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 = kpanno['img_path']
image = scipy.misc.imread(os.path.join(self.imgpath, imagefile))
if image.ndim < 3:
image = cv2.cvtColor(image, cv2.COLOR_GRAY2RGB)
# get center
bb = np.array(kpanno['face'])
kp = np.array(kpanno['landmarks'])
cropimg = data_process.crop(image, bb, (224, 224))
#cropimg = data_process.normalize(cropimg, self.get_color_mean())
# transform keypoints
transformed_kp = data_process.transform_kp(image, bb, kp, (224, 224))
gtmap = data_process.generate_hm((224, 224), transformed_kp, 3)
return np.swapaxes(cropimg, 0, 2), np.swapaxes(gtmap, 0, 2)
def view_crop_image(anno):
print anno.keys()
img_paths = anno['img_paths']
img_width = anno['img_width']
img_height = anno['img_height']
#print anno.keys()
imgdata = scipy.misc.imread(
os.path.join("../../data/mpii/images", img_paths))
draw_joints(imgdata, anno['joint_self'])
#scipy.misc.imshow(imgdata)
center = np.array(anno['objpos'])
outimg = data_process.crop(imgdata,
center=center,
scale=anno['scale_provided'],
res=(256, 256),
rot=0)
outimg_normalized = data_process.normalize(outimg)
print outimg.shape
newjoints = data_process.transform_kp(np.array(anno['joint_self']),
center,
anno['scale_provided'], (64, 64),
rot=0)
#draw_joints(outimg_normalized, newjoints.tolist())
#scipy.misc.imshow(outimg_normalized)
'''
mimage = np.zeros(shape=(64, 64), dtype=np.float)
gtmap = generate_gt_map(newjoints, sigma=1, outres=(64, 64))
for i in range(16):
mimage += gtmap[:, :, i]
scipy.misc.imshow(mimage)
'''
# meta info
metainfo = []
orgjoints = np.array(anno['joint_self'])
for i in range(newjoints.shape[0]):
meta = {
'center': center,
'scale': anno['scale_provided'],
'pts': orgjoints[i],
'tpts': newjoints[i]
}
metainfo.append(meta)
# transform back
tpbpts = list()
for i in range(newjoints.shape[0]):
tpts = newjoints[i]
meta = metainfo[i]
orgpts = tpts
orgpts[0:2] = data_process.transform(tpts,
meta['center'],
meta['scale'],
res=[64, 64],
invert=1,
rot=0)
tpbpts.append(orgpts)
print tpbpts
draw_joints(imgdata, np.array(tpbpts))
scipy.misc.imshow(imgdata)
