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transforms.py
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transforms.py
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from __future__ import absolute_import
import os
import numpy as np
import scipy.misc
import matplotlib.pyplot as plt
import torch
from .misc import *
from .imutils import *
def color_normalize(x, mean, std):
if x.size(0) == 1:
x = x.repeat(3, 1, 1)
for t, m, s in zip(x, mean, std):
t.sub_(m)
return x
def flip_back(flip_output, dataset='mpii'):
"""
flip output map
"""
if dataset == 'mpii':
matchedParts = (
[0,5], [1,4], [2,3],
[10,15], [11,14], [12,13]
)
else:
print('Not supported dataset: ' + dataset)
# flip output horizontally
flip_output = fliplr(flip_output.numpy())
# Change left-right parts
for pair in matchedParts:
tmp = np.copy(flip_output[:, pair[0], :, :])
flip_output[:, pair[0], :, :] = flip_output[:, pair[1], :, :]
flip_output[:, pair[1], :, :] = tmp
return torch.from_numpy(flip_output).float()
def shufflelr(x, width, dataset='mpii'):
"""
flip coords
"""
if dataset == 'mpii':
matchedParts = (
[0,5], [1,4], [2,3],
[10,15], [11,14], [12,13]
)
else:
print('Not supported dataset: ' + dataset)
# Flip horizontal
x[:, 0] = width - x[:, 0]
# Change left-right parts
for pair in matchedParts:
tmp = x[pair[0], :].clone()
x[pair[0], :] = x[pair[1], :]
x[pair[1], :] = tmp
return x
def fliplr(x):
if x.ndim == 3:
x = np.transpose(np.fliplr(np.transpose(x, (0, 2, 1))), (0, 2, 1))
elif x.ndim == 4:
for i in range(x.shape[0]):
x[i] = np.transpose(np.fliplr(np.transpose(x[i], (0, 2, 1))), (0, 2, 1))
return x.astype(float)
def get_transform(center, scale, res, rot=0):
"""
General image processing functions
"""
# 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)
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 transform_preds(coords, center, scale, res):
# size = coords.size()
# coords = coords.view(-1, coords.size(-1))
# print(coords.size())
for p in range(coords.size(0)):
coords[p, 0:2] = to_torch(transform(coords[p, 0:2], center, scale, res, 1, 0))
return coords
def crop(img, center, scale, res, rot=0):
img = im_to_numpy(img)
# Upper left point
ul = np.array(transform([0, 0], center, scale, res, invert=1))
# Bottom right point
br = np.array(transform(res, center, scale, res, invert=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 = new_img[pad:-pad, pad:-pad]
new_img = im_to_torch(scipy.misc.imresize(new_img, res))
return new_img