def inference(args, config, network):
# model_path
misc_utils.ensure_dir('outputs')
saveDir = os.path.join('../model', args.model_dir, config.model_dir)
model_file = os.path.join(saveDir,
'dump-{}.pth'.format(args.resume_weights))
assert os.path.exists(model_file)
# build network
net = network()
net.eval()
check_point = torch.load(model_file, map_location=torch.device('cpu'))
net.load_state_dict(check_point['state_dict'])
# get data
image, resized_img, im_info = get_data(args.img_path,
config.eval_image_short_size,
config.eval_image_max_size)
pred_boxes = net(resized_img, im_info).numpy()
pred_boxes = post_process(pred_boxes, config, im_info[0, 2])
pred_tags = pred_boxes[:, 5].astype(np.int32).flatten()
pred_tags_name = np.array(config.class_names)[pred_tags]
# inplace draw
image = visual_utils.draw_boxes(image,
pred_boxes[:, :4],
scores=pred_boxes[:, 4],
tags=pred_tags_name,
line_thick=1,
line_color='white')
name = args.img_path.split('/')[-1].split('.')[-2]
fpath = 'outputs/{}.png'.format(name)
cv2.imwrite(fpath, image)
def inference(config, network):
# model_path
saveDir = os.path.join('../model', 'rcnn_emd_simple')
model_file = os.path.join(saveDir, 'outputs', 'rcnn_emd_simple_mge.pth')
assert os.path.exists(model_file)
# build network
net = network()
net.eval()
check_point = torch.load(model_file, map_location=torch.device('cpu'))
net.load_state_dict(check_point['state_dict'])
# get data
imglist = os.listdir('../test_imgs')
for index, imgname in enumerate(imglist):
image, resized_img, im_info = get_data(
os.path.join('../test_imgs', imgname), config.eval_image_short_size, config.eval_image_max_size)
pred_boxes = net(resized_img, im_info).numpy()
pred_boxes = post_process(pred_boxes, config, im_info[0, 2])
persons = visual_utils.draw_boxes(
image,
pred_boxes[:, :4],)
# 保存一张从一张图中检测出来的行人
for person_id, person in enumerate(persons):
fpath = 'outputs/{}.png'.format('person' + '_' + str(person_id + 1))
print(fpath)
cv2.imwrite(fpath, person)
def eval_all(args):
# json file
assert os.path.exists(args.json_file), "Wrong json path!"
misc_utils.ensure_dir('outputs')
records = misc_utils.load_json_lines(args.json_file)[:args.number]
for record in records:
dtboxes = misc_utils.load_bboxes(
record, key_name='dtboxes', key_box='box', key_score='score', key_tag='tag')
gtboxes = misc_utils.load_bboxes(record, 'gtboxes', 'box')
dtboxes = misc_utils.xywh_to_xyxy(dtboxes)
gtboxes = misc_utils.xywh_to_xyxy(gtboxes)
keep = dtboxes[:, -2] > args.visual_thresh
dtboxes = dtboxes[keep]
len_dt = len(dtboxes)
len_gt = len(gtboxes)
line = "{}: dt:{}, gt:{}.".format(record['ID'], len_dt, len_gt)
print(line)
img_path = img_root + record['ID'] + '.png'
img = misc_utils.load_img(img_path)
visual_utils.draw_boxes(img, dtboxes, line_thick=1, line_color='blue')
visual_utils.draw_boxes(img, gtboxes, line_thick=1, line_color='white')
fpath = 'outputs/{}.png'.format(record['ID'])
cv2.imwrite(fpath, img)
def inference(config, network):
# model_path
saveDir = os.path.join('../model', 'rcnn_emd_simple')
model_file = os.path.join(saveDir, 'outputs', 'rcnn_emd_simple_mge.pth')
assert os.path.exists(model_file)
# build network
net = network()
net.eval()
check_point = torch.load(model_file, map_location=torch.device('cpu'))
net.load_state_dict(check_point['state_dict'])
# get data
video = "../test_video/Crash_walk_f_cm_np1_ri_med_0.avi"
cap = cv2.VideoCapture(video)
retaining = True
# 视频帧计数间隔频率
timeF = 4
# 记录积累的帧数
c = 0
while retaining:
retaining, frame = cap.read()
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
if not retaining and frame is None:
continue
if c % timeF == 0:
image, resized_img, im_info = get_numpy_data(
frame, config.eval_image_short_size,
config.eval_image_max_size)
pred_boxes = net(resized_img, im_info).numpy()
pred_boxes = post_process(pred_boxes, config, im_info[0, 2])
persons = visual_utils.draw_boxes(
image,
pred_boxes[:, :4],
)
# 保存一张从一张图中检测出来的行人
for person_id, person in enumerate(persons):
fpath = 'outputs/{}_{}.png'.format(
str(c), 'person' + '_' + str(person_id + 1))
print(fpath)
cv2.imwrite(fpath, person)
c += 1