File size: 3,864 Bytes
b24c748 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 | import numpy as np
import os
import cv2
def calculate_2d_projections(coordinates_3d, intrinsics):
"""
Input:
coordinates: [3, N]
intrinsics: [3, 3]
Return
projected_coordinates: [N, 2]
"""
projected_coordinates = intrinsics @ coordinates_3d
projected_coordinates = projected_coordinates[:2, :] / projected_coordinates[2, :]
projected_coordinates = projected_coordinates.T
projected_coordinates = np.array(projected_coordinates, dtype=np.int32)
return projected_coordinates
def get_3d_bbox(scale, shift = 0):
"""
Input:
scale: [3] or scalar
shift: [3] or scalar
Return
bbox_3d: [3, N]
"""
if hasattr(scale, "__iter__"):
bbox_3d = np.array([[scale[0] / 2, +scale[1] / 2, scale[2] / 2],
[scale[0] / 2, +scale[1] / 2, -scale[2] / 2],
[-scale[0] / 2, +scale[1] / 2, scale[2] / 2],
[-scale[0] / 2, +scale[1] / 2, -scale[2] / 2],
[+scale[0] / 2, -scale[1] / 2, scale[2] / 2],
[+scale[0] / 2, -scale[1] / 2, -scale[2] / 2],
[-scale[0] / 2, -scale[1] / 2, scale[2] / 2],
[-scale[0] / 2, -scale[1] / 2, -scale[2] / 2]]) + shift
else:
bbox_3d = np.array([[scale / 2, +scale / 2, scale / 2],
[scale / 2, +scale / 2, -scale / 2],
[-scale / 2, +scale / 2, scale / 2],
[-scale / 2, +scale / 2, -scale / 2],
[+scale / 2, -scale / 2, scale / 2],
[+scale / 2, -scale / 2, -scale / 2],
[-scale / 2, -scale / 2, scale / 2],
[-scale / 2, -scale / 2, -scale / 2]]) +shift
bbox_3d = bbox_3d.transpose()
return bbox_3d
def draw_3d_bbox(img, imgpts, color, size=1):
imgpts = np.int32(imgpts).reshape(-1, 2)
# draw ground layer in darker color
color_ground = (int(color[0] * 0.3), int(color[1] * 0.3), int(color[2] * 0.3))
for i, j in zip([4, 5, 6, 7],[5, 7, 4, 6]):
img = cv2.line(img, tuple(imgpts[i]), tuple(imgpts[j]), color_ground, size)
# draw pillars in blue color
color_pillar = (int(color[0]*0.6), int(color[1]*0.6), int(color[2]*0.6))
for i, j in zip(range(4),range(4,8)):
img = cv2.line(img, tuple(imgpts[i]), tuple(imgpts[j]), color_pillar, size)
# finally, draw top layer in color
for i, j in zip([0, 1, 2, 3],[1, 3, 0, 2]):
img = cv2.line(img, tuple(imgpts[i]), tuple(imgpts[j]), color, size)
return img
def draw_3d_pts(img, imgpts, color, size=1):
imgpts = np.int32(imgpts).reshape(-1, 2)
for point in imgpts:
img = cv2.circle(img, (point[0], point[1]), size, color, -1)
return img
def draw_detections(image, pred_rots, pred_trans, model_points, intrinsics, color=(255, 0, 0)):
num_pred_instances = len(pred_rots)
draw_image_bbox = image.copy()
# 3d bbox
scale = (np.max(model_points, axis=0) - np.min(model_points, axis=0))
shift = np.mean(model_points, axis=0)
print(scale, shift)
bbox_3d = get_3d_bbox(scale, shift)
# 3d point
choose = np.random.choice(np.arange(len(model_points)), 512)
pts_3d = model_points[choose].T
for ind in range(num_pred_instances):
# draw 3d bounding box
transformed_bbox_3d = pred_rots[ind]@bbox_3d + pred_trans[ind][:,np.newaxis]
projected_bbox = calculate_2d_projections(transformed_bbox_3d, intrinsics[ind])
draw_image_bbox = draw_3d_bbox(draw_image_bbox, projected_bbox, color)
# draw point cloud
transformed_pts_3d = pred_rots[ind]@pts_3d + pred_trans[ind][:,np.newaxis]
projected_pts = calculate_2d_projections(transformed_pts_3d, intrinsics[ind])
draw_image_bbox = draw_3d_pts(draw_image_bbox, projected_pts, color)
return draw_image_bbox
|