本文介绍了正点原子 STM32MP257 开发板实现物体识别的项目设计。
下载 MobileNet 模型，并通过 ST Edge AI 在线工具，将其转化为 nb 模型；
转换过程参考：【正点原子STM32MP257开发板试用】数字识别 .
模型下载：mobilenet - tensorflow .
包括基于 MobileNet 模型实现物体识别的 USB 摄像头动态帧识别和板端推理的静态图片识别。
采用 USB 摄像头获取动态画面，调用 nb 模型实现物体识别。
终端执行 touch mobilenet_camera.py 指令，新建 python 文件，添加如下代码
import os
import cv2
import numpy as np
import time
from stai_mpu import stai_mpu_network
class ObjectDetector:
def __init__(self, model_path):
try:
# 加载模型
self.model = stai_mpu_network(model_path=model_path)
print(f"Model loaded successfully from {model_path}")
# 确定输入形状
self.input_shape = self.determine_input_shape()
print(f"Input shape: {self.input_shape}")
# 加载标签
label_path = os.path.join(os.path.dirname(model_path), "labels_imagenet_2012.txt")
self.class_labels = self.load_labels(label_path)
except Exception as e:
print(f"Error initializing model: {str(e)}")
raise
def determine_input_shape(self):
"""确定模型输入形状"""
# MobileNet通常使用224x224
return (1, 224, 224, 3)
def load_labels(self, label_path):
"""兼容性更好的标签加载方法"""
labels = {}
try:
if os.path.exists(label_path):
with open(label_path, 'r') as f:
for line_num, line in enumerate(f, 1):
line = line.strip()
if not line:
continue
parts = line.split(maxsplit=1)
if len(parts) == 1:
class_id = len(labels)
class_name = parts[0]
else:
try:
class_id = int(parts[0])
class_name = parts[1]
except ValueError:
class_id = len(labels)
class_name = line
labels[class_id] = class_name
print(f"Loaded {len(labels)} labels")
else:
print("Warning: Using default ImageNet labels")
labels = {i: str(i) for i in range(1000)}
except Exception as e:
print(f"Label loading error: {str(e)}")
labels = {i: str(i) for i in range(1000)}
return labels
def preprocess(self, frame):
"""图像预处理"""
# 确保3通道
if len(frame.shape) == 2:
frame = cv2.cvtColor(frame, cv2.COLOR_GRAY2BGR)
# 调整尺寸
img = cv2.resize(frame, (self.input_shape[2], self.input_shape[1]))
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
# 归一化 (适用于int8量化模型)
img = img.astype(np.float32)
img = (img / 127.5) - 1.0
return np.expand_dims(img, axis=0)
def detect(self, frame):
"""执行检测"""
try:
input_data = self.preprocess(frame)
self.model.set_input(0, input_data)
start_time = time.perf_counter()
self.model.run()
inference_time = (time.perf_counter() - start_time) * 1000
output = self.model.get_output(0)
if hasattr(output, 'numpy'):
output = output.numpy()
return output, inference_time
except Exception as e:
print(f"Detection error: {str(e)}")
return None, 0
def main():
# 配置摄像头后端
os.environ['OPENCV_VIDEOIO_PRIORITY_V4L2'] = '1'
# 模型路径
MODEL_DIR = "/home/root/AI_inference/MobileNet/model"
MODEL_NAME = "mobilenet_v2_1.0_224_int8_per_tensor.nb"
MODEL_PATH = os.path.join(MODEL_DIR, MODEL_NAME)
if not os.path.exists(MODEL_PATH):
print(f"Error: Model file not found at {MODEL_PATH}")
return
try:
detector = ObjectDetector(MODEL_PATH)
except Exception as e:
print(f"Failed to initialize detector: {str(e)}")
return
# 打开摄像头
cap = cv2.VideoCapture(7, cv2.CAP_V4L2)
if not cap.isOpened():
print("Error: Could not open camera using V4L2")
return
try:
while True:
ret, frame = cap.read()
if not ret:
print("Camera read failed")
break
# 执行检测
outputs, inference_time = detector.detect(frame)
if outputs is not None:
# 获取top1结果
class_id = np.argmax(outputs)
label = detector.class_labels.get(class_id, str(class_id))
confidence = outputs[0][class_id]
# 显示结果
cv2.putText(frame, f"{label}: {confidence:.2f}",
(20, 40), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)
cv2.putText(frame, f"FPS: {1000/(inference_time+1):.1f}",
(20, 80), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2)
cv2.imshow('Object Detection', frame)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
finally:
cap.release()
cv2.destroyAllWindows()
if __name__ == '__main__':
main()
终端执行 python3 mobilenet_camera.py 输出推理结果
执行程序后，USB 摄像头采集环境画面，芯片执行推理和分析算法程序，将识别结果显示在板载屏幕上。
动态识别效果见顶部视频。
为了更清晰准确地获得识别结果，采用板端推理的方案进行测试。
终端执行 touch mobilenet_object_inference.py 指令，新建 python 文件，添加如下代码
import os
import cv2
import numpy as np
import sys
from stai_mpu import stai_mpu_network
class ObjectDetector:
def __init__(self, model_dir="model"):
# 模型文件路径
self.model_path = os.path.join(model_dir, "mobilenet_v2_1.0_224_int8_per_tensor.nb")
self.label_path = os.path.join(model_dir, "labels_imagenet_2012.txt")
try:
# 加载模型
self.model = stai_mpu_network(model_path=self.model_path)
print(f"[Success] Loaded model: {os.path.basename(self.model_path)}")
# 设置输入尺寸 (MobileNet默认224x224)
self.input_shape = (1, 224, 224, 3)
# 加载标签
self.class_labels = self.load_labels()
except Exception as e:
print(f"[Error] Initialization failed: {str(e)}")
raise
def load_labels(self):
"""加载类别标签"""
labels = {}
try:
if os.path.exists(self.label_path):
with open(self.label_path, 'r') as f:
for line in f:
line = line.strip()
if line:
parts = line.split(maxsplit=1)
if len(parts) == 1:
labels[len(labels)] = parts[0]
else:
try:
labels[int(parts[0])] = parts[1]
except ValueError:
labels[len(labels)] = line
print(f"[Info] Loaded {len(labels)} labels")
else:
print("[Warning] Using default labels (0-999)")
labels = {i: str(i) for i in range(1000)}
except Exception as e:
print(f"[Error] Loading labels: {str(e)}")
labels = {i: str(i) for i in range(1000)}
return labels
def process_image(self, image_path):
"""处理单张图片"""
if not os.path.exists(image_path):
print(f"[Error] Image not found: {image_path}")
return False
# 读取图片
image = cv2.imread(image_path)
if image is None:
print(f"[Error] Cannot read image: {image_path}")
return False
# 预处理
img = cv2.resize(image, (self.input_shape[2], self.input_shape[1]))
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
img = (img.astype(np.float32) / 127.5) - 1.0
input_data = np.expand_dims(img, axis=0)
# 推理
self.model.set_input(0, input_data)
self.model.run()
outputs = self.model.get_output(0)
if hasattr(outputs, 'numpy'):
outputs = outputs.numpy()
# 获取预测结果
class_id = np.argmax(outputs)
confidence = outputs[0][class_id]
label = self.class_labels.get(class_id, str(class_id))
# 在原始图像上添加标签
result = self.add_simple_label(image, label, confidence)
# 保存结果 (与.py文件同目录)
output_name = f"result_{os.path.basename(image_path)}"
cv2.imwrite(output_name, result)
print(f"[Success] Result saved to: {os.path.abspath(output_name)}")
return True
def add_simple_label(self, image, label, confidence):
"""添加简单文本标签"""
label_text = f"{label}: {confidence:.2f}"
# 设置文本样式
font = cv2.FONT_HERSHEY_SIMPLEX
font_scale = 1.0
thickness = 2
color = (0, 0, 255) # 红色文本
# 计算文本位置 (左上角)
(text_w, text_h), _ = cv2.getTextSize(label_text, font, font_scale, thickness)
margin = 10
pos = (margin, margin + text_h)
# 添加文本
cv2.putText(image, label_text, pos, font, font_scale, color, thickness)
return image
def main():
if len(sys.argv) != 2:
print("Usage: python3 detect.py ./model/xxx.jpg")
return
image_path = sys.argv[1]
if not image_path.startswith("./model/"):
print("[Error] Image must be in ./model/ directory")
return
try:
detector = ObjectDetector()
detector.process_image(image_path)
except Exception as e:
print(f"[Fatal Error] {str(e)}")
if __name__ == '__main__':
main()
终端执行指令 python3 mobilenet_object_inference.py ./model/test.jpg 完成后输出测试结果图片并保存至程序目录。
识别结果与实际情况存在误差。
下面介绍官方教程实现物体与图像识别的 Demo 例程。
将 05、开发工具/01、出厂系统交叉编译器 路径下的 atk-image-openstlinux- weston-stm32mp2.rootfs-x86_64-toolchain-5.0.3-snapshot-20250115-v1.x.sh 执行文件复制到 Ubuntu 系统；
依次执行指令
chmod +x atk-image-openstlinux-weston-stm32mp2.rootfs-x86_64-toolchain-5.0.3-snapshot-20250115-v1.0.sh
./atk-image-openstlinux-weston-stm32mp2.rootfs-x86_64-toolchain-5.0.3-snapshot-20250115-v1.0.sh
修改文件权限并执行，期间需要确认默认安装路径、输入系统密码，解压 SDK 等待 5 分钟；
根据安装完成后的提示，每次在新的终端使用编译器，均需要执行指令
source /opt/st/stm32mp2/5.0.3-snapshot/environment-setup-cortexa35-ostl-linux
执行指令 aarch64-ostl-linux-gcc -v 查看编译器版本。
完成工具链交叉编译后，下载图像识别例程 01、程序源码/05、AI例程源码/03、mobilenet 压缩文件；
模型来源：mobilenet - tensorflow .
./aidemo-build-mp257.sh 完成编译；
install 文件夹包含模型和可执行文件以及标签文件；
cd /atk_mobilenet_image_classification
chmod +x start_aidemo.sh setup_camera.sh atk_mobilenet_image_classification
./start_aidemo.sh
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本文介绍了正点原子 STM32MP257 开发板基于 MobileNet 模型实现图像和物体识别的项目设计，包括准备工作、工程调试、动态识别和板端静态推理、程序优化、SDK编译等流程，为该开发板在人工智能等相关领域的开发、产品的设计和应用等方面提供了参考。
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