yanty123 / bp- Goto Github PK

from math import *
import warnings
import struct
import math
import os

#from sklearn.preprocessing import OneHotEncoder
#from matplotlib import pyplot as plt
from collections import Counter

import numpy as np
import cv2

warnings.filterwarnings('ignore')

def edge_padding(img):
'''
作用:边缘填充
'''
h, w = img.shape
pad_length = max(h, w) // 2
padded_img = cv2.copyMakeBorder(img, *([pad_length] * 4), cv2.BORDER_CONSTANT, value = [0, 0, 0])
return padded_img

def get_minAreaRect(img,isminst=False):
'''
作用:获取轮廓最小外界矩形
参数:
image:原图片
ismnist：是否为mnist数据集图片
返回值：
最小外界矩形
'''
threshold_value = 0.3 if isminst else 100
cv2.threshold(img, threshold_value, 1, cv2.THRESH_BINARY_INV, img)
img = np.array(img,np.uint8)
b, contours, hierarchy = cv2.findContours(img, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)

rect = cv2.minAreaRect(contours[0])
box = cv2.boxPoints(rect)
box = np.int0(box)
minImg = rotate(img, *box)
return minImg

#由四角坐标截取图片
def rotate(img, pt1, pt2, pt3, pt4):
'''
作用:图片裁剪
参数:
image:原图片
pt1~pt4:四点坐标
返回值：
裁剪后图片
'''
widthRect = math.sqrt((pt4[0] - pt1[0]) ** 2 + (pt4[1] - pt1[1]) ** 2) # 矩形框的宽度
heightRect = math.sqrt((pt1[0] - pt2[0]) ** 2 + (pt1[1] - pt2[1]) **2)
angle = acos((pt4[0] - pt1[0]) / widthRect) * (180 / math.pi) # 矩形框旋转角度
if pt4[1] <= pt1[1]:
angle = -angle
height, width = img.shape[:2]
rotateMat = cv2.getRotationMatrix2D((int(width / 2), int(height / 2)), angle, 1) # 按angle角度旋转图像
heightNew = int(width * math.fabs(sin(math.radians(angle))) + height * math.fabs(cos(math.radians(angle))))
widthNew = int(height * math.fabs(sin(math.radians(angle))) + width * math.fabs(cos(math.radians(angle))))

rotateMat[0, 2] += (widthNew - width) / 2
rotateMat[1, 2] += (heightNew - height) / 2
imgRotation = cv2.warpAffine(img, rotateMat, (widthNew, heightNew), borderValue=(0, 0, 0))
# 旋转后图像的四点坐标
[[pt1[0]], [pt1[1]]] = np.dot(rotateMat, np.array([[pt1[0]], [pt1[1]], [1]]))
[[pt2[0]], [pt2[1]]] = np.dot(rotateMat, np.array([[pt2[0]], [pt2[1]], [1]]))
[[pt3[0]], [pt3[1]]] = np.dot(rotateMat, np.array([[pt3[0]], [pt3[1]], [1]]))
[[pt4[0]], [pt4[1]]] = np.dot(rotateMat, np.array([[pt4[0]], [pt4[1]], [1]]))

# 处理反转的情况
if pt2[1] > pt4[1]:
    pt2[1], pt4[1] = pt4[1], pt2[1]
if pt1[0] > pt3[0]:
    pt1[0], pt3[0] = pt3[0], pt1[0]

imgOut = imgRotation[int(pt2[1]):int(pt4[1]), int(pt1[0]):int(pt3[0])]
return imgOut

def rotate_bound(image, angle):
'''
作用:图片翻转
参数:
image:原图片
angle:翻转角度
返回值：
翻转后图片
'''
(h, w) = image.shape[:2]
(cX, cY) = (w // 2, h // 2)
M = cv2.getRotationMatrix2D((cX, cY), -angle, 1.0)
cos = np.abs(M[0, 0])
sin = np.abs(M[0, 1])
# compute the new bounding dimensions of the image
nW = int((h * sin) + (w * cos))
nH = int((h * cos) + (w * sin))
# adjust the rotation matrix to take into account translation
M[0, 2] += (nW / 2) - cX
M[1, 2] += (nH / 2) - cY
# perform the actual rotation and return the image
img = cv2.warpAffine(image, M, (nW, nH))

return img

def delate(img, size=(4, 4)):
'''
作用:图像膨胀
参数:
image:原图像
size :卷积核大小
'''
'''
cv2.MORPH_RECT: 矩形结构
cv2.MORPH_ELLIPSE: 椭圆结构
cv2.MORPH_CROSS: 十字形结构
'''
kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, size)
return cv2.dilate(img, kernel)

def standardizing(img):
'''
作用:图像标准化
1.边缘补白
2.缩小为28 * 28像素
'''
h, w = img.shape
dh, dw = max(h, w) - h, max(h, w) - w
padding = max(h, w) * 3 // 10
img = cv2.copyMakeBorder(img, dh // 2 + padding, dh // 2 + padding, dw // 2 + padding//2, dw // 2 + padding//2,
cv2.BORDER_CONSTANT, value = [0, 0, 0])
'''
NTER_NEAREST 最近邻插值
INTER_LINEAR 双线性插值（默认设置）
INTER_AREA 使用像素区域关系进行重采样。 它可能是图像抽取的首选方法，因为它会产生无云纹理的结果。 但是当图像缩放时，它类似于INTER_NEAREST方法。
INTER_CUBIC 4x4像素邻域的双三次插值
INTER_LANCZOS4 8x8像素邻域的Lanczos插值
'''
img = cv2.resize(img, (28, 28), interpolation=cv2.INTER_LINEAR)

return img

def predict(model, x):
if np.ndim(x) == 1:
return np.argmax(model.forwardprop(x))
else:
p = [model.forwardprop(xi)[0]for xi in x]
print('p:', p)
colidx = np.argmax(p, axis=1)
print('coldix', colidx)
ans = [p[i][colidx[i]] for i in range(len(colidx))]
print('ans', ans)
rowidx = np.argmax(ans)
print('rowdix', rowidx)
print('The number is', colidx[rowidx])

def processing_predict(model, path):
'''
参数:
model:模型
path:图像路径
'''
pre_img = cv2.imread(path, 0)
# padded_img = edge_padding(pre_img)
# min_img = get_minAreaRect(padded_img) #获得最小矩形（摆正数字
min_img = get_minAreaRect(pre_img)
delated_img = delate(min_img) #膨胀
standard_img = standardizing(delated_img) #边缘补白，居中，缩小为28 * 28像素
# print(standardimg)
imglist = [rotate_bound(standard_img, 90 * i).reshape(1, -1) for i in range(4)] #获得旋转后的共四张图像
ans = predict(model, np.array(imglist)) #预测