def main():
# intrinsic paramters of Intel Realsense SR300
fx, fy, ux, uy = 628.668, 628.668, 311.662, 231.571
depth_scale = 0.0010000000474974513
# paramters
dataset = 'hands17'
if len(sys.argv) == 2:
dataset = sys.argv[1]
print(dataset)
lower_ = 1
upper_ = 435
# init hand pose estimation model
try:
hand_model = ModelPoseREN(
dataset,
lambda img: get_center(img, lower=lower_, upper=upper_),
param=(fx, fy, ux, uy),
use_gpu=True)
except:
print('Model not found')
# for msra dataset, use the weights for first split
if dataset == 'msra':
hand_model.reset_model(dataset, test_id=0)
# realtime hand pose estimation loop
frames = read_images(img_array, depth_scale)
# preprocessing depth
# # training samples are left hands in icvl dataset,
# # right hands in nyu dataset and msra dataset,
# # for this demo you should use your right hand
if dataset == 'icvl':
frames = frames[:, ::-1] # flip
# get hand pose
predicted = []
f = open('results.txt', 'w')
for idx, depth in enumerate(frames):
depth = frames[idx, :, :]
# depth = np.rot90(depth,2)
depth[depth == 0] = depth.max()
results, cropped_image = hand_model.detect_image(depth)
img_show = show_results(depth, results, cropped_image, dataset)
# cv2.imshow('result', img_show)
cv2.imwrite('result_{}.png'.format(idx), img_show)
f.write('image_{}.png'.format(idx))
for r in results:
for i in r:
f.write(' %s' % i)
f.write('\n')
#print(results)
predicted.append(results)
f.close()
def main():
# intrinsic paramters of Intel Realsense D415
fx, fy, ux, uy = 628.668, 628.668, 311.662, 231.571
# paramters
dataset = 'icvl'
if len(sys.argv) == 2:
dataset = sys.argv[1]
print(dataset)
lower_ = 1
upper_ = 650
# init realsense
pipeline, depth_scale = init_device()
# init hand pose estimation model
try:
hand_model = ModelPoseREN(
dataset,
lambda img: get_center(img, lower=lower_, upper=upper_),
param=(fx, fy, ux, uy),
use_gpu=True)
except:
print('Model not found')
# for msra dataset, use the weights for first split
if dataset == 'msra':
hand_model.reset_model(dataset, test_id=0)
# realtime hand pose estimation loop
while True:
depth = read_frame_from_device(pipeline, depth_scale)
# preprocessing depth
depth[depth == 0] = depth.max()
# training samples are left hands in icvl dataset,
# right hands in nyu dataset and msra dataset,
# for this demo you should use your right hand
if dataset == 'icvl':
depth = depth[:, ::-1] # flip
# get hand pose
results, cropped_image = hand_model.detect_image(depth)
img_show = show_results(depth, results, cropped_image, dataset)
cv2.imshow('result', img_show)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
stop_device(pipeline)
def main():
if len(sys.argv) < 3:
print_usage()
dataset = sys.argv[1]
out_file = sys.argv[2]
data_dir_dict = {
'nyu': config.nyu_data_dir,
'icvl': config.icvl_data_dir,
'msra': config.msra_data_dir,
'hands17': config.hands_data_dir
}
base_dir = data_dir_dict[dataset] #sys.argv[3]
batch_size = 64
if len(sys.argv) == 4:
batch_size = int(sys.argv[3])
# generate deploy prototxt
make_baseline_net(
os.path.join('/home/boonyew/Documents/PoseRen', 'models/'), dataset)
make_pose_ren_net(
os.path.join('/home/boonyew/Documents/PoseRen', 'models/'), dataset)
hand_model = ModelPoseREN(dataset)
names = util.load_names(dataset)
centers = util.load_centers(dataset)
if dataset == 'msra':
# the last index of frames belong to the same subject
msra_id_split_range = np.array(
[8499, 16991, 25403, 33891, 42391, 50888, 59385, 67883, 76375]) - 1
results = []
for test_id in range(9):
hand_model.reset_model(dataset, test_id)
sidx = msra_id_split_range[test_id - 1] + 1 if test_id else 0
eidx = msra_id_split_range[test_id]
sub_names = names[sidx:eidx + 1]
sub_centers = centers[sidx:eidx + 1]
print('evaluating for subject {} ...'.format(test_id))
sub_results = hand_model.detect_files(base_dir,
sub_names,
sub_centers,
max_batch=batch_size)
if test_id == 0:
results = sub_results
else:
results = np.concatenate((results, sub_results), axis=0)
util.save_results(results, out_file)
else:
results = hand_model.detect_files(base_dir,
names,
centers,
max_batch=batch_size)
util.save_results(results, out_file)
def main():
if len(sys.argv) < 3:
print_usage()
dataset = sys.argv[1]
out_file = sys.argv[2]
data_dir_dict = {'nyu': config.nyu_data_dir,
'icvl': config.icvl_data_dir + 'test/Depth/',
'msra': config.msra_data_dir}
base_dir = data_dir_dict[dataset] #sys.argv[3]
batch_size = 64
if len(sys.argv) == 4:
batch_size = int(sys.argv[3])
# generate deploy prototxt
make_baseline_net(os.path.join(ROOT_DIR, '../models'), dataset)
make_pose_ren_net(os.path.join(ROOT_DIR, '../models'), dataset)
hand_model = ModelPoseREN(dataset)
names = util.load_names(dataset)
centers = util.load_centers(dataset)
# print("centers = {}".format(np.shape(centers)))
# c = centers[0]
# print("centers[0] = {}".format(list(c)))
if dataset == 'msra':
# the last index of frames belong to the same subject
msra_id_split_range = np.array([8499, 16991, 25403, 33891, 42391, 50888, 59385, 67883, 76375]) - 1
results = []
for test_id in xrange(9):
hand_model.reset_model(dataset, test_id)
sidx = msra_id_split_range[test_id-1] + 1 if test_id else 0
eidx = msra_id_split_range[test_id]
sub_names = names[sidx:eidx+1]
sub_centers= centers[sidx:eidx + 1]
print('evaluating for subject {} ...'.format(test_id))
sub_results = hand_model.detect_files(base_dir, sub_names, sub_centers, max_batch=batch_size)
if test_id == 0:
results = sub_results
else:
results = np.concatenate((results, sub_results), axis=0)
util.save_results(results, out_file)
else:
results = hand_model.detect_files(base_dir, names, centers, max_batch=batch_size)
print(results[0])
img = cv2.imread("/media/reborn/Others/Study/Reborn/Github/Pose-REN/test/image_0000.png", 2).astype("float32")
img_show = show_results(img, results[0], dataset)
cv2.imwrite('result.jpg', img_show)
util.save_results(results, out_file)
def main():
# intrinsic paramters of Intel Realsense SR300
# fx, fy, ux, uy = 463.889, 463.889, 320, 240
fx, fy, ux, uy = 385.13, 385.13, 316.802, 241.818
# paramters
dataset = 'hands17'
if len(sys.argv) == 2:
dataset = sys.argv[1]
lower_ = 200
upper_ = 450
# init realsense
pipeline, depth_scale = init_device()
# init hand pose estimation model
hand_model = ModelPoseREN(
dataset,
lambda img: get_center(img, lower=lower_, upper=upper_),
param=(fx, fy, ux, uy),
use_gpu=True)
# for msra dataset, use the weights for first split
if dataset == 'msra':
hand_model.reset_model(dataset, test_id=0)
is_recording = False
is_testing = False
d, f = read_trainset()
rec = []
# realtime hand pose estimation loop
while True:
depth = read_frame_from_device(pipeline, depth_scale)
# preprocessing depth
depth[depth == 0] = depth.max()
# training samples are left hands in icvl dataset,
# right hands in nyu dataset and msra dataset,
# for this demo you should use your right hand
if dataset == 'icvl':
depth = depth[:, ::-1] # flip
# get hand pose
results, cropped_image = hand_model.detect_image(depth)
img_show = show_results(depth, results, cropped_image, dataset)
cv2.imshow('result', img_show)
if is_recording or is_testing:
rec.append(results)
readkey = cv2.waitKey(1) & 0xFF
if readkey == ord('q'):
break
elif readkey == ord('r'):
if is_testing:
print('*** cannot record ***')
if not is_recording:
print("--- is recording ---")
is_recording = True
else:
print("--- recording stopped ---")
is_recording = False
filename = input('filename: ')
np.save(filename + '.npy', rec)
rec = []
elif readkey & 0xFF == ord('t'):
if is_recording:
print('*** cannot test ***')
if not is_testing:
print('--- is testing ---')
is_testing = True
else:
is_testing = False
t_feat = get_feature(np.array(rec))
rec = []
test(t_feat, f)
stop_device(pipeline)
def main():
if len(sys.argv) < 3:
print_usage()
dataset = sys.argv[1]
out_file = sys.argv[2]
# data_dir_dict = {'nyu': config.nyu_data_dir,
# 'icvl': config.icvl_data_dir + "test/Depth",
# 'msra': config.msra_data_dir}
# base_dir = data_dir_dict[dataset] #sys.argv[3]
# name = os.path.join(base_dir, names[0])
batch_size = 64
if len(sys.argv) == 4:
batch_size = int(sys.argv[3])
# generate deploy prototxt
make_baseline_net(os.path.join(ROOT_DIR, '../models'), dataset)
make_pose_ren_net(os.path.join(ROOT_DIR, '../models'), dataset)
names = util.load_names(dataset)
# centers = util.load_centers(dataset)
centers = None
fx, fy, ux, uy = 587.270, 587.270, 326.548, 230.419
# fx, fy, ux, uy = util.get_param(dataset)
lower_ = 1
upper_ = 650
hand_model = ModelPoseREN(
dataset,
lambda img: get_center(img, lower=lower_, upper=upper_),
param=(fx, fy, ux, uy),
use_gpu=True)
if dataset == 'msra':
hand_model.reset_model(dataset, test_id=0)
base_dir = "/media/reborn/Others/Study/Reborn/Github/Pose-REN/test"
# depthName = os.path.join(base_dir, "000000_depth.bin")
depthName = os.path.join(base_dir, "0.img")
imgName = os.path.join(base_dir, "image_0000.png")
if (dataset == "msra"):
# with open(depthName,"rb") as file:
# data = np.fromfile(file, dtype=np.uint32)
# width, height, left, top, right , bottom = data[:6]
# depth = np.zeros((height, width), dtype=np.float32)
# file.seek(4*6)
# data = np.fromfile(file, dtype=np.float32)
# depth[top:bottom, left:right] = np.reshape(data, (bottom-top, right-left))
# depth[depth == 0] = 10000
# print(depth[depth < depth.max()])
# cv2.imwrite("img0.jpg",depth)
with open(depthName, "rb") as file:
data = np.fromfile(file, dtype=np.uint16)
height = 480
width = 640
# depth = np.zeros((height, width), dtype=np.uint16)
depth = np.reshape(data, (height, width)).astype(np.float32)
min = depth.min()
max = depth.max()
print("min = {}, max = {}".format(min, max))
flag = np.logical_xor(depth <= upper_, depth >= lower_)
depth[flag] = 0
kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (7, 7))
depth = cv2.morphologyEx(depth, cv2.MORPH_CLOSE, kernel)
depth[depth == 0] = 10000
# depth[depth == 0] = depth.max()
# with open(depthName,"rb") as file:
# depth = []
# cnt = 0
# contents = iter(partial(file.read, 2), b'')
# for r in contents:
# r_int = int.from_bytes(r, byteorder='big') #将 byte转化为 int
# cnt += 1
# depth.append(r_int)
# # print("i = {} -- {}".format(cnt,r_int))
# depth = np.array(depth)
# depth = np.reshape(depth,(480,640))
# depth[depth == 0] = 10000
elif (dataset == "icvl"):
depth = cv2.imread(imgName, 2)
depth[depth == 0] = depth.max() # invalid pixel
depth = depth.astype(float)
# depth = np.reshape(depth,(240,320))
# depth[depth == 0] = depth.max()
print("np.shape(depth) = {}".format(np.shape(depth)))
# depth = depth[:, ::-1]
# print("names = {}".format(imgName))
# print("np.shape(img) = {}".format(np.shape(img)))
# results = hand_model.detect_files(base_dir, names, centers, max_batch=batch_size)
results = hand_model.detect_image(depth)
print("results = {}".format(results))
print(np.shape(results))
img_show = show_results(depth, results, dataset)
cv2.imwrite('result.jpg', img_show)
cv2.waitKey()