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"""
Image processing tools
Modified from open source projects:
(https://github.com/nkolot/GraphCMR/)
(https://github.com/open-mmlab/mmdetection)
"""
import numpy as np
import base64
import cv2
import torch
import scipy.misc
def img_from_base64(imagestring):
try:
jpgbytestring = base64.b64decode(imagestring)
nparr = np.frombuffer(jpgbytestring, np.uint8)
r = cv2.imdecode(nparr, cv2.IMREAD_COLOR)
return r
except ValueError:
return None
def myimrotate(img, angle, center=None, scale=1.0, border_value=0, auto_bound=False):
if center is not None and auto_bound:
raise ValueError('`auto_bound` conflicts with `center`')
h, w = img.shape[:2]
if center is None:
center = ((w - 1) * 0.5, (h - 1) * 0.5)
assert isinstance(center, tuple)
matrix = cv2.getRotationMatrix2D(center, angle, scale)
if auto_bound:
cos = np.abs(matrix[0, 0])
sin = np.abs(matrix[0, 1])
new_w = h * sin + w * cos
new_h = h * cos + w * sin
matrix[0, 2] += (new_w - w) * 0.5
matrix[1, 2] += (new_h - h) * 0.5
w = int(np.round(new_w))
h = int(np.round(new_h))
rotated = cv2.warpAffine(img, matrix, (w, h), borderValue=border_value)
return rotated
def myimresize(img, size, return_scale=False, interpolation='bilinear'):
h, w = img.shape[:2]
resized_img = cv2.resize(
img, (size[0],size[1]), interpolation=cv2.INTER_LINEAR)
if not return_scale:
return resized_img
else:
w_scale = size[0] / w
h_scale = size[1] / h
return resized_img, w_scale, h_scale
def get_transform(center, scale, res, rot=0):
"""Generate transformation matrix."""
h = 200 * scale
t = np.zeros((3, 3))
t[0, 0] = float(res[1]) / h
t[1, 1] = float(res[0]) / h
t[0, 2] = res[1] * (-float(center[0]) / h + .5)
t[1, 2] = res[0] * (-float(center[1]) / h + .5)
t[2, 2] = 1
if not rot == 0:
rot = -rot # To match direction of rotation from cropping
rot_mat = np.zeros((3,3))
rot_rad = rot * np.pi / 180
sn,cs = np.sin(rot_rad), np.cos(rot_rad)
rot_mat[0,:2] = [cs, -sn]
rot_mat[1,:2] = [sn, cs]
rot_mat[2,2] = 1
# Need to rotate around center
t_mat = np.eye(3)
t_mat[0,2] = -res[1]/2
t_mat[1,2] = -res[0]/2
t_inv = t_mat.copy()
t_inv[:2,2] *= -1
t = np.dot(t_inv,np.dot(rot_mat,np.dot(t_mat,t)))
return t
def transform(pt, center, scale, res, invert=0, rot=0):
"""Transform pixel location to different reference."""
t = get_transform(center, scale, res, rot=rot)
if invert:
# t = np.linalg.inv(t)
t_torch = torch.from_numpy(t)
t_torch = torch.inverse(t_torch)
t = t_torch.numpy()
new_pt = np.array([pt[0]-1, pt[1]-1, 1.]).T
new_pt = np.dot(t, new_pt)
return new_pt[:2].astype(int)+1
def crop(img, center, scale, res, rot=0):
"""Crop image according to the supplied bounding box."""
# Upper left point
ul = np.array(transform([1, 1], center, scale, res, invert=1))-1
# Bottom right point
br = np.array(transform([res[0]+1,
res[1]+1], center, scale, res, invert=1))-1
# Padding so that when rotated proper amount of context is included
pad = int(np.linalg.norm(br - ul) / 2 - float(br[1] - ul[1]) / 2)
if not rot == 0:
ul -= pad
br += pad
new_shape = [br[1] - ul[1], br[0] - ul[0]]
if len(img.shape) > 2:
new_shape += [img.shape[2]]
new_img = np.zeros(new_shape)
# Range to fill new array
new_x = max(0, -ul[0]), min(br[0], len(img[0])) - ul[0]
new_y = max(0, -ul[1]), min(br[1], len(img)) - ul[1]
# Range to sample from original image
old_x = max(0, ul[0]), min(len(img[0]), br[0])
old_y = max(0, ul[1]), min(len(img), br[1])
new_img[new_y[0]:new_y[1], new_x[0]:new_x[1]] = img[old_y[0]:old_y[1],
old_x[0]:old_x[1]]
if not rot == 0:
# Remove padding
# new_img = scipy.misc.imrotate(new_img, rot)
new_img = myimrotate(new_img, rot)
new_img = new_img[pad:-pad, pad:-pad]
# new_img = scipy.misc.imresize(new_img, res)
new_img = myimresize(new_img, [res[0], res[1]])
return new_img
def uncrop(img, center, scale, orig_shape, rot=0, is_rgb=True):
"""'Undo' the image cropping/resizing.
This function is used when evaluating mask/part segmentation.
"""
res = img.shape[:2]
# Upper left point
ul = np.array(transform([1, 1], center, scale, res, invert=1))-1
# Bottom right point
br = np.array(transform([res[0]+1,res[1]+1], center, scale, res, invert=1))-1
# size of cropped image
crop_shape = [br[1] - ul[1], br[0] - ul[0]]
new_shape = [br[1] - ul[1], br[0] - ul[0]]
if len(img.shape) > 2:
new_shape += [img.shape[2]]
new_img = np.zeros(orig_shape, dtype=np.uint8)
# Range to fill new array
new_x = max(0, -ul[0]), min(br[0], orig_shape[1]) - ul[0]
new_y = max(0, -ul[1]), min(br[1], orig_shape[0]) - ul[1]
# Range to sample from original image
old_x = max(0, ul[0]), min(orig_shape[1], br[0])
old_y = max(0, ul[1]), min(orig_shape[0], br[1])
# img = scipy.misc.imresize(img, crop_shape, interp='nearest')
img = myimresize(img, [crop_shape[0],crop_shape[1]])
new_img[old_y[0]:old_y[1], old_x[0]:old_x[1]] = img[new_y[0]:new_y[1], new_x[0]:new_x[1]]
return new_img
def rot_aa(aa, rot):
"""Rotate axis angle parameters."""
# pose parameters
R = np.array([[np.cos(np.deg2rad(-rot)), -np.sin(np.deg2rad(-rot)), 0],
[np.sin(np.deg2rad(-rot)), np.cos(np.deg2rad(-rot)), 0],
[0, 0, 1]])
# find the rotation of the body in camera frame
per_rdg, _ = cv2.Rodrigues(aa)
# apply the global rotation to the global orientation
resrot, _ = cv2.Rodrigues(np.dot(R,per_rdg))
aa = (resrot.T)[0]
return aa
def flip_img(img):
"""Flip rgb images or masks.
channels come last, e.g. (256,256,3).
"""
img = np.fliplr(img)
return img
def flip_kp(kp):
"""Flip keypoints."""
flipped_parts = [5, 4, 3, 2, 1, 0, 11, 10, 9, 8, 7, 6, 12, 13, 14, 15, 16, 17, 18, 19, 21, 20, 23, 22]
kp = kp[flipped_parts]
kp[:,0] = - kp[:,0]
return kp
def flip_pose(pose):
"""Flip pose.
The flipping is based on SMPL parameters.
"""
flippedParts = [0, 1, 2, 6, 7, 8, 3, 4, 5, 9, 10, 11, 15, 16, 17, 12, 13,
14 ,18, 19, 20, 24, 25, 26, 21, 22, 23, 27, 28, 29, 33,
34, 35, 30, 31, 32, 36, 37, 38, 42, 43, 44, 39, 40, 41,
45, 46, 47, 51, 52, 53, 48, 49, 50, 57, 58, 59, 54, 55,
56, 63, 64, 65, 60, 61, 62, 69, 70, 71, 66, 67, 68]
pose = pose[flippedParts]
# we also negate the second and the third dimension of the axis-angle
pose[1::3] = -pose[1::3]
pose[2::3] = -pose[2::3]
return pose
def flip_aa(aa):
"""Flip axis-angle representation.
We negate the second and the third dimension of the axis-angle.
"""
aa[1] = -aa[1]
aa[2] = -aa[2]
return aa |