opencv摄像头识别手写数字的代码

Sure,以下是使用OpenCV和Python实现手写数字识别的代码。

import cv2
import numpy as np
from keras.models import load_model

model = load_model('mnist.h5')
cap = cv2.VideoCapture(0)

while True:
    _, frame = cap.read()
    frame = cv2.flip(frame, 1)
    roi = frame[100:400, 100:400]
    cv2.rectangle(frame, (100, 100), (400, 400), (0, 255, 0), 0)
    hsv = cv2.cvtColor(roi, cv2.COLOR_BGR2HSV)

    # Set threshold for blue color
    lower_blue = np.array([0, 48, 80], dtype=np.uint8)
    upper_blue = np.array([20, 255, 255], dtype=np.uint8)

    # Create a binary image with blue color detected
    mask = cv2.inRange(hsv, lower_blue, upper_blue)

    # Find contours in the binary image
    contours, _ = cv2.findContours(mask, cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE)

    # Find the contour with maximum area
    if len(contours) > 0:
        c = max(contours, key=cv2.contourArea)

        # Create bounding rectangle around the contour
        x, y, w, h = cv2.boundingRect(c)

        # Draw the rectangle box in green color
        cv2.rectangle(roi, (x, y), (x + w, y + h), (0, 255, 0), 2)

        # Crop the digit from roi
        digit = roi[y:y + h, x:x + w]

        # Resize the cropped digit to 28x28 pixels
        digit = cv2.resize(digit, (28, 28))

        # Convert cropped image to grayscale
        digit = cv2.cvtColor(digit, cv2.COLOR_BGR2GRAY)

        # Apply binary threshold to get black and white image
        _, digit = cv2.threshold(digit, 30, 255, cv2.THRESH_BINARY)

        # Reshape the digit from 28x28 to 1x784
        digit = digit.reshape(1, 784)

        # Normalize the digit
        digit = digit / 255.0

        # Predict the class of digit
        prediction = model.predict_classes(digit)

        # Write the prediction on the image
        font = cv2.FONT_HERSHEY_SIMPLEX
        cv2.putText(frame, str(prediction[0]), (450, 100), font, 2, (0, 0, 255), 3, cv2.LINE_AA)

    cv2.imshow("frame", frame)

    # Wait for 'q' key to exit
    if cv2.waitKey(1) == ord('q'):
        break

cap.release()
cv2.destroyAllWindows()

这个程序使用计算机的摄像头来捕捉数字图像，然后使用OpenCV来处理这些图像，以便可以通过神经网络模型进行分类。 它使用Python编程语言和Keras深度学习框架来构建神经网络模型，并且可以通过更改其参数来进一步提高它的准确性。

希望能够帮助你解决问题！