def get_3d(self, f):
image_file = self.image_dir + f
box2d_file = self.box2d_dir + f.replace('png', 'txt')
img = cv2.imread(image_file)
dect2D_data, box2d_reserved = get_dect2D_data(box2d_file, self.classes)
for data in dect2D_data:
cls = data[0]
box_2D = np.asarray(data[1],dtype=np.float)
xmin = box_2D[0]
ymin = box_2D[1]
xmax = box_2D[2]
ymax = box_2D[3]
patch = img[int(ymin):int(ymax), int(xmin):int(xmax)]
patch = cv2.resize(patch, (224, 224))
patch = patch - np.array([[[103.939, 116.779, 123.68]]])
patch = np.expand_dims(patch, 0)
prediction = self.model.predict(patch)
# compute dims
dims = self.dims_avg[self.cls_to_ind[cls]] + prediction[0][0]
# Transform regressed angle
box2d_center_x = (xmin + xmax) / 2.0
# Transfer arctan() from (-pi/2,pi/2) to (0,pi)
theta_ray = np.arctan(self.fx /(box2d_center_x - self.u0))
if theta_ray<0:
theta_ray = theta_ray+np.pi
max_anc = np.argmax(prediction[2][0])
anchors = prediction[1][0][max_anc]
if anchors[1] > 0:
angle_offset = np.arccos(anchors[0])
else:
angle_offset = -np.arccos(anchors[0])
bin_num = prediction[2][0].shape[0]
wedge = 2. * np.pi / bin_num
theta_loc = angle_offset + max_anc * wedge
theta = theta_loc + theta_ray
# object's yaw angle
yaw = np.pi/2 - theta
points2D = gen_3D_box(yaw, dims, self.cam_to_img, box_2D)
return points2D, box2d_reserved, img
def three_D(image_file):
cam_to_img = get_cam_data(calib_file)
fx = cam_to_img[0][0]
u0 = cam_to_img[0][2]
v0 = cam_to_img[1][2]
# for f in all_image:
# image_file = image_dir + f
# box2d_file = box2d_dir + f.replace('png', 'txt')
img = cv2.imread(image_file)
dect2D_data,box2d_reserved = get_dect2D_data(box2d_file,classes)
# Pedestrian 0.00 0 -0.20 712.40 143.00 810.73 307.92 1.89 0.48 1.20 1.84 1.47 8.41 0.01
for data in dect2D_data:
cls = data[0]
box_2D = np.asarray(data[1],dtype=np.float)
xmin = box_2D[0]
ymin = box_2D[1]
xmax = box_2D[2]
ymax = box_2D[3]
patch = img[int(ymin):int(ymax), int(xmin):int(xmax)]
patch = cv2.resize(patch, (224, 224))
patch = patch - np.array([[[103.939, 116.779, 123.68]]])
patch = np.expand_dims(patch, 0)
prediction = model.predict(patch)
# compute dims
dims = dims_avg[cls_to_ind[cls]] + prediction[0][0]
# Transform regressed angle
box2d_center_x = (xmin + xmax) / 2.0
# Transfer arctan() from (-pi/2,pi/2) to (0,pi)
theta_ray = np.arctan(fx /(box2d_center_x - u0))
if theta_ray<0:
theta_ray = theta_ray+np.pi
max_anc = np.argmax(prediction[2][0])
anchors = prediction[1][0][max_anc]
if anchors[1] > 0:
angle_offset = np.arccos(anchors[0])
else:
angle_offset = -np.arccos(anchors[0])
bin_num = prediction[2][0].shape[0]
wedge = 2. * np.pi / bin_num
theta_loc = angle_offset + max_anc * wedge
theta = theta_loc + theta_ray
# object's yaw angle
yaw = np.pi/2 - theta
points2D = gen_3D_box(yaw, dims, cam_to_img, box_2D)
draw_3D_box(img, points2D)
for cls,box in box2d_reserved:
draw_2D_box(img,box)
cv2.imshow(f, img)
cv2.waitKey(0)
cv2.destroyAllWindows()
cv2.imwrite('output/'+ f.replace('png','jpg'), img)
theta_ray = np.arctan(fx / (box2d_center_x - u0))
if theta_ray < 0:
theta_ray = theta_ray + np.pi
max_anc = np.argmax(prediction[2][0])
anchors = prediction[1][0][max_anc]
if anchors[1] > 0:
angle_offset = np.arccos(anchors[0])
else:
angle_offset = -np.arccos(anchors[0])
bin_num = prediction[2][0].shape[0]
wedge = 2. * np.pi / bin_num
theta_loc = angle_offset + max_anc * wedge
theta = theta_loc + theta_ray
# object's yaw angle
yaw = np.pi / 2 - theta
points2D = gen_3D_box(yaw, dims, cam_to_img, box_2D)
draw_3D_box(img, points2D)
for cls, box in box2d_reserved:
draw_2D_box(img, box)
#cv2.imshow(f, img)
#cv2.waitKey(0)
#cv2.destroyAllWindows()
cv2.imwrite('output/' + f.replace('png', 'jpg'), img)
anchors = prediction[1][0][max_anc]
if anchors[1] > 0:
angle_offset = np.arccos(anchors[0])
else:
angle_offset = -np.arccos(anchors[0])
bin_num = prediction[2][0].shape[0]
wedge = 2. * np.pi / bin_num
theta_loc = angle_offset + max_anc * wedge
theta = theta_loc + theta_ray
# object's yaw angle
yaw = np.pi / 2 - theta
self.bbox_3.yaw = yaw
result.append(bbox_3)
return result
if __name__ == "__main__":
f = open("config.json")
config = json.loads(f)
image_dir = config["dir"]["image_dir"]
all_image = sorted(os.listdir(image_dir))
detector = det_3d(config)
for image in all_image:
dets = yolo.detctor(image)
result = det_3d(image, dets)
for res in result:
points2D = gen_3D_box(res.yaw, res.dims, detector.cam_to_img)