def write_dets_results(res, data_id, save_path, dataset):
batch_size, win_size, max_dets, _ = res.shape
classes = dataset.object_cfg.classes
with open(save_path, 'a+') as f:
for b in range(batch_size):
for w in range(win_size):
for d in range(max_dets):
cla_id = int(res[b, w, d, 0])
if cla_id == -1:
continue
row_id = res[b, w, d, 1]
col_id = res[b, w, d, 2]
conf = res[b, w, d, 3]
f.write("%d %s %d %d %s\n" % (data_id + w, get_class_name(cla_id, classes), row_id, col_id, conf))
def write_dets_results_single_frame(res, data_id, save_path, dataset):
max_dets, _ = res.shape
classes = dataset.object_cfg.classes
with open(save_path, 'a+') as f:
for d in range(max_dets):
cla_id = int(res[d, 0])
if cla_id == -1:
continue
row_id = res[d, 1]
col_id = res[d, 2]
conf = res[d, 3]
f.write(
"%d %s %d %d %.4f\n" % (data_id, get_class_name(
cla_id, classes), row_id, col_id, conf))
def write_dets_results_single_frame(res, data_id, save_path, dataset):
max_dets, _ = res.shape
classes = dataset.object_cfg.classes
with open(save_path, 'a+') as f:
for d in range(max_dets):
cla_id = int(res[d, 0])
if cla_id == -1:
continue
row_id = res[d, 1]
col_id = res[d, 2]
conf = res[d, 3]
rid = int(row_id)
aid = int(col_id)
rng, azm = idx2ra(rid, aid, dataset.range_grid, dataset.angle_grid)
f.write("%d %f %f %s %s\n" % (data_id , rng, azm, get_class_name(cla_id, classes), conf))
def visualize_test_img(fig_name,
img_path,
input_radar,
confmap_pred,
confmap_gt,
res_final,
dataset,
viz=False,
sybl=False):
max_dets, _ = res_final.shape
classes = dataset.object_cfg.classes
img_data = mpimg.imread(img_path)
if img_data.shape[0] > 864:
img_data = img_data[:img_data.shape[0] // 5 * 4, :, :]
fig.add_subplot(2, 2, 1)
plt.imshow(img_data.astype(np.uint8))
plt.axis('off')
plt.title("Image")
fig.add_subplot(2, 2, 2)
plt.imshow(input_radar, origin='lower', aspect='auto')
plt.axis('off')
plt.title("RA Heatmap")
fig.add_subplot(2, 2, 3)
confmap_pred = np.transpose(confmap_pred, (1, 2, 0))
confmap_pred[confmap_pred < 0] = 0
confmap_pred[confmap_pred > 1] = 1
plt.imshow(confmap_pred, vmin=0, vmax=1, origin='lower', aspect='auto')
for d in range(max_dets):
cla_id = int(res_final[d, 0])
if cla_id == -1:
continue
row_id = res_final[d, 1]
col_id = res_final[d, 2]
conf = res_final[d, 3]
conf = 1.0 if conf > 1 else conf
cla_str = get_class_name(cla_id, classes)
if sybl:
text = symbols[cla_str]
plt.text(col_id,
row_id + 3,
text,
fontproperties=fp,
color='white',
size=20,
ha="center")
else:
plt.scatter(col_id, row_id, s=10, c='white')
text = cla_str + '\n%.2f' % conf
plt.text(col_id + 5, row_id, text, color='white', fontsize=10)
plt.axis('off')
plt.title("RODNet Detection")
fig.add_subplot(2, 2, 4)
confmap_gt = np.transpose(confmap_gt, (1, 2, 0))
plt.imshow(confmap_gt, vmin=0, vmax=1, origin='lower', aspect='auto')
plt.axis('off')
plt.title("Ground Truth")
plt.savefig(fig_name)
if viz:
plt.pause(0.1)
plt.clf()