path: C://yolov5/auto_maker # dataset root dir
train: images # train images (relative to 'path') 90% of 847 train imagess
val: val/images # train images (relative to 'path') 10% of 847 train images

names:
  0: bodyarmor_1_5
  1: bodyarmor_2_5
  2: bodyarmor_3_5
  3: bodyarmor_4_5
  4: bodyarmor_5_5
  5: firstaid_1_5
  6: firstaid_2_5
  7: firstaid_3_5
  8: firstaid_4_5
  9: firstaid_5_5
  10: man_1_5
  11: man_2_5
  12: man_3_5
  13: smoke_1_5
  14: smoke_2_5
  15: smoke_3_5
  16: smoke_4_5
  17: smoke_5_5
  18: spontoon_1_5
  19: spontoon_2_5
  20: spontoon_3_5
  21: spontoon_4_5
  22: spontoon_5_5

    parser.add_argument('--epochs', type=int, default=300, help='total training epochs')
    parser.add_argument('--batch-size', type=int, default=-1, help='total batch size for all GPUs, -1 for autobatch')

python .\train.py --data .\xView.yaml --weight yolov5n.pt --img 640

python .\export.py --rknpu --weight .\runs\train\exp2\weights\1.pt

import os
import cv2
import sys
import argparse
import time
# add path
realpath = os.path.abspath(__file__)
_sep = os.path.sep
realpath = realpath.split(_sep)
sys.path.append(os.path.join(realpath[0]+_sep, *realpath[1:realpath.index('scripts')+1]))

from py_utils.coco_utils import COCO_test_helper
import numpy as np


OBJ_THRESH = 0.25
NMS_THRESH = 0.45

# The follew two param is for map test
# OBJ_THRESH = 0.001
# NMS_THRESH = 0.65

IMG_SIZE = (640, 640)  # (width, height), such as (1280, 736)



# modefile the things there
CLASSES = ("person", "bicycle", "car","motorbike ","aeroplane ","bus ","train","truck ","boat","traffic light",
           "fire hydrant","stop sign ","parking meter","bench","bird","cat","dog ","horse ","sheep","cow","elephant",
           "bear","zebra ","giraffe","backpack","umbrella","handbag","tie","suitcase","frisbee","skis","snowboard","sports ball","kite",
           "baseball bat","baseball glove","skateboard","surfboard","tennis racket","bottle","wine glass","cup","fork","knife ",
           "spoon","bowl","banana","apple","sandwich","orange","broccoli","carrot","hot dog","pizza ","donut","cake","chair","sofa",
           "pottedplant","bed","diningtable","toilet ","tvmonitor","laptop	","mouse	","remote ","keyboard ","cell phone","microwave ",
           "oven ","toaster","sink","refrigerator ","book","clock","vase","scissors ","teddy bear ","hair drier", "toothbrush ")

coco_id_list = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 27, 28, 31, 32, 33, 34,
         35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63,
         64, 65, 67, 70, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 84, 85, 86, 87, 88, 89, 90]


def filter_boxes(boxes, box_confidences, box_class_probs):
    """Filter boxes with object threshold.
    """
    box_confidences = box_confidences.reshape(-1)
    class_max_score = np.max(box_class_probs, axis=-1)
    classes = np.argmax(box_class_probs, axis=-1)

    _class_pos = np.where(class_max_score* box_confidences >= OBJ_THRESH)
    scores = (class_max_score* box_confidences)[_class_pos]

    boxes = boxes[_class_pos]
    classes = classes[_class_pos]

    return boxes, classes, scores

def nms_boxes(boxes, scores):
    """Suppress non-maximal boxes.
    # Returns
        keep: ndarray, index of effective boxes.
    """
    x = boxes[:, 0]
    y = boxes[:, 1]
    w = boxes[:, 2] - boxes[:, 0]
    h = boxes[:, 3] - boxes[:, 1]

    areas = w * h
    order = scores.argsort()[::-1]

    keep = []
    while order.size > 0:
        i = order[0]
        keep.append(i)

        xx1 = np.maximum(x[i], x[order[1:]])
        yy1 = np.maximum(y[i], y[order[1:]])
        xx2 = np.minimum(x[i] + w[i], x[order[1:]] + w[order[1:]])
        yy2 = np.minimum(y[i] + h[i], y[order[1:]] + h[order[1:]])

        w1 = np.maximum(0.0, xx2 - xx1 + 0.00001)
        h1 = np.maximum(0.0, yy2 - yy1 + 0.00001)
        inter = w1 * h1

        ovr = inter / (areas[i] + areas[order[1:]] - inter)
        inds = np.where(ovr <= NMS_THRESH)[0]
        order = order[inds + 1]
    keep = np.array(keep)
    return keep


def box_process(position, anchors):
    grid_h, grid_w = position.shape[2:4]
    col, row = np.meshgrid(np.arange(0, grid_w), np.arange(0, grid_h))
    col = col.reshape(1, 1, grid_h, grid_w)
    row = row.reshape(1, 1, grid_h, grid_w)
    grid = np.concatenate((col, row), axis=1)
    stride = np.array([IMG_SIZE[1]//grid_h, IMG_SIZE[0]//grid_w]).reshape(1,2,1,1)

    col = col.repeat(len(anchors), axis=0)
    row = row.repeat(len(anchors), axis=0)
    anchors = np.array(anchors)
    anchors = anchors.reshape(*anchors.shape, 1, 1)

    box_xy = position[:,:2,:,:]*2 - 0.5
    box_wh = pow(position[:,2:4,:,:]*2, 2) * anchors

    box_xy += grid
    box_xy *= stride
    box = np.concatenate((box_xy, box_wh), axis=1)

    # Convert [c_x, c_y, w, h] to [x1, y1, x2, y2]
    xyxy = np.copy(box)
    xyxy[:, 0, :, :] = box[:, 0, :, :] - box[:, 2, :, :]/ 2  # top left x
    xyxy[:, 1, :, :] = box[:, 1, :, :] - box[:, 3, :, :]/ 2  # top left y
    xyxy[:, 2, :, :] = box[:, 0, :, :] + box[:, 2, :, :]/ 2  # bottom right x
    xyxy[:, 3, :, :] = box[:, 1, :, :] + box[:, 3, :, :]/ 2  # bottom right y

    return xyxy

def post_process(input_data, anchors):
    boxes, scores, classes_conf = [], [], []
    # 1*255*h*w -> 3*85*h*w
    input_data = [_in.reshape([len(anchors[0]),-1]+list(_in.shape[-2:])) for _in in input_data]
    for i in range(len(input_data)):
        boxes.append(box_process(input_data[i][:,:4,:,:], anchors[i]))
        scores.append(input_data[i][:,4:5,:,:])
        classes_conf.append(input_data[i][:,5:,:,:])

    def sp_flatten(_in):
        ch = _in.shape[1]
        _in = _in.transpose(0,2,3,1)
        return _in.reshape(-1, ch)

    boxes = [sp_flatten(_v) for _v in boxes]
    classes_conf = [sp_flatten(_v) for _v in classes_conf]
    scores = [sp_flatten(_v) for _v in scores]

    boxes = np.concatenate(boxes)
    classes_conf = np.concatenate(classes_conf)
    scores = np.concatenate(scores)

    # filter according to threshold
    boxes, classes, scores = filter_boxes(boxes, scores, classes_conf)

    # nms
    nboxes, nclasses, nscores = [], [], []

    for c in set(classes):
        inds = np.where(classes == c)
        b = boxes[inds]
        c = classes[inds]
        s = scores[inds]
        keep = nms_boxes(b, s)

        if len(keep) != 0:
            nboxes.append(b[keep])
            nclasses.append(c[keep])
            nscores.append(s[keep])

    if not nclasses and not nscores:
        return None, None, None

    boxes = np.concatenate(nboxes)
    classes = np.concatenate(nclasses)
    scores = np.concatenate(nscores)

    return boxes, classes, scores


def draw(image, boxes, scores, classes):
    for box, score, cl in zip(boxes, scores, classes):
        top, left, right, bottom = [int(_b) for _b in box]
        print("%s @ (%d %d %d %d) %.3f" % (CLASSES[cl], top, left, right, bottom, score))
        cv2.rectangle(image, (top, left), (right, bottom), (255, 0, 0), 2)
        cv2.putText(image, '{0} {1:.2f}'.format(CLASSES[cl], score),
                    (top, left - 6), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 0, 255), 2)

def setup_model(args):
    model_path = args.model_path
    if model_path.endswith('.pt') or model_path.endswith('.torchscript'):
        platform = 'pytorch'
        from py_utils.pytorch_executor import Torch_model_container
        model = Torch_model_container(args.model_path)
    elif model_path.endswith('.rknn'):
        platform = 'rknn'
        from py_utils.rknn_executor import RKNN_model_container 
        model = RKNN_model_container(args.model_path, args.target, args.device_id)
    elif model_path.endswith('onnx'):
        platform = 'onnx'
        from py_utils.onnx_executor import ONNX_model_container
        model = ONNX_model_container(args.model_path)
    else:
        assert False, "{} is not rknn/pytorch/onnx model".format(model_path)
    print('Model-{} is {} model, starting val'.format(model_path, platform))
    return model, platform

def img_check(path):
    img_type = ['.jpg', '.jpeg', '.png', '.bmp']
    for _type in img_type:
        if path.endswith(_type) or path.endswith(_type.upper()):
            return True
    return False

class Detect:
    def __init__(self) -> None:
        # 初始化摄像头
        self.cap = cv2.VideoCapture(0)

        parser = argparse.ArgumentParser(description='Process some integers.')
        # basic params
        parser.add_argument('--model_path', type=str, default= "/home/iflytek/ucar_ws/src/ucar_nav/scripts/scripts/1.onnx",  help='model path, could be .pt or .rknn file')
        parser.add_argument('--target', type=str, default='rk3588', help='target RKNPU platform')
        parser.add_argument('--device_id', type=str, default=None, help='device id')
        
        parser.add_argument('--img_show', action='store_true', default=False, help='draw the result and show')
        parser.add_argument('--img_save', action='store_true', default=False, help='save the result')

        # data params
        parser.add_argument('--anno_json', type=str, default='../../../datasets/COCO/annotations/instances_val2017.json', help='coco annotation path')
        # coco val folder: '../../../datasets/COCO//val2017'
        parser.add_argument('--img_folder', type=str, default='../model', help='img folder path')
        parser.add_argument('--coco_map_test', action='store_true', help='enable coco map test')
        parser.add_argument('--anchors', type=str, default='/home/iflytek/ucar_ws/src/ucar_nav/scripts/scripts/anchors_yolov5.txt', help='target to anchor file, only yolov5, yolov7 need this param')

        args = parser.parse_args()

        # load anchor
        with open(args.anchors, 'r') as f:
            values = [float(_v) for _v in f.readlines()]
            self.anchors = np.array(values).reshape(3,-1,2).tolist()
        print("use anchors from '{}', which is {}".format(args.anchors, self.anchors))
        
        # init model
        self.model, self.platform = setup_model(args)

        file_list = sorted(os.listdir(args.img_folder))
        img_list = []
        for path in file_list:
            if img_check(path):
                img_list.append(path)
        self.co_helper = COCO_test_helper(enable_letter_box=True)

        # run test
        for i in range(len(img_list)):
            print('infer {}/{}'.format(i+1, len(img_list)), end='\r')

            img_name = img_list[i]
            img_path = os.path.join(args.img_folder, img_name)
            if not os.path.exists(img_path):
                print("{} is not found", img_name)
                continue


    def get_data(self):
        ret, img_src = self.cap.read() 
        # 在这里开始魔改
        if not ret:
            return None
        # Due to rga init with (0,0,0), we using pad_color (0,0,0) instead of (114, 114, 114)
        pad_color = (0,0,0)
        img = self.co_helper.letter_box(im= img_src.copy(), new_shape=(IMG_SIZE[1], IMG_SIZE[0]), pad_color=(0,0,0))
        img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
        # preprocee if not rknn model
        if self.platform in ['pytorch', 'onnx']:
            input_data = img.transpose((2,0,1))
            input_data = input_data.reshape(1,*input_data.shape).astype(np.float32)
            input_data = input_data/255.
        else:
            input_data = img

        outputs = self.model.run([input_data])
        boxes, classes, scores = post_process(outputs, self.anchors)
        print(boxes,classes,scores)
        return boxes, classes, scores
    def free_cam(self):
        self.cap.release()
        return 1
if __name__ == "__main__":
    det = Detect()
    while True:
        det.get_data()
        

sudo modprobe rknpu