def __call__(self, img_path):
kps = get_people(img_path)
input_img, proc_param, img = preprocess_image(img_path, kps)
# Add batch dimension: 1 x D x D x 3
input_img = np.expand_dims(input_img, 0)
joints, verts, cams, joints3d, theta = self._model.predict(
input_img, get_theta=True)
# theta SMPL angles
return self.kinematicTree(theta[0])
def main(img_path):
sess = tf.Session()
model = RunModel(config, sess=sess)
kps = get_people(img_path)
input_img, proc_param, img = preprocess_image(img_path, kps)
# Add batch dimension: 1 x D x D x 3
input_img = np.expand_dims(input_img, 0)
joints, verts, cams, joints3d, theta = model.predict(input_img,
get_theta=True)
print(joints3d.shape)
p3d(joints3d, cams[0], proc_param)
def locate_person_and_crop(self, img_path):
kps = get_people(img_path)
img = io.imread(img_path)
if img.shape[2] == 4:
img = img[:, :, :3]
scale, center = op_util.get_bbox_dict(kps)
crop, proc_param = img_util.scale_and_crop(img, scale, center,
224)
# Normalize image to [-1, 1]
crop = 2 * ((crop / 255.) - 0.5)
# Add batch dimension: 1 x D x D x 3
crop = np.expand_dims(crop, 0)
return crop, proc_param, img
def main(img_path):
sess = tf.Session()
model = RunModel(config, sess=sess)
kps = get_people(img_path)
input_img, proc_param, img = preprocess_image(img_path, kps)
# Add batch dimension: 1 x D x D x 3
input_img = np.expand_dims(input_img, 0)
joints, verts, cams, joints3d, theta = model.predict(input_img,
get_theta=True)
theta = theta[0, 3:75]
q = quaternion.from_rotation_vector(theta[3:6])
print(q)
q = quaternion.from_rotation_vector(theta[6:9])
print(q)
get_angles(joints3d, cams[0], proc_param)
def main(img_dir):
sess = tf.Session()
model = RunModel(config, sess=sess)
print(img_dir)
onlyfiles = [
f for f in os.listdir(img_dir)
if os.path.isfile(os.path.join(img_dir, f))
]
for file in onlyfiles:
img_path = os.path.join(img_dir, file)
kps = get_people(img_path)
input_img, proc_param, img = preprocess_image(img_path, kps)
# Add batch dimension: 1 x D x D x 3
input_img = np.expand_dims(input_img, 0)
joints, verts, cams, joints3d, theta = model.predict(input_img,
get_theta=True)
p3d(img, joints3d, joints[0], verts[0], cams[0], proc_param, file)