ftdcherub 0.2

Just use for myself

How to use

from ftd.module.detector import Detector

# 解析yaml配置文件
cfg = LoadYaml(opt.yaml)    
print(cfg) 
device=torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
model=Detector(cfg.category_num,True).to(device)

# 数据预处理
ori_img = cv2.imread(opt.img)
res_img = cv2.resize(ori_img, (cfg.input_width, cfg.input_height), interpolation = cv2.INTER_LINEAR) 
img = res_img.reshape(1, cfg.input_height, cfg.input_width, 3)
img = torch.from_numpy(img.transpose(0, 3, 1, 2))
img = img.to(device).float() / 255.0

# 模型推理
start = time.perf_counter()
preds = model(img)
end = time.perf_counter()
time = (end - start) * 1000.
print("forward time:%fms"%time)

# 特征图后处理
output = handle_preds(preds, device, opt.thresh)

# 加载label names
LABEL_NAMES = []
with open(cfg.names, 'r') as f:
    for line in f.readlines():
        LABEL_NAMES.append(line.strip())

H, W, _ = ori_img.shape
scale_h, scale_w = H / cfg.input_height, W / cfg.input_width

# 绘制预测框
for box in output[0]:
    print(box)
    box = box.tolist()
    
    obj_score = box[4]
    category = LABEL_NAMES[int(box[5])]

    x1, y1 = int(box[0] * W), int(box[1] * H)
    x2, y2 = int(box[2] * W), int(box[3] * H)

    cv2.rectangle(ori_img, (x1, y1), (x2, y2), (255, 255, 0), 2)
    cv2.putText(ori_img, '%.2f' % obj_score, (x1, y1 - 5), 0, 0.7, (0, 255, 0), 2)	
    cv2.putText(ori_img, category, (x1, y1 - 25), 0, 0.7, (0, 255, 0), 2)

cv2.imwrite("result.png", ori_img)