---
title: 目标检测模型代码
description: "“python/src/models/yolov5.py”为yolov5模型的定义代码，为小车的基础运行提供核心的智能目标识别与检测功能。1. 示例代码定义了如何重塑图片的尺寸，并计算需要零值填充大小的功能。"
url: https://www.hiascend.com/document/detail/zh/Atlas200IDKA2DeveloperKit/latest/Application%20Cases/ivadg/ivadg_0028.html
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---
# 目标检测模型代码

“python/src/models/yolov5.py”为yolov5模型的定义代码，为小车的基础运行提供核心的智能目标识别与检测功能。1. 示例代码定义了如何重塑图片的尺寸，并计算需要零值填充大小的功能。

```
def letterbox(img, new_shape=(640, 640), color=(114, 114, 114), auto=False, scaleFill=False, scaleup=True):
# Resize image to a 32-pixel-multiple rectangle https://github.com/ultralytics/yolov3/issues/232
shape = img.shape[:2]  # current shape [height, width]
if isinstance(new_shape, int):
new_shape = (new_shape, new_shape)
# Scale ratio (new / old)
r = min(new_shape[0] / shape[0], new_shape[1] / shape[1])
if not scaleup:  # only scale down, do not scale up (for better test mAP)
r = min(r, 1.0)
# Compute padding
ratio = r, r  # width, height ratios
new_unpad = int(round(shape[1] * r)), int(round(shape[0] * r))
dw, dh = new_shape[1] - new_unpad[0], new_shape[0] - new_unpad[1]  # wh padding
if auto:  # minimum rectangle
dw, dh = np.mod(dw, 64), np.mod(dh, 64)  # wh padding
elif scaleFill:  # stretch
dw, dh = 0.0, 0.0
new_unpad = (new_shape[1], new_shape[0])
ratio = new_shape[1] / shape[1], new_shape[0] / shape[0]  # width, height ratios
dw /= 2  # divide padding into 2 sides
dh /= 2
if shape[::-1] != new_unpad:  # resize
img = cv2.resize(img, new_unpad, interpolation=cv2.INTER_LINEAR)
top, bottom = int(round(dh - 0.1)), int(round(dh + 0.1))
left, right = int(round(dw - 0.1)), int(round(dw + 0.1))
img = cv2.copyMakeBorder(img, top, bottom, left, right, cv2.BORDER_CONSTANT, value=color)  # add border
return img, ratio, (dw, dh)
```


2. Yolov5的模型定义，以及推理的实现过程，形成最终的推理结果目标框和对应的类别名称。

```
class
YoloV5
(Model):
def
__init__
(self, model_path, acl_init=True):
super().__init__(model_path, acl_init)
self.neth = 640
self.netw = 640
self.conf_threshold =
0.1
dic = {0: 'left',
1: 'right',
2: 'stop',
3: 'turnaround'}
self.names = ['person', 'sports_ball', 'bicycle', 'motorcycle', 'car', 'bus', 'truck'] * 12
self.object_list = ['person', 'sports_ball', 'bicycle', 'motorcycle', 'car', 'bus', 'truck']
self.names = list(dic.values())
self.object_list = list(dic.values())
def
infer
(self, img_bgr):
imgh, imgw = img_bgr.shape[0], img_bgr.shape[1]
imginfo = np.array([self.neth, self.netw, imgh, imgw], dtype=np.float16)
img_padding = letterbox(img_bgr, new_shape=(self.neth, self.netw))[0]  # padding resize bgr
img = []
img.append(img_padding)
img = np.stack(img, axis=0)
img = img[..., ::-1].transpose(0, 3, 1, 2)  # BGR tp RGB
image_np = np.array(img, dtype=np.float32)
image_np_expanded = image_np /
255.0
img = np.ascontiguousarray(image_np_expanded).astype(np.float16) #将tensor的内存连续排列
result = self.execute([img, imginfo]) #调用推理接口
batch_boxout, boxnum = result
pred_boxes = []
idx = 0
num_det = int(boxnum[idx][0])
bbox = batch_boxout[idx][:num_det * 6].reshape(6, -1).transpose().astype(np.float32)  # 6xN -> Nx6
for
idx, class_id in enumerate(bbox[:, 5]):
obj_name = self.names[int(bbox[idx][5])]
if
not obj_name in self.object_list:
continue
confidence = bbox[idx][4]
if
float(confidence) < self.conf_threshold:
continue
x1 = int(bbox[idx][0])
y1 = int(bbox[idx][1])
x2 = int(bbox[idx][2])
y2 = int(bbox[idx][3])
pred_boxes.append([x1, y1, x2, y2, obj_name, confidence]) #获取推理结果
return
pred_boxes
```