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#coding=utf8
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import matplotlib.pyplot as plt
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import cv2
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import numpy as np
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from math import *
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from flask import jsonify
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import tensorflow as tf
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from apps.service import settings
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def wrap_success_json(result_name):
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result_name = 'http://127.0.0.1:5000/api/get_res_file/' + result_name
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return_data = {'code': '1', 'message': 'Success', 'result_name': result_name}
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return jsonify(return_data)
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def wrap_failure_json(result_name):
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result_name = 'http://127.0.0.1:5000/api/get_res_file/' + result_name
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return_data = {'code': '2', 'message': 'Failure', 'result_name': result_name}
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return jsonify(return_data)
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def img_expand(img1, img2):
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shape1 = img1.shape
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shape2 = img2.shape
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row1 = shape1[0]
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col1 = shape1[1]
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row2 = shape2[0]
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col2 = shape2[1]
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if row1 > row2:
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row = row1
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else:
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row = row2
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if col1 > col2:
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col = col1
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else:
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col = col2
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if (row - row1) % 2 != 0:
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r11 = int((row - row1) / 2)
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r12 = r11 + 1
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else:
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r11 = r12 = int((row - row1) / 2)
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if (col - col1) % 2 != 0:
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c11 = int((col - col1) / 2)
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c12 = c11 + 1
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else:
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c11 = c12 = int((col - col1) / 2)
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if (row - row2) % 2 != 0:
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r21 = int((row - row2) / 2)
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r22 = r21 + 1
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else:
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r21 = r22 = int((row - row2) / 2)
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if (col - col2) % 2 != 0:
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c21 = int((col - col2) / 2)
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c22 = c21 + 1
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else:
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c21 = c22 = int((col - col2) / 2)
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img1 = cv2.copyMakeBorder(img1, r11, r12, c11, c12, cv2.BORDER_CONSTANT, value=[255, 255, 255])
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img2 = cv2.copyMakeBorder(img2, r21, r22, c21, c22, cv2.BORDER_CONSTANT, value=[255, 255, 255])
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return img1, img2
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def swap(val1, val2):
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return val2, val1
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def order_desc(array):
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# 列表的长度
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length = len(array)
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# 对列表进行选择排序，获得有序的列表
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for i in range(length):
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for j in range(i + 1, length):
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# 选择最大的值
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if array[j] > array[i]:
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# 交换位置
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temp = array[j]
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array[j] = array[i]
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array[i] = temp
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return array
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def grayHist(img, filename):
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plt.figure(filename, figsize=(16, 8))
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plt.subplot(121)
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plt.imshow(img, 'gray')
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plt.subplot(122)
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h, w = img.shape[:2]
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pixelSequence = img.reshape(1, h * w)
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numberBins = 256
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histogram, bins, patch = plt.hist(img.ravel(), 256, [0, 255])
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plt.xlabel("gray label")
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plt.ylabel("number of pixels")
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plt.axis([0, 255, 0, np.max(histogram)])
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plt.savefig(filename)
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plt.show()
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def rotate(image, angle):
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height, width, channels = image.shape
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heightNew = int(width * fabs(sin(radians(angle))) + height * fabs(cos(radians(angle))))
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widthNew = int(height * fabs(sin(radians(angle))) + width * fabs(cos(radians(angle))))
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matRotation = cv2.getRotationMatrix2D((width / 2, height / 2), angle, 1)
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matRotation[0, 2] += (widthNew - width) / 2
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matRotation[1, 2] += (heightNew - height) / 2
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imgRotation = None
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if channels == 1:
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imgRotation = cv2.warpAffine(image, matRotation, (widthNew, heightNew), borderValue=(255))
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elif channels == 3:
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imgRotation = cv2.warpAffine(image, matRotation, (widthNew, heightNew), borderValue=(255, 255, 255))
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return imgRotation
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# 频域平滑
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def ideal_low_filter(img, D0):
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"""
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生成一个理想低通滤波器（并返回）
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"""
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h, w = img.shape[:2]
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filter_img = np.ones((h, w))
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u = np.fix(h / 2)
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v = np.fix(w / 2)
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for i in range(h):
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for j in range(w):
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d = np.sqrt((i - u) ** 2 + (j - v) ** 2)
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filter_img[i, j] = 0 if d > D0 else 1
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return filter_img
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def butterworth_low_filter(img, D0, rank):
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"""
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生成一个Butterworth低通滤波器（并返回）
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"""
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h, w = img.shape[:2]
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filter_img = np.zeros((h, w))
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u = np.fix(h / 2)
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v = np.fix(w / 2)
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for i in range(h):
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for j in range(w):
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d = np.sqrt((i - u) ** 2 + (j - v) ** 2)
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filter_img[i, j] = 1 / (1 + 0.414 * (d / D0) ** (2 * rank))
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return filter_img
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def exp_low_filter(img, D0, rank):
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"""
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生成一个指数低通滤波器（并返回）
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"""
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h, w = img.shape[:2]
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filter_img = np.zeros((h, w))
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u = np.fix(h / 2)
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v = np.fix(w / 2)
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for i in range(h):
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for j in range(w):
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d = np.sqrt((i - u) ** 2 + (j - v) ** 2)
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filter_img[i, j] = np.exp(np.log(1 / np.sqrt(2)) * (d / D0) ** (2 * rank))
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return filter_img
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def filter_use(img, filter):
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"""
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将图像img与滤波器filter结合，生成对应的滤波图像
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"""
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# 首先进行傅里叶变换
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f = np.fft.fft2(img)
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f_center = np.fft.fftshift(f)
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# 应用滤波器进行反变换
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S = np.multiply(f_center, filter) # 频率相乘——l(u,v)*H(u,v)
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f_origin = np.fft.ifftshift(S) # 将低频移动到原来的位置
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f_origin = np.fft.ifft2(f_origin) # 使用ifft2进行傅里叶的逆变换
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f_origin = np.abs(f_origin) # 设置区间
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return f_origin
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def DFT_show(img):
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"""
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对传入的图像进行傅里叶变换，生成频域图像
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"""
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f = np.fft.fft2(img) # 使用numpy进行傅里叶变换
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fshift = np.fft.fftshift(f) # 把零频率分量移到中间
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result = np.log(1 + abs(fshift))
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return result
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# 频域锐化
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def ideal_high_filter(img, D0):
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"""
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生成一个理想高通滤波器（并返回）
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"""
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h, w = img.shape[:2]
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filter_img = np.zeros((h, w))
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u = np.fix(h / 2)
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v = np.fix(w / 2)
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for i in range(h):
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for j in range(w):
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d = np.sqrt((i - u) ** 2 + (j - v) ** 2)
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filter_img[i, j] = 0 if d < D0 else 1
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return filter_img
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def butterworth_high_filter(img, D0, rank):
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"""
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生成一个Butterworth高通滤波器（并返回）
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"""
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h, w = img.shape[:2]
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filter_img = np.zeros((h, w))
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u = np.fix(h / 2)
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v = np.fix(w / 2)
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for i in range(h):
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for j in range(w):
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d = np.sqrt((i - u) ** 2 + (j - v) ** 2)
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filter_img[i, j] = 1 / (1 + (D0 / d) ** (2 * rank))
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return filter_img
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def exp_high_filter(img, D0, rank):
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"""
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生成一个指数高通滤波器（并返回）
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"""
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h, w = img.shape[:2]
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filter_img = np.zeros((h, w))
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u = np.fix(h / 2)
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v = np.fix(w / 2)
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for i in range(h):
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for j in range(w):
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d = np.sqrt((i - u) ** 2 + (j - v) ** 2)
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filter_img[i, j] = np.exp((-1) * (D0 / d) ** rank)
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return filter_img
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def filter_use2(img, filter):
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"""
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将图像img与滤波器filter结合，生成对应的滤波图像
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"""
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# 首先进行傅里叶变换
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f = np.fft.fft2(img)
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f_center = np.fft.fftshift(f)
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# 应用滤波器进行反变换
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S = np.multiply(f_center, filter) # 频率相乘——l(u,v)*H(u,v)
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f_origin = np.fft.ifftshift(S) # 将低频移动到原来的位置
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f_origin = np.fft.ifft2(f_origin) # 使用ifft2进行傅里叶的逆变换
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f_origin = np.abs(f_origin) # 设置区间
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f_origin = f_origin / np.max(f_origin.all())
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return f_origin
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# 我们准备使用经典网络在imagenet数据集上的与训练权重，所以归一化时也要使用imagenet的平均值和标准差
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image_mean = tf.constant([0.485, 0.456, 0.406])
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image_std = tf.constant([0.299, 0.224, 0.225])
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def normalization(x):
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"""
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对输入图片x进行归一化，返回归一化的值
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"""
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return (x - image_mean) / image_std
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def load_images(image_path, width=settings.WIDTH, height=settings.HEIGHT):
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"""
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加载并处理图片
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:param image_path: 图片路径
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:param width: 图片宽度
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:param height: 图片长度
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:return: 一个张量
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"""
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# 加载文件
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x = tf.io.read_file(image_path)
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# 解码图片
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x = tf.image.decode_jpeg(x, channels=3)
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# 修改图片大小
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x = tf.image.resize(x, [height, width])
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x = x / 255.
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# 归一化
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x = normalization(x)
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x = tf.reshape(x, [1, height, width, 3])
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# 返回结果
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return x
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def save_image(image, filename):
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x = tf.reshape(image, image.shape[1:])
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x = x * image_std + image_mean
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x = x * 255.
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x = tf.cast(x, tf.int32)
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x = tf.clip_by_value(x, 0, 255)
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x = tf.cast(x, tf.uint8)
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x = tf.image.encode_jpeg(x)
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tf.io.write_file(filename, x)
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