def show_image_with_boxes(img,
objects_res,
object_gt,
calib,
save_path,
height_threshold=0):
img2 = np.copy(img)
for obj in objects_res:
box3d_pts_2d, _ = compute_box_3d(obj, calib.P)
color_tmp = tuple(
[int(tmp * 255) for tmp in colors[obj.id % max_color]])
img2 = draw_projected_box3d(img2, box3d_pts_2d, color=color_tmp)
text = 'ID: %d' % obj.id
if box3d_pts_2d is not None:
img2 = cv2.putText(
img2,
text,
(int(box3d_pts_2d[4, 0]), int(box3d_pts_2d[4, 1]) - 8),
cv2.FONT_HERSHEY_TRIPLEX,
0.5,
color=color_tmp)
img = Image.fromarray(img2)
img = img.resize((width, height))
img.save(save_path)
def show_image_with_boxes(self,
img,
objects_res,
calib,
save_path,
height_threshold=0):
img2 = np.copy(img)
for obj in objects_res:
#box3d_pts_2d = compute_box_3d(obj, calib.P, calib.V2C)
box3d_pts_2d = compute_box_3d(obj, calib.P, calib.V2C)
color_tmp = tuple(
[int(tmp * 255) for tmp in colors[obj.id % max_color]])
#print("box3d_pts_2d", box3d_pts_2d)
img2 = draw_projected_box3d(img2, box3d_pts_2d, color=color_tmp)
text = 'ID: %d' % obj.id # + ', s: %.2f' % obj.score
if box3d_pts_2d is not None:
img2 = cv2.putText(
img2,
text,
(int(box3d_pts_2d[4, 0]), int(box3d_pts_2d[4, 1]) - 8),
cv2.FONT_HERSHEY_TRIPLEX,
0.5,
color=color_tmp)
#img2 = cv2.cvtColor(img2, cv2.COLOR_BGR2RGB)
#cv2.imwrite(save_path, img2)
self.image_pub.publish(self.bridge.cv2_to_imgmsg(img2, "rgb8"))
filecontent = np.array([f.split() for f in open(file_path, 'r')])
alllabels = np.unique(filecontent[:, 2])
labels = ['Car', 'Pedestrian', 'Cyclist']
finalset = [x for x in alllabels if x not in labels]
for val in finalset:
filecontent = filecontent[filecontent[:, 2] != val, :]
data = (Object3d(getstringfromarray(line[2:]))
for line in filecontent[filecontent[:, 0] == '0', :])
calib = Calibration('/home/eshan/Pictures/calib.txt')
velo = load_velo_scan('/home/eshan/Pictures/000009.bin',
np.float32,
n_vec=4)[:, 0:4]
img2 = np.copy(img)
show_lidar_with_boxes(velo, data, calib)
for obj in data:
print("here")
box3d_pts_2d, _ = compute_box_3d(obj, calib.P)
img2 = draw_projected_box3d(img2, box3d_pts_2d, color=(0, 0, 0))
text = 'gt ID: %d, Type: %s' % (0, obj.type)
if box3d_pts_2d is not None:
print("also")
img2 = cv2.putText(
img2,
text, (int(box3d_pts_2d[4, 0]), int(box3d_pts_2d[4, 1]) - 8),
cv2.FONT_HERSHEY_TRIPLEX,
0.5,
color=(0, 0, 0))
# img = Image.fromarray(img2)
# img = img.resize((width, height))
cv2.imshow("image", img2)
cv2.waitKey()
def show_image_with_boxes(img,
velo,
objects_res,
objects_res_det,
objects_res_raw,
labeldata,
object_gt,
calib,
save_path,
height_threshold=0,
show_lidar=True,
save_image=False):
img2 = np.copy(img)
for obj in objects_res:
box3d_pts_2d, _ = compute_box_3d(obj, calib.P)
color_tmp = tuple(
[int(tmp * 255) for tmp in colors[obj.id % max_color]])
img2 = draw_projected_box3d(img2, box3d_pts_2d, color=(0, 0, 255))
text = 'Tracked ID: %d, Type: %s' % (obj.id, obj.type)
if box3d_pts_2d is not None:
img2 = cv2.putText(
img2,
text,
(int(box3d_pts_2d[4, 0]), int(box3d_pts_2d[4, 1]) - 8),
cv2.FONT_HERSHEY_TRIPLEX,
0.5,
color=(0, 0, 255))
for obj in objects_res_det:
box3d_pts_2d, _ = compute_box_3d(obj, calib.P)
color_tmp = tuple(
[int(tmp * 255) for tmp in colors[obj.id % max_color]])
img2 = draw_projected_box3d(img2, box3d_pts_2d, color=(0, 255, 0))
text = 'Detection ID: %d, Type: %s' % (obj.id, obj.type)
if box3d_pts_2d is not None:
img2 = cv2.putText(
img2,
text,
(int(box3d_pts_2d[3, 0]), int(box3d_pts_2d[3, 1]) - 8),
cv2.FONT_HERSHEY_TRIPLEX,
0.5,
color=(0, 255, 0))
import itertools
labeldata, labeldata2 = itertools.tee(labeldata)
for obj in labeldata:
# print("here")
box3d_pts_2d, _ = compute_box_3d(obj, calib.P)
img2 = draw_projected_box3d(img2, box3d_pts_2d, color=(255, 0, 0))
text = 'GT, Type: %s' % (obj.type)
if box3d_pts_2d is not None:
# print("also")
print(text)
img2 = cv2.putText(
img2,
text,
(int(box3d_pts_2d[4, 0]), int(box3d_pts_2d[4, 1]) - 8),
cv2.FONT_HERSHEY_TRIPLEX,
0.5,
color=(255, 0, 0))
# for obj in objects_res_raw:
# box3d_pts_2d, _ = compute_box_3d(obj, calib.P)
# color_tmp = tuple([int(tmp * 255)
# for tmp in colors[obj.id % max_color]])
# img2 = draw_projected_box3d(img2, box3d_pts_2d, color=(255,0,0))
# text = 'Estimate ID: %d' % obj.id
# if box3d_pts_2d is not None:
# img2 = cv2.putText(img2, text, (int(box3d_pts_2d[2, 0]), int(
# box3d_pts_2d[2, 1]) - 8), cv2.FONT_HERSHEY_TRIPLEX, 0.5, color=(255,0,0))
if show_lidar:
show_lidar_with_boxes(velo,
objects=labeldata2,
calib=calib,
objects_pred=objects_res)
img = Image.fromarray(img2)
img = img.resize((width, height))
cv2.imshow("Image", img2)
cv2.waitKey()
if save_image:
print("Saving Image at", save_path)
img.save(save_path)
return img2