树莓派基于tensorflow的数字识别具体实现代码

以下是基于TensorFlow的树莓派数字识别的示例代码：

import time
import tensorflow as tf
from picamera.array import PiRGBArray
from picamera import PiCamera
import cv2
import numpy as np

# 加载模型
model = tf.keras.models.load_model('model.h5')

# 初始化摄像头
camera = PiCamera()
camera.resolution = (640, 480)
camera.framerate = 32
rawCapture = PiRGBArray(camera, size=(640, 480))
time.sleep(0.1)

# 循环读取摄像头数据
for frame in camera.capture_continuous(rawCapture, format="bgr", use_video_port=True):
    # 获取图像数据并转换为灰度图像
    image = frame.array
    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

    # 对图像进行二值化处理
    _, thresh = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)

    # 使用边缘检测算法检测数字
    contours, _ = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
    for cnt in contours:
        # 计算数字上下左右的坐标
        x, y, w, h = cv2.boundingRect(cnt)
        x1 = int(x - 0.1 * w)
        y1 = int(y - 0.1 * h)
        x2 = int(x + w + 0.1 * w)
        y2 = int(y + h + 0.1 * h)

        # 提取数字图像并缩放为28x28
        digit = thresh[y1:y2, x1:x2]
        digit = cv2.resize(digit, (28, 28))

        # 将图像转换为模型可用的格式
        digit = np.reshape(digit, (1, 28, 28, 1))
        digit = digit.astype('float32')
        digit /= 255

        # 使用模型进行预测
        prediction = model.predict(digit)
        digit_class = np.argmax(prediction)

        # 在图像上标注数字
        cv2.rectangle(image, (x1, y1), (x2, y2), (0, 255, 0), 2)
        cv2.putText(image, str(digit_class), (x, y), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 2)

    # 显示图像并清空缓冲区
    cv2.imshow("Digits", image)
    key = cv2.waitKey(1) & 0xFF
    rawCapture.truncate(0)

    # 如果按下q键则退出程序
    if key == ord("q"):
        break

cv2.destroyAllWindows()