in multi_obj_pose_estimation/valid_multi.py [0:0]
def valid(datacfg, cfgfile, weightfile):
def truths_length(truths):
for i in range(50):
if truths[i][1] == 0:
return i
# Parse data configuration files
data_options = read_data_cfg(datacfg)
valid_images = data_options['valid']
meshname = data_options['mesh']
name = data_options['name']
im_width = int(data_options['im_width'])
im_height = int(data_options['im_height'])
fx = float(data_options['fx'])
fy = float(data_options['fy'])
u0 = float(data_options['u0'])
v0 = float(data_options['v0'])
# Parse net configuration file
net_options = parse_cfg(cfgfile)[0]
loss_options = parse_cfg(cfgfile)[-1]
conf_thresh = float(net_options['conf_thresh'])
num_keypoints = int(net_options['num_keypoints'])
num_classes = int(loss_options['classes'])
num_anchors = int(loss_options['num'])
anchors = [float(anchor) for anchor in loss_options['anchors'].split(',')]
# Read object model information, get 3D bounding box corners, get intrinsics
mesh = MeshPly(meshname)
vertices = np.c_[np.array(mesh.vertices), np.ones((len(mesh.vertices), 1))].transpose()
corners3D = get_3D_corners(vertices)
diam = float(data_options['diam'])
intrinsic_calibration = get_camera_intrinsic(u0, v0, fx, fy) # camera params
# Network I/O params
num_labels = 2*num_keypoints+3 # +2 for width, height, +1 for object class
errs_2d = [] # to save
with open(valid_images) as fp: # validation file names
tmp_files = fp.readlines()
valid_files = [item.rstrip() for item in tmp_files]
# Compute-related Parameters
use_cuda = True # whether to use cuda or no
kwargs = {'num_workers': 4, 'pin_memory': True} # number of workers etc.
# Specicy model, load pretrained weights, pass to GPU and set the module in evaluation mode
model = Darknet(cfgfile)
model.load_weights(weightfile)
model.cuda()
model.eval()
# Get the dataloader for the test dataset
valid_dataset = dataset_multi.listDataset(valid_images, shape=(model.width, model.height), shuffle=False, objclass=name, transform=transforms.Compose([transforms.ToTensor(),]))
test_loader = torch.utils.data.DataLoader(valid_dataset, batch_size=1, shuffle=False, **kwargs)
# Iterate through test batches (Batch size for test data is 1)
logging('Testing {}...'.format(name))
for batch_idx, (data, target) in enumerate(test_loader):
t1 = time.time()
# Pass data to GPU
if use_cuda:
data = data.cuda()
# target = target.cuda()
# Wrap tensors in Variable class, set volatile=True for inference mode and to use minimal memory during inference
data = Variable(data, volatile=True)
t2 = time.time()
# Forward pass
output = model(data).data
t3 = time.time()
# Using confidence threshold, eliminate low-confidence predictions
trgt = target[0].view(-1, num_labels)
all_boxes = get_multi_region_boxes(output, conf_thresh, num_classes, num_keypoints, anchors, num_anchors, int(trgt[0][0]), only_objectness=0)
t4 = time.time()
# Iterate through all images in the batch
for i in range(output.size(0)):
# For each image, get all the predictions
boxes = all_boxes[i]
# For each image, get all the targets (for multiple object pose estimation, there might be more than 1 target per image)
truths = target[i].view(-1, num_labels)
# Get how many object are present in the scene
num_gts = truths_length(truths)
# Iterate through each ground-truth object
for k in range(num_gts):
box_gt = list()
for j in range(1, num_labels):
box_gt.append(truths[k][j])
box_gt.extend([1.0, 1.0])
box_gt.append(truths[k][0])
# If the prediction has the highest confidence, choose it as our prediction
best_conf_est = -sys.maxsize
for j in range(len(boxes)):
if (boxes[j][2*num_keypoints] > best_conf_est) and (boxes[j][2*num_keypoints+2] == int(truths[k][0])):
best_conf_est = boxes[j][2*num_keypoints]
box_pr = boxes[j]
match = corner_confidence(box_gt[:2*num_keypoints], torch.FloatTensor(boxes[j][:2*num_keypoints]))
# Denormalize the corner predictions
corners2D_gt = np.array(np.reshape(box_gt[:2*num_keypoints], [-1, 2]), dtype='float32')
corners2D_pr = np.array(np.reshape(box_pr[:2*num_keypoints], [-1, 2]), dtype='float32')
corners2D_gt[:, 0] = corners2D_gt[:, 0] * im_width
corners2D_gt[:, 1] = corners2D_gt[:, 1] * im_height
corners2D_pr[:, 0] = corners2D_pr[:, 0] * im_width
corners2D_pr[:, 1] = corners2D_pr[:, 1] * im_height
corners2D_gt_corrected = fix_corner_order(corners2D_gt) # Fix the order of corners
# Compute [R|t] by pnp
objpoints3D = np.array(np.transpose(np.concatenate((np.zeros((3, 1)), corners3D[:3, :]), axis=1)), dtype='float32')
K = np.array(intrinsic_calibration, dtype='float32')
R_gt, t_gt = pnp(objpoints3D, corners2D_gt_corrected, K)
R_pr, t_pr = pnp(objpoints3D, corners2D_pr, K)
# Compute pixel error
Rt_gt = np.concatenate((R_gt, t_gt), axis=1)
Rt_pr = np.concatenate((R_pr, t_pr), axis=1)
proj_2d_gt = compute_projection(vertices, Rt_gt, intrinsic_calibration)
proj_2d_pred = compute_projection(vertices, Rt_pr, intrinsic_calibration)
proj_corners_gt = np.transpose(compute_projection(corners3D, Rt_gt, intrinsic_calibration))
proj_corners_pr = np.transpose(compute_projection(corners3D, Rt_pr, intrinsic_calibration))
norm = np.linalg.norm(proj_2d_gt - proj_2d_pred, axis=0)
pixel_dist = np.mean(norm)
errs_2d.append(pixel_dist)
t5 = time.time()
# Compute 2D projection score
eps = 1e-5
for px_threshold in [5, 10, 15, 20, 25, 30, 35, 40, 45, 50]:
acc = len(np.where(np.array(errs_2d) <= px_threshold)[0]) * 100. / (len(errs_2d)+eps)
# Print test statistics
logging(' Acc using {} px 2D Projection = {:.2f}%'.format(px_threshold, acc))