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image.py
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image.py
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import numpy as np
import cv2
import random
def flip(img):
return img[:, :, ::-1].copy()
# todo what the hell is this?
def get_border(border, size):
i = 1
while size - border // i <= border // i:
i *= 2
return border // i
def transform_preds(coords, center, scale, output_size):
target_coords = np.zeros(coords.shape)
trans = get_affine_transform(center, scale, 0, output_size, inv=1)
for p in range(coords.shape[0]):
target_coords[p, 0:2] = affine_transform(coords[p, 0:2], trans)
return target_coords
def get_affine_transform(center,
scale,
rot,
output_size,
shift=np.array([0, 0], dtype=np.float32),
inv=0):
if not isinstance(scale, np.ndarray) and not isinstance(scale, list):
scale = np.array([scale, scale], dtype=np.float32)
scale_tmp = scale
src_w = scale_tmp[0]
dst_w = output_size[0]
dst_h = output_size[1]
rot_rad = np.pi * rot / 180
src_dir = get_dir([0, src_w * -0.5], rot_rad)
dst_dir = np.array([0, dst_w * -0.5], np.float32)
src = np.zeros((3, 2), dtype=np.float32)
dst = np.zeros((3, 2), dtype=np.float32)
src[0, :] = center + scale_tmp * shift
src[1, :] = center + src_dir + scale_tmp * shift
dst[0, :] = [dst_w * 0.5, dst_h * 0.5]
dst[1, :] = np.array([dst_w * 0.5, dst_h * 0.5], np.float32) + dst_dir
src[2:, :] = get_3rd_point(src[0, :], src[1, :])
dst[2:, :] = get_3rd_point(dst[0, :], dst[1, :])
if inv:
trans = cv2.getAffineTransform(np.float32(dst), np.float32(src))
else:
trans = cv2.getAffineTransform(np.float32(src), np.float32(dst))
return trans
def affine_transform(pt, t):
new_pt = np.array([pt[0], pt[1], 1.], dtype=np.float32).T
new_pt = np.dot(t, new_pt)
return new_pt[:2]
def get_3rd_point(a, b):
direct = a - b
return b + np.array([-direct[1], direct[0]], dtype=np.float32)
def get_dir(src_point, rot_rad):
_sin, _cos = np.sin(rot_rad), np.cos(rot_rad)
src_result = [0, 0]
src_result[0] = src_point[0] * _cos - src_point[1] * _sin
src_result[1] = src_point[0] * _sin + src_point[1] * _cos
return src_result
def crop(img, center, scale, output_size, rot=0):
trans = get_affine_transform(center, scale, rot, output_size)
dst_img = cv2.warpAffine(img,
trans,
(int(output_size[0]), int(output_size[1])),
flags=cv2.INTER_LINEAR)
return dst_img
# todo what's this?
def gaussian_radius(det_size, min_overlap=0.7):
height, width = det_size
a1 = 1
b1 = (height + width)
c1 = width * height * (1 - min_overlap) / (1 + min_overlap)
sq1 = np.sqrt(b1 ** 2 - 4 * a1 * c1)
r1 = (b1 + sq1) / 2
a2 = 4
b2 = 2 * (height + width)
c2 = (1 - min_overlap) * width * height
sq2 = np.sqrt(b2 ** 2 - 4 * a2 * c2)
r2 = (b2 + sq2) / 2
a3 = 4 * min_overlap
b3 = -2 * min_overlap * (height + width)
c3 = (min_overlap - 1) * width * height
sq3 = np.sqrt(b3 ** 2 - 4 * a3 * c3)
r3 = (b3 + sq3) / 2
return min(r1, r2, r3)
def gaussian2D(shape, sigma=1):
m, n = [(ss - 1.) / 2. for ss in shape]
y, x = np.ogrid[-m:m + 1, -n:n + 1]
h = np.exp(-(x * x + y * y) / (2 * sigma * sigma))
h[h < np.finfo(h.dtype).eps * h.max()] = 0
return h
def draw_umich_gaussian(heatmap, center, radius, k=1):
diameter = 2 * radius + 1
gaussian = gaussian2D((diameter, diameter), sigma=diameter / 6)
x, y = int(center[0]), int(center[1])
height, width = heatmap.shape[0:2]
left, right = min(x, radius), min(width - x, radius + 1)
top, bottom = min(y, radius), min(height - y, radius + 1)
masked_heatmap = heatmap[y - top:y + bottom, x - left:x + right]
masked_gaussian = gaussian[radius - top:radius + bottom, radius - left:radius + right]
if min(masked_gaussian.shape) > 0 and min(masked_heatmap.shape) > 0: # TODO debug
np.maximum(masked_heatmap, masked_gaussian * k, out=masked_heatmap)
return heatmap
def draw_dense_reg(regmap, heatmap, center, value, radius, is_offset=False):
diameter = 2 * radius + 1
gaussian = gaussian2D((diameter, diameter), sigma=diameter / 6)
value = np.array(value, dtype=np.float32).reshape(-1, 1, 1)
dim = value.shape[0]
reg = np.ones((dim, diameter * 2 + 1, diameter * 2 + 1), dtype=np.float32) * value
if is_offset and dim == 2:
delta = np.arange(diameter * 2 + 1) - radius
reg[0] = reg[0] - delta.reshape(1, -1)
reg[1] = reg[1] - delta.reshape(-1, 1)
x, y = int(center[0]), int(center[1])
height, width = heatmap.shape[0:2]
left, right = min(x, radius), min(width - x, radius + 1)
top, bottom = min(y, radius), min(height - y, radius + 1)
masked_heatmap = heatmap[y - top:y + bottom, x - left:x + right]
masked_regmap = regmap[:, y - top:y + bottom, x - left:x + right]
masked_gaussian = gaussian[radius - top:radius + bottom,
radius - left:radius + right]
masked_reg = reg[:, radius - top:radius + bottom,
radius - left:radius + right]
if min(masked_gaussian.shape) > 0 and min(masked_heatmap.shape) > 0: # TODO debug
idx = (masked_gaussian >= masked_heatmap).reshape(
1, masked_gaussian.shape[0], masked_gaussian.shape[1])
masked_regmap = (1 - idx) * masked_regmap + idx * masked_reg
regmap[:, y - top:y + bottom, x - left:x + right] = masked_regmap
return regmap
def draw_msra_gaussian(heatmap, center, sigma):
tmp_size = sigma * 3
mu_x = int(center[0] + 0.5)
mu_y = int(center[1] + 0.5)
w, h = heatmap.shape[0], heatmap.shape[1]
ul = [int(mu_x - tmp_size), int(mu_y - tmp_size)]
br = [int(mu_x + tmp_size + 1), int(mu_y + tmp_size + 1)]
if ul[0] >= h or ul[1] >= w or br[0] < 0 or br[1] < 0:
return heatmap
size = 2 * tmp_size + 1
x = np.arange(0, size, 1, np.float32)
y = x[:, np.newaxis]
x0 = y0 = size // 2
g = np.exp(- ((x - x0) ** 2 + (y - y0) ** 2) / (2 * sigma ** 2))
g_x = max(0, -ul[0]), min(br[0], h) - ul[0]
g_y = max(0, -ul[1]), min(br[1], w) - ul[1]
img_x = max(0, ul[0]), min(br[0], h)
img_y = max(0, ul[1]), min(br[1], w)
heatmap[img_y[0]:img_y[1], img_x[0]:img_x[1]] = np.maximum(
heatmap[img_y[0]:img_y[1], img_x[0]:img_x[1]],
g[g_y[0]:g_y[1], g_x[0]:g_x[1]])
return heatmap
def grayscale(image):
return cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
def lighting_(data_rng, image, alphastd, eigval, eigvec):
alpha = data_rng.normal(scale=alphastd, size=(3,))
image += np.dot(eigvec, eigval * alpha)
def blend_(alpha, image1, image2):
image1 *= alpha
image2 *= (1 - alpha)
image1 += image2
def saturation_(data_rng, image, gs, gs_mean, var):
alpha = 1. + data_rng.uniform(low=-var, high=var)
blend_(alpha, image, gs[:, :, None])
def brightness_(data_rng, image, gs, gs_mean, var):
alpha = 1. + data_rng.uniform(low=-var, high=var)
image *= alpha
def contrast_(data_rng, image, gs, gs_mean, var):
alpha = 1. + data_rng.uniform(low=-var, high=var)
blend_(alpha, image, gs_mean)
def color_aug(data_rng, image, eig_val, eig_vec):
functions = [brightness_, contrast_, saturation_]
random.shuffle(functions)
gs = grayscale(image)
gs_mean = gs.mean()
for f in functions:
f(data_rng, image, gs, gs_mean, 0.4)
lighting_(data_rng, image, 0.1, eig_val, eig_vec)