Spaces:
Sleeping
Sleeping
File size: 4,015 Bytes
c87d1bc |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 |
from __future__ import absolute_import
from __future__ import print_function
from __future__ import division
import os
import os.path as osp
from collections import OrderedDict
import cv2
import numpy as np
from skimage.filters import gaussian
def get_transform(center, scale, res, rot=0):
"""Generate transformation matrix."""
# res: (height, width), (rows, cols)
crop_aspect_ratio = res[0] / float(res[1])
h = 200 * scale
w = h / crop_aspect_ratio
t = np.zeros((3, 3))
t[0, 0] = float(res[1]) / w
t[1, 1] = float(res[0]) / h
t[0, 2] = res[1] * (-float(center[0]) / w + .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 np.array([round(new_pt[0]), round(new_pt[1])], dtype=int) + 1
def crop(img, center, scale, res):
"""
Crop image according to the supplied bounding box.
res: [rows, cols]
"""
# Upper left point
ul = np.array(transform([1, 1], center, scale, res, invert=1)) - 1
# Bottom right point
br = np.array(transform([res[1] + 1, res[0] + 1], center, scale, res, invert=1)) - 1
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, dtype=np.float32)
# 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])
try:
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]]
except Exception as e:
print(e)
new_img = cv2.resize(new_img, (res[1], res[0])) # (cols, rows)
return new_img, ul, br
def process_image(orig_img_rgb, center, scale, crop_height=256, crop_width=192, blur=False, do_crop=True):
"""
Read image, do preprocessing and possibly crop it according to the bounding box.
If there are bounding box annotations, use them to crop the image.
If no bounding box is specified but openpose detections are available, use them to get the bounding box.
"""
if blur:
# Blur image to avoid aliasing artifacts
downsampling_factor = ((scale * 200 * 1.0) / crop_height)
downsampling_factor = downsampling_factor / 2.0
if downsampling_factor > 1.1:
orig_img_rgb = gaussian(orig_img_rgb, sigma=(downsampling_factor-1)/2, channel_axis=2, preserve_range=True)
IMG_NORM_MEAN = [0.485, 0.456, 0.406]
IMG_NORM_STD = [0.229, 0.224, 0.225]
if do_crop:
img, ul, br = crop(orig_img_rgb, center, scale, (crop_height, crop_width))
else:
img = orig_img_rgb.copy()
crop_img = img.copy()
img = img / 255.
mean = np.array(IMG_NORM_MEAN, dtype=np.float32)
std = np.array(IMG_NORM_STD, dtype=np.float32)
norm_img = (img - mean) / std
norm_img = np.transpose(norm_img, (2, 0, 1))
return norm_img, crop_img |