def process_image(self, sample_index, kpanno, sigma, rot_flag, scale_flag,
flip_flag):
imagefile = kpanno['img_paths']
# -image = scipy.misc.imread(os.path.join(self.imgpath, imagefile))
image = imageio.imread(os.path.join(self.imgpath, imagefile))
# get center
center = np.array(kpanno['objpos'])
joints = np.array(kpanno['joint_self'])
scale = kpanno['scale_provided']
# Adjust center/scale slightly to avoid cropping limbs
if center[0] != -1:
center[1] = center[1] + 15 * scale
scale = scale * 1.25
# filp
if flip_flag and random.choice([0, 1]):
image, joints, center = self.flip(image, joints, center)
# scale
if scale_flag:
scale = scale * np.random.uniform(0.8, 1.2)
# rotate image
if rot_flag and random.choice([0, 1]):
rot = np.random.randint(-1 * 30, 30)
else:
rot = 0
cropimg = data_process.crop(image, center, scale, self.inres, rot)
cropimg = data_process.normalize(cropimg, self.get_color_mean())
# transform keypoints
transformedKps = data_process.transform_kp(joints, center, scale,
self.outres, rot)
gtmap = data_process.generate_gtmap(transformedKps, sigma, self.outres)
# meta info
metainfo = {
'sample_index': sample_index,
'center': center,
'scale': scale,
'pts': joints,
'tpts': transformedKps,
'name': imagefile
}
return cropimg, gtmap, metainfo
def process_image(self, sample_index, kpanno, sigma):
imagefile = 'rgb_1_' + str(sample_index + 1).zfill(7) + '.jpg'
image = imageio.imread(os.path.join(self.imgpath, imagefile))
# norm_image = data_process.normalize(image, self.get_color_mean(image)) #UNNECESSARY
norm_image = image / 255.0
# create heatmaps
heatmaps, orig_size_map = data_process.generate_gtmap(
kpanno, sigma, self.outres)
if self.debug:
orig_image = cv2.resize(
image, dsize=(480, 480), interpolation=cv2.INTER_CUBIC) / 255.0
im = np.concatenate(
[orig_image,
np.sum(orig_size_map, axis=-1)[:, :, np.newaxis]],
axis=-1)
for i in range(kpanno.shape[0]):
x = kpanno[i, 0]
y = kpanno[i, 1]
orig_image = cv2.circle(orig_image, (int(x), int(y)), 5,
(0, 0, 255), 2)
cv2.imshow('orig {} with heatmaps GENERATOR'.format(sample_index),
orig_image)
cv2.imshow('orig {} heatmap GENERATOR'.format(sample_index),
np.sum(orig_size_map, axis=-1))
cv2.imshow('gt heatmap GENERATOR', np.sum(heatmaps, axis=-1))
cv2.waitKey(0) # FIXME
# meta info
metainfo = {
'sample_index': sample_index,
'tpts': kpanno,
'name': imagefile
}
return norm_image, heatmaps, metainfo