def main_inference(model_json, model_weights, num_stack, num_class, imgfile, confth, tiny):
if tiny:
xnet = HourglassNet(num_classes=16, num_stacks=args.num_stack, num_channels=128, inres=(192, 192),
outres=(48, 48))
else:
xnet = HourglassNet(num_classes=16, num_stacks=args.num_stack, num_channels=256, inres=(256, 256),
outres=(64, 64))
xnet.load_model(model_json, model_weights)
out, scale = xnet.inference_file(imgfile)
kps = post_process_heatmap(out[0, :, :, :])
ignore_kps = ['plevis', 'thorax', 'head_top']
kp_keys = MPIIDataGen.get_kp_keys()
mkps = list()
for i, _kp in enumerate(kps):
if kp_keys[i] in ignore_kps:
_conf = 0.0
else:
_conf = _kp[2]
mkps.append((_kp[0] * scale[1] * 4, _kp[1] * scale[0] * 4, _conf))
cvmat = render_joints(cv2.imread(imgfile), mkps, confth)
cv2.imshow('frame', cvmat)
cv2.waitKey()
def main_eval(model_json, model_weights, num_stack, num_class, matfile):
xnet = HourglassNet(num_class, num_stack, (256, 256), (64, 64))
xnet.load_model(model_json, model_weights)
valdata = MPIIDataGen("../../data/mpii/mpii_annotations.json", "../../data/mpii/images",
inres=(256, 256), outres=(64, 64), is_train=False)
print 'val data size', valdata.get_dataset_size()
valkps = np.zeros(shape=(valdata.get_dataset_size(), 16, 2), dtype=np.float)
count = 0
batch_size = 8
for _img, _gthmap, _meta in valdata.generator(batch_size, num_stack, sigma=1, is_shuffle=False , with_meta=True):
count += batch_size
if count > valdata.get_dataset_size():
break
out = xnet.model.predict(_img)
get_final_pred_kps(valkps, out[-1], _meta)
scipy.io.savemat(matfile, mdict={'preds' : valkps})
run_pckh(model_json, matfile)
def main_test():
xnet = HourglassNet(16, 8, (256, 256), (64, 64))
xnet.load_model("../../trained_models/hg_s8_b1_sigma1/net_arch.json", "../../trained_models/hg_s8_b1_sigma1/weights_epoch22.h5")
valdata = MPIIDataGen("../../data/mpii/mpii_annotations.json", "../../data/mpii/images",
inres=(256, 256), outres=(64, 64), is_train=False)
total_good, total_fail = 0, 0
threshold = 0.5
print('val data size', valdata.get_dataset_size())
count = 0
batch_size = 8
for _img, _gthmap, _meta in valdata.tt_generator(batch_size, 8, sigma=2, is_shuffle=False , with_meta=True):
count += batch_size
if count % (batch_size*100) == 0:
print(count, 'processed', total_good, total_fail)
if count > valdata.get_dataset_size():
break
out = xnet.model.predict(_img)
good, bad = cal_heatmap_acc(out[-1], _meta, threshold)
total_good += good
total_fail += bad
print(total_good, total_fail, threshold, total_good*1.0/(total_good + total_fail))
def main_test():
xnet = HourglassNet(16, 8, (256, 256), (64, 64))
xnet.load_model("../../trained_models/hg_s8_b1_v1_adam/net_arch.json",
"../../trained_models/hg_s8_b1_v1_adam/weights_epoch22.h5")
valdata = MPIIDataGen("../../data/mpii/mpii_annotations.json", "../../data/mpii/images",
inres=(256, 256), outres=(64, 64), is_train=False)
print 'val data size', valdata.get_dataset_size()
valkps = np.zeros(shape=(valdata.get_dataset_size(), 16, 2), dtype=np.float)
count = 0
batch_size = 8
for _img, _gthmap, _meta in valdata.generator(batch_size, 8, sigma=2, is_shuffle=False , with_meta=True):
count += batch_size
if count > valdata.get_dataset_size():
break
out = xnet.model.predict(_img)
get_final_pred_kps(valkps, out[-1], _meta)
matfile = os.path.join( "../../trained_models/hg_s8_b1_v1_adam/", 'preds_e22.mat')
scipy.io.savemat(matfile, mdict={'preds' : valkps})
run_pckh('hg_s8_b1_epoch22', matfile)
def main_video(model_json, model_weights, num_stack, num_class, videofile,
confth):
xnet = HourglassNet(num_class, num_stack, (256, 256), (64, 64))
xnet.load_model(model_json, model_weights)
cap = cv2.VideoCapture(videofile)
while (cap.isOpened()):
ret, frame = cap.read()
if ret:
rgb = frame[:, :, ::-1] # bgr -> rgb
out, scale = xnet.inference_rgb(rgb, frame.shape)
kps = post_process_heatmap(out[0, :, :, :])
ignore_kps = ['plevis', 'thorax', 'head_top']
kp_keys = MPIIDataGen.get_kp_keys()
mkps = list()
for i, _kp in enumerate(kps):
if kp_keys[i] in ignore_kps:
_conf = 0.0
else:
_conf = _kp[2]
mkps.append(
(_kp[0] * scale[1] * 4, _kp[1] * scale[0] * 4, _conf))
framejoints = render_joints(frame, mkps, confth)
cv2.imshow('frame', framejoints)
cv2.waitKey(10)
def main_eval(model_json, model_weights, num_stack, num_class, matfile, tiny):
inres = (192, 192) if tiny else (256, 256)
outres = (48, 48) if tiny else (64, 64)
num_channles = 128 if tiny else 256
xnet = HourglassNet(num_classes=num_class,
num_stacks=num_stack,
num_channels=num_channles,
inres=inres,
outres=outres)
xnet.load_model(model_json, model_weights)
# dataset_path = '/home/tomas_bordac/nyu_croped'
dataset_path = os.path.join('D:\\', 'nyu_croped')
valdata = NYUHandDataGen('joint_data.mat',
dataset_path,
inres=inres,
outres=outres,
is_train=False)
print('val data size', valdata.get_dataset_size())
valkps = np.zeros(shape=(valdata.get_dataset_size(), 11, 2),
dtype=np.float)
count = 0
batch_size = 8
for _img, _gthmap, _meta in valdata.generator(batch_size,
num_stack,
sigma=3,
is_shuffle=False,
with_meta=True):
count += batch_size
if count > valdata.get_dataset_size():
break
out = xnet.model.predict(_img)
get_final_pred_kps(valkps, out[-1], _meta, outres)
scipy.io.savemat(matfile, mdict={'preds': valkps})
run_pckh(model_json, matfile)
def main_test():
xnet = HourglassNet(16, 8, (256, 256), (64, 64))
xnet.load_model("../../trained_models/hg_s8_b1/net_arch.json",
"../../trained_models/hg_s8_b1/weights_epoch29.h5")
valdata = MPIIDataGen("../../data/mpii/mpii_annotations.json",
"../../data/mpii/images",
inres=(256, 256),
outres=(64, 64),
is_train=False)
for _img, _gthmap in valdata.generator(1, 8, sigma=2, is_shuffle=True):
out = xnet.model.predict(_img)
scipy.misc.imshow(_img[0, :, :, :])
#view_predict_hmap(_gthmap)
view_predict_hmap(out, show_raw=False)
def main_eval(model_path, num_stack, num_class, matfile, tiny):
inres = (192, 192) if tiny else (256, 256)
outres = (48, 48) if tiny else (64, 64)
num_channles = 128 if tiny else 256
xnet = HourglassNet(num_classes=num_class,
num_stacks=num_stack,
num_channels=num_channles,
inres=inres,
outres=outres)
xnet.load_model(model_path)
valdata = MPIIDataGen("../../data/mpii/mpii_annotations.json",
"../../data/mpii/images",
inres=inres,
outres=outres,
is_train=False)
print('val data size', valdata.get_dataset_size())
valkps = np.zeros(shape=(valdata.get_dataset_size(), 16, 2),
dtype=np.float)
count = 0
batch_size = 8
for _img, _gthmap, _meta in valdata.generator(batch_size,
num_stack,
sigma=1,
is_shuffle=False,
with_meta=True):
count += batch_size
if count > valdata.get_dataset_size():
break
out = xnet.model.predict(_img)
get_final_pred_kps(valkps, out[-1], _meta, outres)
scipy.io.savemat(matfile, mdict={'preds': valkps})
run_pckh(model_path, matfile)