class Yolo:
    labels = ["aeroplane","bicycle","bird","boat","bottle","bus","car","cat","chair","cow","diningtable","dog","horse","motorbike","person","pottedplant","sheep","sofa","train","tvmonitor"]
    anchors = [0.4165, 0.693 , 0.9765, 1.6065, 1.5855, 3.122 , 2.821 , 1.8515 , 3.612 , 3.7275]
    m = {
        "bin": "/home/model/aipu_yolo_VOC2007.bin"
    }
    options = {
        "model_type":  "aipu",
        "inputs": {
            "input0": (224, 224, 3)
        },
        "outputs": {
             "output0": (7, 7, (1+4+len(labels))*5)
        },
        "mean": [127.5, 127.5, 127.5],
        "norm": [0.0078125, 0.0078125, 0.0078125],
        "scale":[8.031941],
    }
    def __init__(self):
        from maix import nn
        from maix.nn import decoder
        self.model = nn.load(self.m, opt=self.options)
        self.decoder = decoder.Yolo2(len(self.labels), self.anchors, net_in_size=(224, 224), net_out_size=(7, 7))
    def __del__(self):
        del self.model
        del self.decoder
print(Yolo)

from maix import camera, display
yolo = Yolo()
print(yolo)
while True:
    img = camera.capture().resize(224,224)
    out = yolo.model.forward(img, quantize=True, layout="chw")
    boxes, probs = yolo.decoder.run(out, nms=0.5, threshold=0.5, img_size=(224, 224))
    if len(boxes):
        for i, box in enumerate(boxes):
            class_id = probs[i][0]
            prob = probs[i][1][class_id]
            disp_str = "{}:{:.2f}%".format(yolo.labels[class_id], prob*100)
            img.draw_rectangle(box[0], box[1], box[0]+box[2], box[1]+box[3], (255,0,0), 1)
            img.draw_string(box[0], box[1]+ box[3] ,disp_str, scale=0.5,color=(0, 0, 255), thickness=1)
        display.show(img)
    else:
        display.show(img)