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npu端基本测试

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songhaoyu
2026-05-21 16:53:31 +08:00
parent 0ec79c9781
commit 719d4081e8
10 changed files with 430 additions and 64 deletions
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.idea/.gitignore
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# 默认忽略的文件
/shelf/
/workspace.xml
# 基于编辑器的 HTTP 客户端请求
/httpRequests/
.idea/inspectionProfiles/Project_Default.xml
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<component name="InspectionProjectProfileManager">
<profile version="1.0">
<option name="myName" value="Project Default" />
<inspection_tool class="PyPep8NamingInspection" enabled="true" level="WEAK WARNING" enabled_by_default="true">
<option name="ignoredErrors">
<list>
<option value="N801" />
</list>
</option>
</inspection_tool>
<inspection_tool class="PyStubPackagesAdvertiser" enabled="true" level="WARNING" enabled_by_default="true">
<option name="ignoredPackages">
<list>
<option value="scipy" />
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</option>
</inspection_tool>
<inspection_tool class="PyUnresolvedReferencesInspection" enabled="true" level="WARNING" enabled_by_default="true">
<option name="ignoredIdentifiers">
<list>
<option value="types.NoneType.*" />
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</profile>
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.idea/inspectionProfiles/profiles_settings.xml
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<component name="InspectionProjectProfileManager">
<settings>
<option name="USE_PROJECT_PROFILE" value="false" />
<version value="1.0" />
</settings>
</component>
.idea/misc.xml
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<?xml version="1.0" encoding="UTF-8"?>
<project version="4">
<component name="Black">
<option name="sdkName" value="D:\program file\conda\envs\pytorch" />
</component>
<component name="ProjectRootManager" version="2" project-jdk-name="D:\program file\conda\envs\pytorch" project-jdk-type="Python SDK" />
</project>
.idea/modules.xml
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<?xml version="1.0" encoding="UTF-8"?>
<project version="4">
<component name="ProjectModuleManager">
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<module fileurl="file://$PROJECT_DIR$/.idea/opi-yolo.iml" filepath="$PROJECT_DIR$/.idea/opi-yolo.iml" />
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.idea/opi-yolo.iml
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<?xml version="1.0" encoding="UTF-8"?>
<module type="PYTHON_MODULE" version="4">
<component name="NewModuleRootManager">
<content url="file://$MODULE_DIR$" />
<orderEntry type="jdk" jdkName="D:\program file\conda\envs\pytorch" jdkType="Python SDK" />
<orderEntry type="sourceFolder" forTests="false" />
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<component name="PyDocumentationSettings">
<option name="format" value="PLAIN" />
<option name="myDocStringFormat" value="Plain" />
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<?xml version="1.0" encoding="UTF-8"?>
<project version="4">
<component name="VcsDirectoryMappings">
<mapping directory="" vcs="Git" />
<mapping directory="$PROJECT_DIR$" vcs="Git" />
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</project>
infer.py
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import os
import cv2
import numpy as np
import argparse
from ais_bench.infer.interface import InferSession
# 导入昇腾核心推理库（板子自带）
from acllite_model import AclLiteModel
from acllite_resource import AclLiteResource
class YOLO26Infer:
def __init__(self, model_path, device_id=0):
"""
初始化推理器
:param model_path: .om 模型的本地路径
:param device_id: NPU 设备ID
"""
self.device_id = device_id
self.model_path = model_path
# 创建 ais_bench 推理会话
self.session = InferSession(device_id, model_path)
# 获取模型输入尺寸，用于后续预处理
self.input_width = 640
self.input_height = 640
def preprocess(img_path, model_w=640, model_h=640):
"""图片预处理：缩放、通道转换、归一化"""
orig_img = cv2.imread(img_path)
h, w, _ = orig_img.shape
# 1. 缩放到模型要求的 640x640
img = cv2.resize(orig_img, (model_w, model_h))
# 2. BGR 转 RGB (YOLO 训练时通常是 RGB)
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
# 3. 归一化并调整维度为 [1, 3, 640, 640]
img = img.astype(np.float32) / 255.0
img = img.transpose(2, 0, 1) # HWC -> CHW
img = np.expand_dims(img, axis=0) # CHW -> NCHW
# 必须将其转换为连续的内存，方便 NPU 读取
return np.ascontiguousarray(img), orig_img
def letterbox(self, img, new_shape=(640, 640), color=(114, 114, 114)):
shape = img.shape[:2]
r = min(new_shape[0] / shape[0], new_shape[1] / shape[1])
# 计算缩放后的新尺寸（四舍五入）
new_unpad = (int(round(shape[1] * r)), int(round(shape[0] * r)))
# 计算需要填充的总宽度和高度
dw = new_shape[1] - new_unpad[0]
dh = new_shape[0] - new_unpad[1]
# 分别计算左右/上下填充量（允许奇数不对称）
left = dw // 2
right = dw - left
top = dh // 2
bottom = dh - top
def main():
# 1. 初始化昇腾 NPU 资源
acl_resource = AclLiteResource()
acl_resource.init()
# 缩放图片
if shape[::-1] != new_unpad:
img = cv2.resize(img, new_unpad, interpolation=cv2.INTER_LINEAR)
# 2. 加载你的 YOLO26 .om 模型
model_path = "./yolo26_8t.om"
model = AclLiteModel(model_path)
# 添加填充（可能左右或上下不对称，但总尺寸正好是 new_shape）
img = cv2.copyMakeBorder(img, top, bottom, left, right, cv2.BORDER_CONSTANT, value=color)
return img, r, (left, top) # 返回填充的左上角偏移量
# 3. 图像预处理
input_data, orig_img = preprocess("./test.jpg")
def preprocess(self, image_path):
"""
预处理图片，使其符合模型输入要求
"""
# 使用OpenCV读取图片 (BGR格式)
img_bgr = cv2.imread(image_path)
if img_bgr is None:
raise FileNotFoundError(f"图片未找到: {image_path}")
# 4. 送入 NPU 进行硬件加速推理
# execute 接收一个列表，里面是模型的输入 Tensor
result_list = model.execute([input_data])
# 获取原始图片尺寸，用于后处理时的坐标转换
self.original_height, self.original_width = img_bgr.shape[:2]
# 5. 后处理（针对 YOLO26 的端到端特性）
# YOLO26 输出的直接是最终预测框，result_list[0] 形状通常是 [1, 300, 6] 或类似
# 6个维度的含义一般为：[x_min, y_min, x_max, y_max, score, class_id]
predictions = result_list[0][0]
# 将图片从BGR转换为RGB
img_rgb = cv2.cvtColor(img_bgr, cv2.COLOR_BGR2RGB)
CONF_THRESHOLD = 0.25 # 设定置信度阈值
orig_h, orig_w = orig_img.shape[:2]
# 调整图片尺寸并保持宽高比，进行Letterbox填充
img_resized, self.ratio, (self.pad_left, self.pad_top) = self.letterbox(
img_rgb, (self.input_width, self.input_height)
)
box_count = 0
for pred in predictions:
# 取出置信度和类别
score = pred[4]
class_id = int(pred[5])
# 过滤掉低置信度的框
if score > CONF_THRESHOLD:
box_count += 1
# 取出缩放后的坐标 [0~640]
x1, y1, x2, y2 = pred[0:4]
# 将坐标还原到原图尺寸
x1 = int(x1 * orig_w / 640)
y1 = int(y1 * orig_h / 640)
x2 = int(x2 * orig_w / 640)
y2 = int(y2 * orig_h / 640)
# 在原图上画框和标签
cv2.rectangle(orig_img, (x1, y1), (x2, y2), (0, 255, 0), 2)
cv2.putText(orig_img, f"Class {class_id}: {score:.2f}", (x1, y1 - 10),
cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)
# 归一化：将像素值从 [0, 255] 转为 [0, 1]
img_input = (img_resized.astype(np.float32) / 255.0).astype(np.float32)
print(f"推理完成，共检测到 {box_count} 个目标。")
# 6. 保存检测结果图
cv2.imwrite("./result.jpg", orig_img)
# 调整维度：HWC -> NCHW，并转换为连续数组
img_input = np.ascontiguousarray(np.transpose(img_input, (2, 0, 1))[np.newaxis, :, :, :])
return img_input
def infer(self, img_input):
"""
执行推理
"""
# 使用会话的 infer 方法进行推理
# infer 方法接收一个列表，包含输入张量，返回一个列表，包含输出张量
result = self.session.infer([img_input])
print(f"Input tensor shape: {img_input.shape}")
print(f"Input tensor dtype: {img_input.dtype}")
print(f"Input tensor size: {img_input.nbytes}")
return result
def postprocess(self, infer_result, conf_threshold=0.5):
"""
对模型输出进行后处理
:param infer_result: 模型原始输出，一个列表，通常第一个元素是输出张量
:param conf_threshold: 置信度阈值
:return: 包含检测结果的列表，每个元素为 [x1, y1, x2, y2, confidence, class_id]
"""
detections = []
# 获取输出张量，假设输出列表的第一个元素是输出数组
if isinstance(infer_result, (list, tuple)):
output = infer_result[0]
else:
output = infer_result
# ========== 调试信息 ==========
# print(f"[DEBUG] output shape: {output.shape}")
# print(f"[DEBUG] output dtype: {output.dtype}")
# print(f"[DEBUG] output min: {output.min()}, max: {output.max()}")
# # 打印前5个检测框（如果形状是 [N,6] 或 [1,N,6]）
# if output.ndim == 3:
# flat_output = output[0]
# else:
# flat_output = output
# print(f"[DEBUG] first 5 rows:\n{flat_output[:5]}")
# 假设输出维度为 [batch, num_detections, 6]
# 移除batch维度，并只取第一个batch的结果
if output.ndim == 3:
output = output[0]
# 遍历所有检测框
for det in output:
confidence = det[4]
if confidence < conf_threshold:
continue
# 获取原始输出坐标 (x1, y1, x2, y2) 和 类别ID
x1_scale, y1_scale, x2_scale, y2_scale = det[0], det[1], det[2], det[3]
cls_id = int(det[5])
# 将坐标从模型输入尺寸映射回原始图片尺寸
x1 = (x1_scale - self.pad_left) / self.ratio
y1 = (y1_scale - self.pad_top) / self.ratio
x2 = (x2_scale - self.pad_left) / self.ratio
y2 = (y2_scale - self.pad_top) / self.ratio
# 边界限制，防止超出原始图片范围
x1 = max(0, min(x1, self.original_width))
y1 = max(0, min(y1, self.original_height))
x2 = max(0, min(x2, self.original_width))
y2 = max(0, min(y2, self.original_height))
detections.append([x1, y1, x2, y2, confidence, cls_id])
return detections
def draw_boxes(image, detections, class_names=None):
"""
在图片上绘制检测框和标签
:param image: 原始图片数组 (BGR格式)
:param detections: 后处理得到的结果列表
:param class_names: 类别名称列表，默认使用类别ID
"""
img_copy = image.copy()
for det in detections:
x1, y1, x2, y2, conf, cls_id = det
x1, y1, x2, y2 = int(x1), int(y1), int(x2), int(y2)
# 绘制矩形框
cv2.rectangle(img_copy, (x1, y1), (x2, y2), (0, 255, 0), 2)
# 构建标签文本
label = f"Class: {cls_id}" if class_names is None else class_names[cls_id]
label += f" {conf:.2f}"
# 设置标签背景
(label_w, label_h), _ = cv2.getTextSize(label, cv2.FONT_HERSHEY_SIMPLEX, 0.5, 1)
cv2.rectangle(img_copy, (x1, y1 - label_h - 5), (x1 + label_w, y1), (0, 255, 0), -1)
cv2.putText(img_copy, label, (x1, y1 - 5), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 0), 1)
return img_copy
if __name__ == "__main__":
main()
parser = argparse.ArgumentParser(description="YOLOv26 目标检测推理")
parser.add_argument("--model", type=str, required=True, help="YOLOv26 OM模型文件路径")
parser.add_argument("--image", type=str, required=True, help="输入图片路径")
parser.add_argument("--conf", type=float, default=0.5, help="置信度阈值")
args = parser.parse_args()
# 初始化推理器
yolo_infer = YOLO26Infer(args.model)
# 图片预处理
print("预处理图片...")
input_tensor = yolo_infer.preprocess(args.image)
# 执行推理
print("执行推理...")
raw_result = yolo_infer.infer(input_tensor)
# 后处理
print("后处理...")
detections = yolo_infer.postprocess(raw_result, conf_threshold=args.conf)
# 打印结果
print(f"检测到 {len(detections)} 个目标:")
for det in detections:
print(f"类别ID: {int(det[5])}, 置信度: {det[4]:.4f}, 边界框: ({det[0]:.2f}, {det[1]:.2f}, {det[2]:.2f}, {det[3]:.2f})")
# 可选：绘制结果并保存
original_img = cv2.imread(args.image)
result_img = draw_boxes(original_img, detections)
output_path = "output_" + os.path.basename(args.image)
cv2.imwrite(output_path, result_img)
print(f"结果图片已保存至: {output_path}")
mav-test.py
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import cv2
import math
import time
import serial
import numpy as np
from ais_bench.infer.interface import InferSession
from pymavlink import mavutil
class DroneDetector:
def __init__(self, model_path, serial_port='/dev/ttyAMA0', baudrate=57600):
self.session = InferSession(0, model_path)
self.input_size = 640
# 串口/MAVLink
self.serial_port = serial_port
self.baudrate = baudrate
self.mav = None
self.init_serial()
def init_serial(self):
"""初始化串口（如果用于自定义协议）或 MAVLink 连接"""
try:
self.mav = mavutil.mavlink_connection(self.serial_port, baud=self.baudrate)
print(f"MAVLink 已连接到 {self.serial_port} 波特率 {self.baudrate}")
except Exception as e:
print(f"串口打开失败: {e}")
self.mav = None
def letterbox(self, img, color=(114, 114, 114)):
shape = img.shape[:2]
r = min(self.input_size / shape[0], self.input_size / shape[1])
new_unpad = (int(round(shape[1] * r)), int(round(shape[0] * r)))
dw = self.input_size - new_unpad[0]
dh = self.input_size - new_unpad[1]
left = dw // 2
right = dw - left
top = dh // 2
bottom = dh - top
img = cv2.resize(img, new_unpad, interpolation=cv2.INTER_LINEAR)
img = cv2.copyMakeBorder(img, top, bottom, left, right, cv2.BORDER_CONSTANT, value=color)
return img, r, (left, top)
def preprocess(self, img):
img_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
img_resized, self.ratio, (self.pad_left, self.pad_top) = self.letterbox(img_rgb)
img_input = img_resized.astype(np.float32) / 255.0
img_input = np.ascontiguousarray(np.transpose(img_input, (2, 0, 1))[np.newaxis, ...])
return img_input
def postprocess(self, output, conf_thresh=0.5):
detections = []
if output.ndim == 3:
output = output[0]
for det in output:
raw_conf = det[4]
conf = 1.0 / (1.0 + math.exp(-raw_conf))
if conf < conf_thresh:
continue
x1, y1, x2, y2 = det[0], det[1], det[2], det[3]
# 映射回原图坐标
x1 = (x1 - self.pad_left) / self.ratio
y1 = (y1 - self.pad_top) / self.ratio
x2 = (x2 - self.pad_left) / self.ratio
y2 = (y2 - self.pad_top) / self.ratio
cls_id = int(det[5])
detections.append({
'bbox': (x1, y1, x2, y2),
'confidence': conf,
'class': cls_id,
'center': ((x1 + x2) / 2, (y1 + y2) / 2)
})
return detections
def send_mavlink_obstacle(self, angle_deg, distance_m):
"""发送 DISTANCE_SENSOR 消息，表示目标方向与距离"""
if not self.mav:
return
self.mav.mav.distance_sensor_send(
0, # time_boot_ms
0, # min_distance cm
10000, # max_distance cm
int(distance_m * 100), # current_distance cm
0, # type (0=laser)
0, # id
int(angle_deg / 360.0 * 65535), # orientation (0~360° -> 0~65535)
0 # covariance
)
def send_custom_serial(self, detections):
"""通过串口发送自定义格式的数据（例如 JSON）"""
if not self.serial or not self.serial.is_open:
return
import json
data = {
'timestamp': time.time(),
'detections': detections
}
self.serial.write((json.dumps(data) + '\n').encode())
def detect_and_send(self, frame, send_method='mavlink', conf_thresh=0.5):
"""对单帧图像进行检测，并发送结果"""
input_tensor = self.preprocess(frame)
outputs = self.session.infer([input_tensor])
detections = self.postprocess(outputs[0], conf_thresh)
if not detections:
return detections
# 假设只发送置信度最高的那个目标
best = detections[0]
if send_method == 'mavlink':
# 计算角度和距离（需要根据相机参数和实际尺寸估算）
# 这里简单示例：根据目标在图像中的偏移量估算角度
cx = best['center'][0]
img_w = frame.shape[1]
angle = (cx - img_w / 2) / (img_w / 2) * 45 # 水平视场角90°，偏移量映射为±45°
# 距离估算：根据目标在图像中的高度（假设目标实际高度1m）
# 需要相机焦距等，简化：使用固定值10米
distance = 10.0
self.send_mavlink_obstacle(angle, distance)
elif send_method == 'custom':
self.send_custom_serial(detections)
return detections
def run_on_camera(self, camera_id=0, conf_thresh=0.5, send_method='mavlink'):
cap = cv2.VideoCapture(camera_id)
if not cap.isOpened():
print("摄像头打开失败")
return
print("开始检测，按 q 退出")
while True:
ret, frame = cap.read()
if not ret:
break
detections = self.detect_and_send(frame, send_method, conf_thresh)
# 绘制检测框（调试）
for det in detections:
x1, y1, x2, y2 = map(int, det['bbox'])
cv2.rectangle(frame, (x1, y1), (x2, y2), (0, 255, 0), 2)
cv2.putText(frame, f"{det['class']}:{det['confidence']:.2f}", (x1, y1 - 5),
cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)
cv2.imshow("Detection", frame)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
cap.release()
cv2.destroyAllWindows()
def run_on_image(self, image_path, conf_thresh=0.5, send_method='mavlink'):
frame = cv2.imread(image_path)
if frame is None:
print("图片读取失败")
return
detections = self.detect_and_send(frame, send_method, conf_thresh)
print(f"检测到 {len(detections)} 个目标")
for d in detections:
print(d)
# 显示结果
for det in detections:
x1, y1, x2, y2 = map(int, det['bbox'])
cv2.rectangle(frame, (x1, y1), (x2, y2), (0, 255, 0), 2)
cv2.putText(frame, f"{det['class']}:{det['confidence']:.2f}", (x1, y1 - 5),
cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)
cv2.imshow("Detection", frame)
cv2.waitKey(0)
cv2.destroyAllWindows()
if __name__ == "__main__":
MODEL_PATH = "./om/yolo26n.om"
SERIAL_PORT = "/dev/ttyAMA0" # 根据实际修改
BAUD = 57600
detector = DroneDetector(MODEL_PATH, SERIAL_PORT, BAUD)
# 选择模式：摄像头或单张图片
# detector.run_on_camera(camera_id=0, conf_thresh=0.5, send_method='mavlink')
detector.run_on_image("test.jpg", conf_thresh=0.5, send_method='mavlink')
test.jpg
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