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Running
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Zero
File size: 8,743 Bytes
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import gradio as gr
from PIL import Image
import numpy as np
from copy import deepcopy
import cv2
import torch
from ZoeDepth.zoedepth.utils.misc import colorize
from objctrl_2_5d.utils.vis_camera import vis_camera_rescale
from objctrl_2_5d.utils.objmask_util import trajectory_to_camera_poses_v1
from objctrl_2_5d.utils.customized_cam import rotation, clockwise, pan_and_zoom
zc_threshold = 0.2
depth_scale_ = 5.2
center_margin = 10
height, width = 320, 576
num_frames = 14
intrinsics = np.array([[float(width), float(width), float(width) / 2, float(height) / 2]])
intrinsics = np.repeat(intrinsics, num_frames, axis=0) # [n_frame, 4]
fx = intrinsics[0, 0] / width
fy = intrinsics[0, 1] / height
cx = intrinsics[0, 2] / width
cy = intrinsics[0, 3] / height
def process_image(raw_image):
image, points = raw_image['image'], raw_image['points']
print(points)
try:
assert(len(points)) == 1, "Please select only one point"
[x1, y1, _, x2, y2, _] = points[0]
image = image.crop((x1, y1, x2, y2))
image = image.resize((width, height))
except:
image = image.resize((width, height))
return image, gr.update(value={'image': image})
# -------------- general UI functionality --------------
def get_subject_points(canvas):
return canvas["image"], canvas["points"]
def run_segment(segmentor):
def segment(canvas, image, logits):
if logits is not None:
logits *= 32.0
_, points = get_subject_points(canvas)
image = np.array(image)
with torch.inference_mode(), torch.autocast("cuda", dtype=torch.bfloat16):
segmentor.set_image(image)
input_points = []
input_boxes = []
for p in points:
[x1, y1, _, x2, y2, _] = p
if x2==0 and y2==0:
input_points.append([x1, y1])
else:
input_boxes.append([x1, y1, x2, y2])
if len(input_points) == 0:
input_points = None
input_labels = None
else:
input_points = np.array(input_points)
input_labels = np.ones(len(input_points))
if len(input_boxes) == 0:
input_boxes = None
else:
input_boxes = np.array(input_boxes)
masks, _, logits = segmentor.predict(
point_coords=input_points,
point_labels=input_labels,
box=input_boxes,
multimask_output=False,
return_logits=True,
mask_input=logits,
)
mask = masks > 0
masked_img = mask_image(image, mask[0], color=[252, 140, 90], alpha=0.9)
masked_img = Image.fromarray(masked_img)
return mask[0], masked_img, masked_img, logits / 32.0
return segment
def mask_image(image,
mask,
color=[255,0,0],
alpha=0.5):
""" Overlay mask on image for visualization purpose.
Args:
image (H, W, 3) or (H, W): input image
mask (H, W): mask to be overlaid
color: the color of overlaid mask
alpha: the transparency of the mask
"""
out = deepcopy(image)
img = deepcopy(image)
img[mask == 1] = color
out = cv2.addWeighted(img, alpha, out, 1-alpha, 0, out)
return out
def get_points(img,
sel_pix,
evt: gr.SelectData):
# collect the selected point
img = np.array(img)
img = deepcopy(img)
sel_pix.append(evt.index)
# only draw the last two points
# if len(sel_pix) > 2:
# sel_pix = sel_pix[-2:]
points = []
for idx, point in enumerate(sel_pix):
if idx % 2 == 0:
# draw a red circle at the handle point
cv2.circle(img, tuple(point), 10, (255, 0, 0), -1)
else:
# draw a blue circle at the handle point
cv2.circle(img, tuple(point), 10, (0, 0, 255), -1)
points.append(tuple(point))
# draw an arrow from handle point to target point
# if len(points) == idx + 1:
if idx > 0:
cv2.arrowedLine(img, points[idx-1], points[idx], (255, 255, 255), 4, tipLength=0.5)
# points = []
return img if isinstance(img, np.ndarray) else np.array(img), sel_pix
# clear all handle/target points
def undo_points(original_image):
return original_image, []
def run_depth(d_model_NK):
def get_depth(image, points):
depth = d_model_NK.infer_pil(image)
colored_depth = colorize(depth, cmap='gray_r') # [h, w, 4] 0-255
depth_img = deepcopy(colored_depth[:, :, :3])
if len(points) > 0:
for idx, point in enumerate(points):
if idx % 2 == 0:
cv2.circle(depth_img, tuple(point), 10, (255, 0, 0), -1)
else:
cv2.circle(depth_img, tuple(point), 10, (0, 0, 255), -1)
if idx > 0:
cv2.arrowedLine(depth_img, points[idx-1], points[idx], (255, 255, 255), 4, tipLength=0.5)
return depth, depth_img, colored_depth[:, :, :3]
return get_depth
def interpolate_points(points, num_points):
x = points[:, 0]
y = points[:, 1]
# Interpolating the points
t = np.linspace(0, 1, len(points))
t_new = np.linspace(0, 1, num_points)
x_new = np.interp(t_new, t, x)
y_new = np.interp(t_new, t, y)
return np.vstack((x_new, y_new)).T # []
def traj2cam(traj, depth, rescale):
traj = np.array(traj)
trajectory = interpolate_points(traj, num_frames)
center_h_margin, center_w_margin = center_margin, center_margin
depth_center = np.mean(depth[height//2-center_h_margin:height//2+center_h_margin, width//2-center_w_margin:width//2+center_w_margin])
depth_rescale = round(depth_scale_ * rescale / depth_center, 2)
r_depth = depth * depth_rescale
zc = []
for i in range(num_frames):
zc.append(r_depth[int(trajectory[i][1]), int(trajectory[i][0])])
print(f'zc: {zc}')
## norm zc
zc_norm = np.array(zc)
zc_grad = zc_norm[1:] - zc_norm[:-1]
zc_grad = np.abs(zc_grad)
zc_grad = zc_grad[1:] - zc_grad[:-1]
zc_grad_std = np.std(zc_grad)
if zc_grad_std > zc_threshold:
zc = [zc[0]] * num_frames
print(f'zc_grad_std: {zc_grad_std}, zc_threshold: {zc_threshold}')
print(f'zc: {zc}')
traj_w2c = trajectory_to_camera_poses_v1(trajectory, intrinsics, num_frames, zc=zc) # numpy: [n_frame, 4, 4]
RTs = traj_w2c[:, :3]
fig = vis_camera_rescale(RTs)
return RTs, fig
def get_rotate_cam(angle, depth):
# mean_depth = np.mean(depth * mask)
center_h_margin, center_w_margin = center_margin, center_margin
depth_center = np.mean(depth[height//2-center_h_margin:height//2+center_h_margin, width//2-center_w_margin:width//2+center_w_margin])
print(f'rotate depth_center: {depth_center}')
RTs = rotation(num_frames, angle, depth_center, depth_center)
fig = vis_camera_rescale(RTs)
return RTs, fig
def get_clockwise_cam(angle, depth, mask):
mask = mask.astype(np.float32) # [0, 1]
mean_depth = np.mean(depth * mask)
# center_h_margin, center_w_margin = center_margin, center_margin
# depth_center = np.mean(depth[height//2-center_h_margin:height//2+center_h_margin, width//2-center_w_margin:width//2+center_w_margin])
RTs = clockwise(angle, num_frames)
RTs[:, -1, -1] = mean_depth
fig = vis_camera_rescale(RTs)
return RTs, fig
def get_translate_cam(Tx, Ty, Tz, depth, mask, speed):
mask = mask.astype(np.float32) # [0, 1]
mean_depth = np.mean(depth * mask)
T = np.array([Tx, Ty, Tz])
T = T.reshape(3, 1)
T = T[None, ...].repeat(num_frames, axis=0)
RTs = pan_and_zoom(T, speed, n=num_frames)
RTs[:, -1, -1] += mean_depth
fig = vis_camera_rescale(RTs)
return RTs, fig
def get_camera_pose(camera_mode):
def trigger_camera_pose(camera_option, selected_points, depth, mask, rescale, angle, Tx, Ty, Tz, speed):
if camera_option == camera_mode[0]: # traj2cam
return traj2cam(selected_points, depth, rescale)
elif camera_option == camera_mode[1]: # rotate
return get_rotate_cam(angle, depth)
elif camera_option == camera_mode[2]: # clockwise
return get_clockwise_cam(angle, depth, mask)
elif camera_option == camera_mode[3]: # translate
return get_translate_cam(Tx, Ty, Tz, depth, mask, speed)
return trigger_camera_pose
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