def get_idx_hands_up():
from src.pose_augment import set_network_input_wh
set_network_input_wh(368, 368)
show_sample = True
db = CocoPoseLMDB('/data/public/rw/coco-pose-estimation-lmdb/', is_train=True, decode_img=show_sample)
db.reset_state()
total_cnt = 0
handup_cnt = 0
for idx, metas in enumerate(db.get_data()):
meta = metas[0]
if len(meta.joint_list) <= 0:
continue
body = meta.joint_list[0]
if body[CocoPart.Neck.value][1] <= 0:
continue
if body[CocoPart.LWrist.value][1] <= 0:
continue
if body[CocoPart.RWrist.value][1] <= 0:
continue
if body[CocoPart.Neck.value][1] > body[CocoPart.LWrist.value][1] or body[CocoPart.Neck.value][1] > body[CocoPart.RWrist.value][1]:
print(meta.idx)
handup_cnt += 1
if show_sample:
l1, l2, l3 = pose_to_img(metas)
CocoPose.display_image(l1, l2, l3)
total_cnt += 1
print('%d / %d' % (handup_cnt, total_cnt))
def get_idx_hands_up():
from src.pose_augment import set_network_input_wh
set_network_input_wh(368, 368)
show_sample = True
db = CocoPoseLMDB('/data/public/rw/coco-pose-estimation-lmdb/', is_train=True, decode_img=show_sample)
db.reset_state()
total_cnt = 0
handup_cnt = 0
for idx, metas in enumerate(db.get_data()):
meta = metas[0]
if len(meta.joint_list) <= 0:
continue
body = meta.joint_list[0]
if body[CocoPart.Neck.value][1] <= 0:
continue
if body[CocoPart.LWrist.value][1] <= 0:
continue
if body[CocoPart.RWrist.value][1] <= 0:
continue
if body[CocoPart.Neck.value][1] > body[CocoPart.LWrist.value][1] or body[CocoPart.Neck.value][1] > body[CocoPart.RWrist.value][1]:
print(meta.idx)
handup_cnt += 1
if show_sample:
l1, l2, l3 = pose_to_img(metas)
CocoPose.display_image(l1, l2, l3)
total_cnt += 1
print('%d / %d' % (handup_cnt, total_cnt))
average_loss_ll_heat += lss_ll_heat * len(images_test)
total_cnt += len(images_test)
logger.info('validation(%d) %s loss=%f, loss_ll=%f, loss_ll_paf=%f, loss_ll_heat=%f' % (total_cnt, args.tag, average_loss / total_cnt, average_loss_ll / total_cnt, average_loss_ll_paf / total_cnt, average_loss_ll_heat / total_cnt))
last_gs_num2 = gs_num
sample_image = [enqueuer.last_dp[0][i] for i in range(4)]
outputMat = sess.run(
outputs,
feed_dict={q_inp: np.array((sample_image + val_image) * max(1, (args.batchsize // 16)))}
)
pafMat, heatMat = outputMat[:, :, :, 19:], outputMat[:, :, :, :19]
sample_results = []
for i in range(len(sample_image)):
test_result = CocoPose.display_image(sample_image[i], heatMat[i], pafMat[i], as_numpy=True)
test_result = cv2.resize(test_result, (640, 640))
test_result = test_result.reshape([640, 640, 3]).astype(float)
sample_results.append(test_result)
test_results = []
for i in range(len(val_image)):
test_result = CocoPose.display_image(val_image[i], heatMat[len(sample_image) + i], pafMat[len(sample_image) + i], as_numpy=True)
test_result = cv2.resize(test_result, (640, 640))
test_result = test_result.reshape([640, 640, 3]).astype(float)
test_results.append(test_result)
# save summary
summary = sess.run(merged_validate_op, feed_dict={
valid_loss: average_loss / total_cnt,
valid_loss_ll: average_loss_ll / total_cnt,
### Change to variable batch size
# only use 16 image batches
sample_image = [enqueuer.last_dp[0][i] for i in range(4)]
test_image = np.array((sample_image + val_image) * args.gpus)
outputMat = sess.run(outputs,
feed_dict={input_node: test_image})
pafMat, heatMat = outputMat[:, :, :,
19:], outputMat[:, :, :, :19]
#TODO not right size
#take the min length batchsize vs sample images
## solved with var batchsize
test_results = []
for image, heat, paf in zip(test_image, heatMat, pafMat):
test_result = CocoPose.display_image(image,
heat,
paf,
as_numpy=True)
test_result = cv2.resize(test_result, (640, 640))
test_result = test_result.reshape([640, 640,
3]).astype(float)
test_results.append(test_result)
train_results = test_results[:4]
val_results = test_results[4:16]
# save summary
#TODO not right size
## solved var batchsize
summary = sess.run(merged_validate_op,
feed_dict={
valid_loss: average_loss / total_cnt,
def sample_augmentations():
ds = CocoPose('/data/public/rw/coco-pose-estimation-lmdb/', is_train=False, only_idx=0)
ds = MapDataComponent(ds, pose_random_scale)
ds = MapDataComponent(ds, pose_rotation)
ds = MapDataComponent(ds, pose_flip)
ds = MapDataComponent(ds, pose_resize_shortestedge_random)
ds = MapDataComponent(ds, pose_crop_random)
ds = MapData(ds, pose_to_img)
augs = [
imgaug.RandomApplyAug(imgaug.RandomChooseAug([
imgaug.GaussianBlur(3),
imgaug.SaltPepperNoise(white_prob=0.01, black_prob=0.01),
imgaug.RandomOrderAug([
imgaug.BrightnessScale((0.8, 1.2), clip=False),
imgaug.Contrast((0.8, 1.2), clip=False),
# imgaug.Saturation(0.4, rgb=True),
]),
]), 0.7),
]
ds = AugmentImageComponent(ds, augs)
ds.reset_state()
for l1, l2, l3 in ds.get_data():
CocoPose.display_image(l1, l2, l3)
format='[lmdb_dataset] %(asctime)s %(levelname)s %(message)s')
if __name__ == '__main__':
"""
Speed Test for Getting Input batches from other nodes
"""
parser = argparse.ArgumentParser(
description='Worker for preparing input batches.')
parser.add_argument('--listen', type=str, default='tcp://0.0.0.0:1027')
parser.add_argument('--show', type=bool, default=False)
args = parser.parse_args()
df = RemoteDataZMQ(args.listen)
logging.info('tcp queue start')
df.reset_state()
t = time.time()
for i, dp in enumerate(df.get_data()):
if i == 100:
break
logging.info('Input batch %d received.' % i)
if i == 0:
for d in dp:
logging.info('%d dp shape={}'.format(d.shape))
if args.show:
CocoPose.display_image(dp[0][0], dp[1][0], dp[2][0])
logging.info('Speed Test Done for 100 Batches in %f seconds.' %
(time.time() - t))
logging.info('inference- elapsed_time={}'.format(time.time() - a))
avg += time.time() - a
logging.info('prediction avg= %f' % (avg / 10))
logging.info('pose+')
a = time.time()
humans = PoseEstimator.estimate(heatMat, pafMat)
logging.info('pose- elapsed_time={}'.format(time.time() - a))
for human in humans:
res = write_coco_json(human, args.input_width, args.input_height)
print(res)
logging.info('image={} heatMap={} pafMat={}'.format(
image.shape, heatMat.shape, pafMat.shape))
process_img = CocoPose.display_image(image,
heatMat,
pafMat,
as_numpy=True)
# display
image = cv2.imread(args.imgpath)
image_h, image_w = image.shape[:2]
image = TfPoseEstimator.draw_humans(image, humans)
scale = 480.0 / image_h
newh, neww = 480, int(scale * image_w + 0.5)
image = cv2.resize(image, (neww, newh), interpolation=cv2.INTER_AREA)
convas = np.zeros([480, 640 + neww, 3], dtype=np.uint8)
convas[:, :640] = process_img
convas[:, 640:] = image
def sample_augmentations():
ds = CocoPose('/data/public/rw/coco-pose-estimation-lmdb/',
is_train=False,
only_idx=0)
ds = MapDataComponent(ds, pose_random_scale)
ds = MapDataComponent(ds, pose_rotation)
ds = MapDataComponent(ds, pose_flip)
ds = MapDataComponent(ds, pose_resize_shortestedge_random)
ds = MapDataComponent(ds, pose_crop_random)
ds = MapData(ds, pose_to_img)
augs = [
imgaug.RandomApplyAug(
imgaug.RandomChooseAug([
imgaug.GaussianBlur(3),
imgaug.SaltPepperNoise(white_prob=0.01, black_prob=0.01),
imgaug.RandomOrderAug([
imgaug.BrightnessScale((0.8, 1.2), clip=False),
imgaug.Contrast((0.8, 1.2), clip=False),
# imgaug.Saturation(0.4, rgb=True),
]),
]),
0.7),
]
ds = AugmentImageComponent(ds, augs)
ds.reset_state()
for l1, l2, l3 in ds.get_data():
CocoPose.display_image(l1, l2, l3)
from pose_dataset import CocoPose
logging.basicConfig(level=logging.DEBUG, format='[lmdb_dataset] %(asctime)s %(levelname)s %(message)s')
if __name__ == '__main__':
"""
Speed Test for Getting Input batches from other nodes
"""
parser = argparse.ArgumentParser(description='Worker for preparing input batches.')
parser.add_argument('--listen', type=str, default='tcp://0.0.0.0:1027')
parser.add_argument('--show', type=bool, default=False)
args = parser.parse_args()
df = RemoteDataZMQ(args.listen)
logging.info('tcp queue start')
df.reset_state()
t = time.time()
for i, dp in enumerate(df.get_data()):
if i == 100:
break
logging.info('Input batch %d received.' % i)
if i == 0:
for d in dp:
logging.info('%d dp shape={}'.format(d.shape))
if args.show:
CocoPose.display_image(dp[0][0], dp[1][0], dp[2][0])
logging.info('Speed Test Done for 100 Batches in %f seconds.' % (time.time() - t))
average_loss_ll_heat += lss_ll_heat * len(images_test)
total_cnt += len(images_test)
logger.info('validation(%d) %s loss=%f, loss_ll=%f, loss_ll_paf=%f, loss_ll_heat=%f' % (total_cnt, training_name, average_loss / total_cnt, average_loss_ll / total_cnt, average_loss_ll_paf / total_cnt, average_loss_ll_heat / total_cnt))
last_gs_num2 = gs_num
sample_image = [enqueuer.last_dp[0][i] for i in range(4)]
outputMat = sess.run(
outputs,
feed_dict={q_inp: np.array((sample_image + val_image)*(args.batchsize // 16))}
)
pafMat, heatMat = outputMat[:, :, :, 19:], outputMat[:, :, :, :19]
sample_results = []
for i in range(len(sample_image)):
test_result = CocoPose.display_image(sample_image[i], heatMat[i], pafMat[i], as_numpy=True)
test_result = cv2.resize(test_result, (640, 640))
test_result = test_result.reshape([640, 640, 3]).astype(float)
sample_results.append(test_result)
test_results = []
for i in range(len(val_image)):
test_result = CocoPose.display_image(val_image[i], heatMat[len(sample_image) + i], pafMat[len(sample_image) + i], as_numpy=True)
test_result = cv2.resize(test_result, (640, 640))
test_result = test_result.reshape([640, 640, 3]).astype(float)
test_results.append(test_result)
# save summary
summary = sess.run(merged_validate_op, feed_dict={
valid_loss: average_loss / total_cnt,
valid_loss_ll: average_loss_ll / total_cnt,