目前文档只包含outputs = model(inputs,coord_ranges,calibs,K=50,mode=‘test’)之后，前向推理的源码解析，附带有测试程序
DEVIANT: Depth EquiVarIAnt NeTwork for Monocular 3D Object Detection
githubs = https://github.com/abhi1kumar/DEVIANT
核心思想:
此算法输出目标的2D坐标和3D坐标
总共有两个坐标系：image坐标系（2D）和相机坐标系（3D），要弄清楚网络输出值具体是哪个坐标系下的。
通过heatmap图确定目标在image坐标系上的位置和类别，再计算出
目标2D image坐标系中心点的偏移，得到2D image坐标系上的目标尺寸。网络输出3D size大小的偏移（相机坐标系下），根据预设的3D mean size大小直接得出目标3D尺寸。网络输出相机坐标系下目标的depth，也就是目标的3D中心点z值，最后利用相机的内外参数，结合z坐标，算出相机坐标系下目标的中心点x,y值。网络输出相机坐标系下目标的角度heading，将角度[-pai,pai]拆分了num_heading_bin=12个bin值，再做出精调，算出每个bin值的偏移量，得到alpha，最后ry = calibs.alpha2ry(alpha, x)得到真实目标角度。
注意：最后输出的三位目标的尺寸（长宽高），对应的是z、y、x轴方向的尺寸，而且最终的角度是沿y轴旋转的，和一般的激光雷达沿z轴旋转不同。
推理测试脚本
demo_inference_zs.py
import os
import sys
BASE_DIR = os.path.dirname(os.path.abspath(__file__))
ROOT_DIR = os.path.dirname(BASE_DIR)
sys.path.append(ROOT_DIR)
import yaml
import logging
import argparse
import torch
import numpy as np
import random
from lib.helpers.dataloader_helper import build_dataloader
from lib.helpers.model_helper import build_model
from lib.helpers.optimizer_helper import build_optimizer
from lib.helpers.scheduler_helper import build_lr_scheduler
from lib.helpers.trainer_helper import Trainer
from lib.helpers.tester_helper import Tester
from lib.datasets.kitti_utils import get_affine_transform
from lib.datasets.kitti_utils import Calibration
from lib.datasets.kitti_utils import compute_box_3d
from lib.datasets.waymo import affine_transform
from lib.helpers.save_helper import load_checkpoint
from lib.helpers.decode_helper import extract_dets_from_outputs
from lib.helpers.decode_helper import decode_detections
from lib.helpers.rpn_util import *
from datetime import datetime
parser = argparse.ArgumentParser(description='implementation of DEVIANT')
parser.add_argument('-e', '--evaluate', dest='evaluate', action='store_true', help='evaluate model on validation set')
parser.add_argument('--config', type=str, default = 'experiments/config.yaml')
parser.add_argument('--resume_model', type=str, default=None)
args = parser.parse_args()
def create_logger(log_file):
log_format = '%(asctime)s %(levelname)5s %(message)s'
logging.basicConfig(level=logging.INFO, format=log_format, filename=log_file)
console = logging.StreamHandler()
console.setLevel(logging.INFO)
console.setFormatter(logging.Formatter(log_format))
logging.getLogger().addHandler(console)
return logging.getLogger(__name__)
def init_torch(rng_seed, cuda_seed):
"""
Initializes the seeds for ALL potential randomness, including torch, numpy, and random packages.
Args:
rng_seed (int): the shared random seed to use for numpy and random
cuda_seed (int): the random seed to use for pytorch's torch.cuda.manual_seed_all function
"""
# seed everything
os.environ['PYTHONHASHSEED'] = str(rng_seed)
torch.manual_seed(rng_seed)
np.random.seed(rng_seed)
random.seed(rng_seed)
torch.cuda.manual_seed(cuda_seed)
torch.cuda.manual_seed_all(cuda_seed)
# make the code deterministic
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
def pretty_print(name, input, val_width=40, key_width=0):
"""
This function creates a formatted string from a given dictionary input.
It may not support all data types, but can probably be extended.
Args:
name (str): name of the variable root
input (dict): dictionary to print
val_width (int): the width of the right hand side values
key_width (int): the minimum key width, (always auto-defaults to the longest key!)
Example:
pretty_str = pretty_print('conf', conf.__dict__)
pretty_str = pretty_print('conf', {'key1': 'example', 'key2': [1,2,3,4,5], 'key3': np.random.rand(4,4)})
print(pretty_str)
or
logging.info(pretty_str)
"""
pretty_str = name + ': {\n'
for key in input.keys(): key_width = max(key_width, len(str(key)) + 4)
for key in input.keys():
val = input[key]
# round values to 3 decimals..
if type(val) == np.ndarray: val = np.round(val, 3).tolist()
# difficult formatting
val_str = str(val)
if len(val_str) > val_width:
# val_str = pprint.pformat(val, width=val_width, compact=True)
val_str = val_str.replace('\n', '\n{tab}')
tab = ('{0:' + str(4 + key_width) + '}').format('')
val_str = val_str.replace('{tab}', tab)
# more difficult formatting
format_str = '{0:' + str(4) + '}{1:' + str(key_width) + '} {2:' + str(val_width) + '}\n'
pretty_str += format_str.format('', key + ':', val_str)
# close root object
pretty_str += '}'
return pretty_str
def np2tuple(n):
return (int(n[0]), int(n[1]))
# def main():
# load cfg
config = "/media/data/zs/project/detection3D/github/DEVIANT/code/experiments/run_test.yaml"
cfg = yaml.load(open(config, 'r'), Loader=yaml.Loader)
exp_parent_dir = os.path.join(cfg['trainer']['log_dir'], os.path.basename(config).split(".")[0])
cfg['trainer']['log_dir'] = exp_parent_dir
logger_dir = os.path.join(exp_parent_dir, "log")
os.makedirs(exp_parent_dir, exist_ok=True)
os.makedirs(logger_dir, exist_ok=True)
timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
logger = create_logger(os.path.join(logger_dir, timestamp))
pretty = pretty_print('conf', cfg)
logging.info(pretty)
# init torch
init_torch(rng_seed= cfg['random_seed']-3, cuda_seed= cfg['random_seed'])
##w,h,l
# Ped [1.7431 0.8494 0.911 ]
# Car [1.8032 2.1036 4.8104]
# Cyc [1.7336 0.823 1.753 ]
# Sign [0.6523 0.6208 0.1254]
cls_mean_size = np.array([[1.7431, 0.8494, 0.9110],
[1.8032, 2.1036, 4.8104],
[1.7336, 0.8230, 1.7530],
[0.6523, 0.6208, 0.1254]])
# build model
model = build_model(cfg,cls_mean_size)
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
#Knew=np.array([[7.070493000000e+02, 0.000000000000e+00, 6.040814000000e+02 ],
# [0.000000000000e+00, 7.070493000000e+02, 1.805066000000e+02 ], [0.000000000000e+00, 0.000000000000e+00, 1.000000000000e+00 ]])
# calibs = np.array([7.070493000000e+02/1920.0*768.0*0.9, 0., (6.040814000000e+023)/1920.0*768.0, 0.,
# 0., 7.070493000000e+02/1080.0*512.0*0.9, 1.805066000000e+02/1080.0*512.0, 0.,
# 0.0, 0.0, 1.0, 0.], dtype=np.float32)
calibs = np.array([9.5749393532104671e+02/1920.0*768.0*0.9, 0., (1.0143950223349893e+03)/1920.0*768.0, 0.,
0., 9.5697913744394737e+02/1080.0*512.0*0.9, 5.4154074349276050e+02/1080.0*512.0, 0.,
0.0, 0.0, 1.0, 0.], dtype=np.float32)
calibsP2=calibs.reshape(3, 4)
downsample = 4
# c++调用的接口
def detect_online(frame_chan, frame):
print("image channel :", frame_chan)
resolution = np.array([768, 512])
img = frame
index = np.array([frame_chan])
#img = img[0:1080, int(150-1):int(1920-150-1)]
# img = cv2.resize(img, (768, 512))
img_cv_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
img = Image.fromarray(img_cv_rgb)
img_size = np.array(img.size)
center = img_size / 2
crop_size = img_size
# coord_ranges = np.array([center-crop_size/2,center+crop_size/2]).astype(np.float32)
coord_ranges = np.array([np.array([0, 0,]),resolution]).astype(np.float32)
features_size = resolution // downsample
info = {
'img_id': index,
'img_size': resolution,
'bbox_downsample_ratio': resolution / features_size}
# add affine transformation for 2d images.
trans, trans_inv = get_affine_transform(center, crop_size, 0, resolution, inv=1)
img = img.transform(tuple(resolution.tolist()),
method=Image.AFFINE,
data=tuple(trans_inv.reshape(-1).tolist()),
resample=Image.BILINEAR)
# image encoding
img = np.array(img).astype(np.float32) / 255.0
mean = np.array([0.485, 0.456, 0.406], dtype=np.float32)
std = np.array([0.229, 0.224, 0.225], dtype=np.float32)
img = (img - mean) / std
img = img.transpose(2, 0, 1) # C * H * W
inputs = img
load_checkpoint(model = model,
optimizer = None,
filename = cfg['tester']['resume_model'],
logger=logger,
map_location= device)
model.to(device)
torch.set_grad_enabled(False)
model.eval()
inputs = torch.from_numpy(inputs)
inputs = inputs.unsqueeze(0)
calibs = torch.from_numpy(calibsP2)
calibs = calibs.unsqueeze(0)
coord_ranges = torch.from_numpy(coord_ranges)
coord_ranges = coord_ranges.unsqueeze(0)
inputs = inputs.to(device)
calibs = calibs.to(device)
coord_ranges = coord_ranges.to(device)
# the outputs of centernet
outputs = model(inputs,coord_ranges,calibs,K=50,mode='test')
dets = extract_dets_from_outputs(outputs=outputs, K=50)
dets = dets.detach().cpu().numpy()
# get corresponding calibs & transform tensor to numpy
#info = {key: val.detach().cpu().numpy() for key, val in info.items()}
calibs = Calibration(calibsP2)
dets = decode_detections(dets = dets,
info = info,
calibs = calibs,
cls_mean_size=cls_mean_size,
threshold = cfg['tester']['threshold'])
img = cv2.imread(img_file, cv2.IMREAD_COLOR)
# img = cv2.resize(img, (768, 512))
print(dets)
for i in range(len(dets)):
#cv2.rectangle(img, (int(dets[i][2]), int(dets[i][3])), (int(dets[i][4]), int(dets[i][5])), (0, 0, 255), 1)
corners_2d,corners_3d=compute_box_3d(dets[i][6], dets[i][7], dets[i][8], dets[i][12],
(dets[i][9], dets[i][10], dets[i][11]), calibs)
for i in range(corners_2d.shape[0]):
corners_2d[i] = affine_transform(corners_2d[i], trans_inv)
cv2.line(img,np2tuple(corners_2d[0]),np2tuple(corners_2d[1]),(255,255,0),1,cv2.LINE_4)
cv2.line(img,np2tuple(corners_2d[1]),np2tuple(corners_2d[2]),(255,255,0),1,cv2.LINE_4)
cv2.line(img,np2tuple(corners_2d[2]),np2tuple(corners_2d[3]),(255,255,0),1,cv2.LINE_4)
cv2.line(img,np2tuple(corners_2d[3]),np2tuple(corners_2d[0]),(255,255,0),1,cv2.LINE_4)
cv2.line(img,np2tuple(corners_2d[4]),np2tuple(corners_2d[5]),(255,255,0),1,cv2.LINE_4)
cv2.line(img,np2tuple(corners_2d[5]),np2tuple(corners_2d[6]),(255,255,0),1,cv2.LINE_4)
cv2.line(img,np2tuple(corners_2d[6]),np2tuple(corners_2d[7]),(255,255,0),1,cv2.LINE_4)
cv2.line(img,np2tuple(corners_2d[7]),np2tuple(corners_2d[4]),(255,255,0),1,cv2.LINE_4)
cv2.line(img,np2tuple(corners_2d[5]),np2tuple(corners_2d[1]),(255,255,0),1,cv2.LINE_4)
cv2.line(img,np2tuple(corners_2d[4]),np2tuple(corners_2d[0]),(255,255,0),1,cv2.LINE_4)
cv2.line(img,np2tuple(corners_2d[6]),np2tuple(corners_2d[2]),(255,255,0),1,cv2.LINE_4)
cv2.line(img,np2tuple(corners_2d[7]),np2tuple(corners_2d[3]),(255,255,0),1,cv2.LINE_4)
cv2.imwrite("3.jpg", img)
if __name__ == '__main__':
img_file = "/media/data/zs/project/detection3D/github/DEVIANT/code/tools/img/result2_00001225.jpg"
# img_file = "./151608731.jpg"
frame = cv2.imread(img_file, cv2.IMREAD_COLOR)
frame_chan = 0
detect_online(frame_chan, frame)
源码解析
def decode_detections(dets, info, calibs, cls_mean_size, threshold):
'''
NOTE: THIS IS A NUMPY FUNCTION
input: dets, numpy array, shape in [batch x max_dets x dim]
input: img_info, dict, necessary information of input images
input: calibs, corresponding calibs for the input batch
output:
'''
results = []
for i in range(dets.shape[0]): # batch
preds = []
for j in range(dets.shape[1]): # max_dets
cls_id = int(dets[i, j, 0])
score = dets[i, j, 1]
if score < threshold: continue
# 2d bboxs decoding
x = dets[i, j, 2] * info['bbox_downsample_ratio'][0]
y = dets[i, j, 3] * info['bbox_downsample_ratio'][1]
w = dets[i, j, 4] * info['bbox_downsample_ratio'][0]
h = dets[i, j, 5] * info['bbox_downsample_ratio'][1]
bbox = [x-w/2, y-h/2, x+w/2, y+h/2]
# 3d bboxs decoding
# depth decoding
depth = dets[i, j, 6]
# heading angle decoding
alpha = get_heading_angle(dets[i, j, 7:31])
ry = calibs.alpha2ry(alpha, x) #alpha、 ry 可以参考kitti数据集，都是表示目标角度
# dimensions decoding
dimensions = dets[i, j, 31:34]
dimensions += cls_mean_size[int(cls_id)]
if True in (dimensions<0.0): continue
# positions decoding
x3d = dets[i, j, 34] * info['bbox_downsample_ratio'][0]
y3d = dets[i, j, 35] * info['bbox_downsample_ratio'][1]
locations = calibs.img_to_rect(x3d, y3d, depth).reshape(-1)#这一步的操作是：将图像坐标系上的3D目标中心点（x，y），通过相机内外参和深度值，得出相机坐标系下的3D目标中心点（x，y）,既相机坐标系下，真实3维世界目标中心点（x，y）
preds.append([cls_id, alpha] + bbox + dimensions.tolist() + locations.tolist() + [ry, score])
results = preds
return results
#two stage style
def extract_dets_from_outputs(outputs, K=50):
# get src outputs
heatmap = outputs['heatmap'] #shape = [1,3,128,192]，heatmap 是导火索，引出目标类别和位置，3是目标的类别个数。
size_2d = outputs['size_2d'] #shape = [1,2,128,192] ，size_2d image上面检测目标大小
offset_2d = outputs['offset_2d'] #shape = [1,2,128,192]，offset_2d image上面检测目标中心的偏移量
batch, channel, height, width = heatmap.size() # get shape
heading = outputs['heading'].view(batch,K,-1) #shape = [1,50,24] #相机坐标系上面检测目标角度
depth = outputs['depth'].view(batch,K,-1)[:,:,0:1] #shape = [1,50,1] #相机坐标系上面检测目标深度，也就是z值。
size_3d = outputs['size_3d'].view(batch,K,-1) #shape = [1,50,3] #相机坐标系上面检测目标尺寸大小
offset_3d = outputs['offset_3d'].view(batch,K,-1) #shape = [1,50,2] #image坐标系上面检测目标中心点偏移
heatmap= torch.clamp(heatmap.sigmoid_(), min=1e-4, max=1 - 1e-4)
# perform nms on heatmaps
heatmap = _nms(heatmap) #shape = [1,3,128,192]
scores, inds, cls_ids, xs, ys = _topk(heatmap, K=K) #scores, inds, cls_ids, xs, ys 的shape都是 = [1,50] ，这里有两个topk，第一个topk是在每3个类别上分别都选取50个目标，共有3*50=150个目标，第二个topk在150个目标上进行，最终所有类别一共得到50个目标，。
offset_2d = _transpose_and_gather_feat(offset_2d, inds) #shape = [1,50,2]，获取image参考系上2D目标的尺寸偏移量。
offset_2d = offset_2d.view(batch, K, 2) #shape = [1,50,2]
xs2d = xs.view(batch, K, 1) + offset_2d[:, :, 0:1] #shape = [1,50,1]，获取image参考系上2D目标的x。
ys2d = ys.view(batch, K, 1) + offset_2d[:, :, 1:2] #shape = [1,50,1]，获取image参考系上2D目标的y。
xs3d = xs.view(batch, K, 1) + offset_3d[:, :, 0:1] #shape = [1,50,1]，获取3D目标的中心点在image参考系上的x值。
ys3d = ys.view(batch, K, 1) + offset_3d[:, :, 1:2] #shape = [1,50,1]，获取3D目标的中心点在image参考系上的y值。
cls_ids = cls_ids.view(batch, K, 1).float() #shape = [1,50,1]
depth_score = (-(0.5*outputs['depth'].view(batch,K,-1)[:,:,1:2]).exp()).exp() #shape = [1,50,1]
scores = scores.view(batch, K, 1)*depth_score #shape = [1,50,1]
# check shape
xs2d = xs2d.view(batch, K, 1)
ys2d = ys2d.view(batch, K, 1)
xs3d = xs3d.view(batch, K, 1)
ys3d = ys3d.view(batch, K, 1)
size_2d = _transpose_and_gather_feat(size_2d, inds)
size_2d = size_2d.view(batch, K, 2) #shape = [1,50,2]
detections = torch.cat([cls_ids, scores, xs2d, ys2d, size_2d, depth, heading, size_3d, xs3d, ys3d], dim=2)
return detections
############### auxiliary function ############
def _nms(heatmap, kernel=3):
padding = (kernel - 1) // 2
heatmapmax = nn.functional.max_pool2d(heatmap, (kernel, kernel), stride=1, padding=padding)
keep = (heatmapmax == heatmap).float()
return heatmap * keep
def _topk(heatmap, K=50):
batch, cat, height, width = heatmap.size()
# batch * cls_ids * 50
topk_scores, topk_inds = torch.topk(heatmap.view(batch, cat, -1), K)
topk_inds = topk_inds % (height * width)
topk_ys = (topk_inds / width).int().float()
topk_xs = (topk_inds % width).int().float()
# batch * cls_ids * 50
topk_score, topk_ind = torch.topk(topk_scores.view(batch, -1), K)
topk_cls_ids = (topk_ind / K).int()
topk_inds = _gather_feat(topk_inds.view(batch, -1, 1), topk_ind).view(batch, K)
topk_ys = _gather_feat(topk_ys.view(batch, -1, 1), topk_ind).view(batch, K)
topk_xs = _gather_feat(topk_xs.view(batch, -1, 1), topk_ind).view(batch, K)
return topk_score, topk_inds, topk_cls_ids, topk_xs, topk_ys
def _gather_feat(feat, ind, mask=None):
'''
Args:
feat: tensor shaped in B * (H*W) * C
ind: tensor shaped in B * K (default: 50)
mask: tensor shaped in B * K (default: 50)
Returns: tensor shaped in B * K or B * sum(mask)
'''
dim = feat.size(2) # get channel dim
ind = ind.unsqueeze(2).expand(ind.size(0), ind.size(1), dim) # B*len(ind) --> B*len(ind)*1 --> B*len(ind)*C
feat = feat.gather(1, ind) # B*(HW)*C ---> B*K*C
if mask is not None:
mask = mask.unsqueeze(2).expand_as(feat) # B*50 ---> B*K*1 --> B*K*C
feat = feat[mask]
feat = feat.view(-1, dim)
return feat
在自己数据集上测试结果：
标定文件形式
数值不一定匹配，但形式是一致的。calib_file.yaml。
%YAML:1.0
---
DateTime: "Thu Feb 23 10:38:37 2023"
CameraNum: 4
CameraModel: OPENCV_PINHOLE
K0: !!opencv-matrix
rows: 3
cols: 3
dt: d
data: [ 1.2658371448642351e+03, 0., 2.0123623581885008e+03, 0.,
1.2660079003415947e+03, 1.0840676742653445e+03, 0., 0., 1. ]
D0: !!opencv-matrix
rows: 4
cols: 1
dt: d
data: [ -3.9718003549149311e-02, -2.8562555429533531e-03,
1.3966607106083210e-03, -6.2156272410679035e-04 ]
T0: !!opencv-matrix
rows: 4
cols: 4
dt: d
data: [ 0., 0., 1., -5.4466703664773138e-02, 0., 1., 0.,
-5.7737934912961107e-03, -1., 0., 0., -7.4737837911302313e-04, 0.,
0., 0., 1. ]
ImageSize0: [ 3840, 2336 ]
K1: !!opencv-matrix
rows: 3
cols: 3
dt: d
data: [ 1.2673739847425820e+03, 0., 1.9896258383310571e+03, 0.,
1.2670188006581632e+03, 1.0475654642936954e+03, 0., 0., 1. ]
D1: !!opencv-matrix
rows: 4
cols: 1
dt: d
data: [ -3.5171777690218543e-02, -1.4571303951032441e-02,
1.3697357332733785e-02, -5.0348584219881134e-03 ]
T1: !!opencv-matrix
rows: 4
cols: 4
dt: d
data: [ 1.3500816814260762e-02, -9.9990311504185525e-01,
-3.3894652890206771e-03, -1.0795804908072548e-01,
2.2206499229567161e-03, -3.3597827259085927e-03,
9.9999189025409563e-01, 4.0965277497536631e-02,
-9.9990639394858916e-01, -1.3508234141897316e-02,
2.1750749642631688e-03, -2.5224508958591818e-03, 0., 0., 0., 1. ]
ImageSize1: [ 3840, 2336 ]
K2: !!opencv-matrix
rows: 3
cols: 3
dt: d
data: [ 1.2594457609741498e+03, 0., 2.0213470480872181e+03, 0.,
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