使用Begin/End将下面的python代码转换成伪代码 h, w = img.shape[:2] pixelSequence = img.reshape([h * w, ]) # [262144,] for i in range(len(pixelSequence)): if 0 < pixelSequence[i] < Max: pixelSequence[i] -= 1 Z = zhi() n = 0 for i in range(len(pixelSequence)): if pixelSequence[i] == Max: if Hidden_Data[n] == 1 or Hidden_Data[n] == '1': pixelSequence[i] -= 1 else: pass n += 1 if n == len(Hidden_Data): break Marked_Image = pixelSequence.reshape(h, w) return Marked_Image

Begin
h, w = img.shape[:2]
pixelSequence = img.reshape([h * w, ]) # [262144,]
for i in range(len(pixelSequence)):
if 0 < pixelSequence[i] < Max:
pixelSequence[i] -= 1
Z = zhi()
n = 0
for i in range(len(pixelSequence)):
if pixelSequence[i] == Max:
if Hidden_Data[n] == 1 or Hidden_Data[n] == '1':
pixelSequence[i] -= 1
else:
pass
n += 1
if n == len(Hidden_Data):
break
Marked_Image = pixelSequence.reshape(h, w)
return Marked_Image
End

相关问题

手写数字识别python代码 (h, w) = img.shape

好的，以下是一个简单的手写数字识别的 Python 代码：

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

# 加载模型
model = load_model('model.h5')

# 读取图片
img = cv2.imread('test.png')

# 灰度化、二值化
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
_, thresh = cv2.threshold(gray, 120, 255, cv2.THRESH_BINARY_INV)

# 查找轮廓
contours, _ = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)

# 遍历轮廓并识别数字
for cnt in contours:
    x, y, w, h = cv2.boundingRect(cnt)
    if w * h > 100:
        roi = gray[y:y+h, x:x+w]
        roi = cv2.resize(roi, (28, 28), interpolation=cv2.INTER_AREA)
        roi = np.reshape(roi, (1, 28, 28, 1))
        roi = roi.astype('float32') / 255
        result = model.predict(roi)
        digit = np.argmax(result)
        cv2.rectangle(img, (x, y), (x+w, y+h), (0, 255, 0), 2)
        cv2.putText(img, str(digit), (x, y), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 2)

# 显示结果
cv2.imshow('result', img)
cv2.waitKey(0)
cv2.destroyAllWindows()

代码中使用了 OpenCV 进行图片处理，Keras 加载模型进行识别。最后将识别结果绘制在原图上并显示出来。其中 `(h, w) = img.shape` 是用来获取图片的高度和宽度的，可以用来判断图片是否成功读取。

img = img.reshape((img.shape[1], img.shape[2], 3))什么作用

这行代码的作用是将图像的形状从原来的 (batch_size, channels, height, width) 转换为 (height, width, channels)，其中 channels 通常是 RGB 三个颜色通道。这是因为很多图像处理函数都需要输入形状为 (height, width, channels) 的图像。reshape 函数通过重新排列图像的像素，将其在内存中按照新的形状存储。这样可以更方便地对图像进行处理和操作，例如显示、保存、增强等。