axis=0)
# print("box sizes before interpolation",box_sizes,size_key_inds)
box_sizes = extend_key_frame_to_all(box_sizes, size_key_inds, 'random')
#print("box sizes after interpolation",box_sizes)
# Step 5, construct boxes in key frames
all_boxes = np.concatenate((ctr_pts - box_sizes * 0.5,
ctr_pts + box_sizes * 0.5), axis=2)
# all_boxes[..., 0::2] = np.clip(all_boxes[..., 0::2], 0, img_wh[0])
# all_boxes[..., 1::2] = np.clip(all_boxes[..., 1::2], 0, img_wh[1])
all_boxes = all_boxes.transpose((1, 0, 2))
return all_boxes
@staticmethod
def gaussian_movement(box_shapes: np.ndarray,
img_wh: tuple,
num_trajs: int,
size_velocity: float,
num_frames: int,
key_frame_probs: List[float]) -> np.ndarray:
""" Simulate the object movement.
Args:
Returns
all_boxes (np.ndarray): the generated box trajectory, in shpae
of [N_traj, N_frame, 4].
"""
def create_traj(box_shapes):
w = img_wh[0]
h = img_wh[1]
#print("gaussian",w,h)
n_points = 48 # how many points to create trajectory
sigma = 8 # bigger sigma -> smoother trajectory
# simulate trajectory points
#x = np.random.uniform(0,112,n_points)
#y = np.random.uniform(0,112,n_points)
# for 112 x 112
x = np.random.uniform(1+box_shapes[0]/2,w-1-box_shapes[0]/2,n_points)
y = np.random.uniform(1+box_shapes[1]/2,h-1-box_shapes[1]/2,n_points)
# for 224x 224
# x = np.random.uniform(0,112,n_points)
# y = np.random.uniform(0,112,n_points)
# smooth trajectory
xk = gaussian_filter1d(x, sigma=sigma, mode='reflect')
yk = gaussian_filter1d(y, sigma=sigma, mode='reflect')
# normalize and random scale
xkk = (xk -xk.min())
xkk /= xkk.max()
ykk = (yk -yk.min())
ykk /= ykk.max()
#scaling_factor = np.random.randint(20,90)
scaling_factor = np.random.randint(40,180)
xkk*=scaling_factor # randomize
ykk*=scaling_factor # randomize
# random translate and clip
translation_factor_x = np.random.randint(0,w-scaling_factor)
translation_factor_y = np.random.randint(0,h-scaling_factor)
tr_x = xkk + translation_factor_x
tr_y = ykk + translation_factor_y
tr_x = np.clip(tr_x,0,w-1)
tr_y = np.clip(tr_y,0,h-1)
# sample 16 points from trajectory with linear spacing
idxs = np.round(np.linspace(0, tr_x.shape[0]-1, num=16)).astype(int)
x_f = tr_x[idxs].astype(int)
y_f = tr_y[idxs].astype(int)
#print(x_f.shape,y_f.shape)
traj = np.column_stack((x_f,y_f))
traj = np.expand_dims(traj, axis=1)
return traj
# Step 1 create a non-linear trajectory
#print(" number of rois",num_trajs,box_shapes.shape)
ctr_pts_list = []
for i in range(num_trajs):
ctr_pts_list.append(create_traj(box_shapes[i]))
ctr_pts = np.concatenate(ctr_pts_list, axis=1)
#print("all center points guassian ",ctr_pts,ctr_pts.shape)
# Step 2 create box shapes for the starting location
boxes_list = []
for i in range(num_trajs):
x1, y1 = ctr_pts[0][i][0], ctr_pts[0][i][1]
box = np.concatenate((
(x1 - box_shapes[i, 0]/2).reshape(-1, 1),
(y1 - box_shapes[i, 1]/2).reshape(-1, 1),
(x1 + box_shapes[i, 0]/2).reshape(-1, 1),
(y1 + box_shapes[i, 1]/2).reshape(-1, 1)),
axis=1)
boxes_list.append(box)
boxes= np.concatenate(boxes_list, axis=0)
box_sizes = (boxes[:, 2:4] - boxes[:, 0:2])
#print("bboxes guassian ",boxes,boxes.shape)
#print("guassian box sizes = ",box_sizes,box_sizes.shape)
# Step 3, sample key frames for shape changes
size_key_inds = sample_key_frames(num_frames, key_frame_probs)
# Step 4, setup shape in different key frames
box_sizes_list = [np.expand_dims(box_sizes, axis=0)]
for i in range(len(size_key_inds) - 1):
if size_velocity > 0:
index_diff = size_key_inds[i + 1] - size_key_inds[i]
scales = np.random.uniform(low=-size_velocity * index_diff,
high=size_velocity * index_diff,
size=box_sizes.shape)
scales = np.exp(scales)
box_sizes = box_sizes * scales
box_sizes_list.append(np.expand_dims(box_sizes, axis=0))
box_sizes = np.concatenate(box_sizes_list, axis=0)
# print("box sizes before interpolation",box_sizes)
box_sizes = extend_key_frame_to_all(box_sizes, size_key_inds, 'random')
#print("box sizes after interpolation",box_sizes)
# Step 5, construct boxes in key frames
all_boxes = np.concatenate((ctr_pts - box_sizes * 0.5,
ctr_pts + box_sizes * 0.5), axis=2)
# all_boxes[..., 0::2] = np.clip(all_boxes[..., 0::2], 0, img_wh[0])
# all_boxes[..., 1::2] = np.clip(all_boxes[..., 1::2], 0, img_wh[1])
all_boxes = all_boxes.transpose((1, 0, 2))
return all_boxes,boxes
def __call__(self,img_tuple):
#def get_transform_param(self, data: List[np.ndarray], *args, **kwargs):
""" Generate the transformation parameters.
Args:
data (List[np.ndarray]): list of image array, each element is in
a shape of [H, W, 3]
Returns:
params (dict): a dict that contains necessary transformation
params, which include:
'patches': list of image patches (np.ndarray)
'alphas': list of alpha mask, same size and shape as patches.
'traj_rois': the trajectory position, in shape of
[N_traj, N_frame, 4]
'traj_labels': whether the patches have been pasted on some
specific frames, in shape of [N_traj, N_frame]
"""
#print("with tubelets")
img_group, label = img_tuple
#print("before length data",len(img_group),img_group[0].size)
new_data = [np.array(img) for img in img_group]
#print("after length data",len(new_data),new_data[0].shape)
data_1 = new_data # Step 1, generate the trajectories.
h, w = data_1[0].shape[0:2]
#print("motion type and size_velocity", self.motion_type,self.size_velocity)
#print(" patch transformation and rotation velocity =",self.patch_transformation,self.rot_velocity)
if self.motion_type == 'linear' :
boxes = self.region_sampler.sample(data_1)
traj_rois = self.rectangle_movement(boxes, (w, h),
self.loc_velocity,
self.size_velocity,
len(data_1),
self.key_frame_probs)
# gaussian
elif self.motion_type == 'gaussian' :
box_shapes = self.region_sampler.sample_box_shapes(data_1)
traj_rois,boxes = self.gaussian_movement(box_shapes, (w, h),
self.region_sampler.num_rois,
self.size_velocity,
len(data_1),
self.key_frame_probs)
#print("gaussian rois",traj_rois.shape)
traj_rois = np.round(traj_rois).astype(int)
# traj_rois[..., 0::2] = np.clip(traj_rois[..., 0::2], 0, w)
# traj_rois[..., 1::2] = np.clip(traj_rois[..., 1::2], 0, h)
# Step 2, crop the patches and prepare the alpha masks.
if not self.use_objects:
#print(" pasting patches")
patches_list, alphas_list, label_list = self.paste_patches(data_1,traj_rois,boxes)
else:
#print(" pasting objects")
patches_list, alphas_list, label_list = self.paste_objects(data_1,traj_rois,boxes)
transforms_dict = dict(
traj_rois=traj_rois,
patches=patches_list,
alphas=alphas_list,
traj_labels=label_list,
rot_velocity = self.rot_velocity,
patch_transformation = self.patch_transformation,
key_frame_probs = self.key_frame_probs
)
output_data = self._apply_image( new_data,transforms_dict)
ret_data = [Image.fromarray(img) for img in output_data]
return ret_data, label, traj_rois
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