安装TensorFlow Lite和依赖库
在树莓派上运行以下命令安装TensorFlow Lite和相关依赖库:
bash
sudo apt-get update
sudo apt-get install python3-pip
pip3 install tflite-runtime
pip3 install opencv-python-headless
下载预训练模型
从TensorFlow官方模型库下载适用于TFLite的目标检测模型(如SSD MobileNet):
bash
wget https://storage.googleapis.com/download.tensorflow.org/models/tflite/coco_ssd_mobilenet_v1_1.0_quant_2018_06_29.zip
unzip coco_ssd_mobilenet_v1_1.0_quant_2018_06_29.zip
编写推理代码
创建Python脚本(如detect.py)加载模型并执行推理:
python
import cv2
import numpy as np
import tflite_runtime.interpreter as tflite
# 初始化模型
interpreter = tflite.Interpreter(model_path="detect.tflite")
interpreter.allocate_tensors()
input_details = interpreter.get_input_details()
output_details = interpreter.get_output_details()
# 处理输入图像
def run_inference(image):
input_data = cv2.resize(image, (300, 300))
input_data = np.expand_dims(input_data, axis=0).astype(np.uint8)
interpreter.set_tensor(input_details[0]['index'], input_data)
interpreter.invoke()
boxes = interpreter.get_tensor(output_details[0]['index'])
classes = interpreter.get_tensor(output_details[1]['index'])
scores = interpreter.get_tensor(output_details[2]['index'])
return boxes, classes, scores
实时摄像头检测
添加摄像头捕获和结果可视化代码:
python
cap = cv2.VideoCapture(0)
while True:
ret, frame = cap.read()
boxes, classes, scores = run_inference(frame)
# 绘制检测结果
for i in range(len(scores[0])):
if scores[0][i] > 0.5:
ymin, xmin, ymax, xmax = boxes[0][i]
cv2.rectangle(frame,
(int(xmin*frame.shape[1]), int(ymin*frame.shape[0])),
(int(xmax*frame.shape[1]), int(ymax*frame.shape[0])),
(0, 255, 0), 2)
cv2.imshow('Detection', frame)
if cv2.waitKey(1) == ord('q'):
break
cap.release()
cv2.destroyAllWindows()
性能优化技巧
启用树莓派GPU加速(需安装libedgetpu):
bash
sudo apt-get install libedgetpu1-std
使用量化模型减少计算量,调整输入分辨率平衡精度与速度。对于多线程处理,可结合Python的multiprocessing模块。
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模型自定义训练
如需使用自定义数据集:
- 使用TensorFlow Model Garden训练模型
- 通过TFLite Converter导出为.tflite格式
- 修改标签文件适配新类别
训练命令示例:
bash
python object_detection/model_main.py \
--pipeline_config_path=ssd_mobilenet_v1_coco.config \
--model_dir=training/ \
--alsologtostderr