手写数字体识别，用保存的模型跑自己的图片

原文博客：https://blog.csdn.net/X_m_w/article/details/101056156
模型训练：https://blog.csdn.net/X1996_/article/details/108883710
这里是用训练的模型直接来预测，模型训练可参考上篇文章
主要是用opencv把图片处理成需要的图片格式，然后预测
输入图片：
输出：

import cv2
import numpy as np
from keras import models
# 反相灰度图，将黑白阈值颠倒


def accessPiexl(img):
    height = img.shape[0]
    width = img.shape[1]
    for i in range(height):
       for j in range(width):
           img[i][j] = 255 - img[i][j]
    return img


# 反相二值化图像
def accessBinary(img, threshold=128):
    img = accessPiexl(img)
    # 边缘膨胀，不加也可以
    kernel = np.ones((3, 3), np.uint8)
    img = cv2.dilate(img, kernel, iterations=1)
    _, img = cv2.threshold(img, threshold, 0, cv2.THRESH_TOZERO)
    return img


# 利用轮廓找位置，不需要行对齐
def findBorderContours(path, maxArea=50):
    img = cv2.imread(path, cv2.IMREAD_GRAYSCALE)
    img = accessBinary(img)
    _, contours, _ = cv2.findContours(img, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
    borders = []
    for contour in contours:
        # 将边缘拟合成一个边框
        x, y, w, h = cv2.boundingRect(contour)
        if w*h > maxArea:
            border = [(x, y), (x+w, y+h)]
            borders.append(border)
    return borders


# 根据边框转换为MNIST格式
def transMNIST(path, borders, size=(28, 28)):
    imgData = np.zeros((len(borders), size[0], size[0], 1), dtype='uint8')
    img = cv2.imread(path, cv2.IMREAD_GRAYSCALE)
    img = accessBinary(img)
    for i, border in enumerate(borders):
        borderImg = img[border[0][1]:border[1][1], border[0][0]:border[1][0]]
        # 根据最大边缘拓展像素，扩大边缘边框，固定值0填充，变成正方形
        # top, bottom, left, right：上下左右要扩展的像素数
        extendPiexl = (max(borderImg.shape) - min(borderImg.shape)) // 2
        # 扩大多少由你的原图数字大小决定，使数字处在图片中间，图片越大，扩大的就大一些
        targetImg = cv2.copyMakeBorder(borderImg, 30, 30, extendPiexl + 30, extendPiexl + 30, cv2.BORDER_CONSTANT)
        targetImg = cv2.resize(targetImg, size)
        # 增加一个维度 28*28*1，我是这样理解的，相当于reshape吧
        targetImg = np.expand_dims(targetImg, axis=-1)
        imgData[i] = targetImg
    return imgData


# 预测手写数字
def predict(modelpath, imgData):
    model = models.load_model(modelpath)
    img = imgData.astype('float32') / 255
    results = model.predict(img)
    result_number = []
    for result in results:
        result_number.append(np.argmax(result))
    return result_number


# 显示结果及边框
def showResults(path, borders, results=None):
    img = cv2.imread(path)
    for i, border in enumerate(borders):
        cv2.rectangle(img, border[0], border[1], (0, 0, 255))
        if results:
            cv2.putText(img, str(results[i]), border[0], cv2.FONT_HERSHEY_COMPLEX, 0.8, (0, 255, 0), 1)
    cv2.imshow('test', img)


path = '11.png'
model = 'model.h5'
borders = findBorderContours(path)
imgData = transMNIST(path, borders)
results = predict(model, imgData)
showResults(path, borders, results)

cv2.waitKey(0)