求能用python实现基于概率分布的超声图像与自然图像性质差异分析这一课题的完整详细代码,其中超声图像所在的文件夹路径是'D:/zzz/us6',自然图像所在的文件夹路径是'D:/zzz/na6',两种图像的类型都是jpg,两种图像都有862张,两种图像的形状都相同,需要判断超声图像与自然图像的像素值分布是否存在显著差异
时间: 2024-01-22 20:19:13 浏览: 15
首先,我们需要导入需要的库,包括numpy、matplotlib、os、cv2、scipy.stats等。然后,我们可以定义一个函数来读取图像数据,并将其转换为numpy数组。具体代码如下:
```python
import numpy as np
import matplotlib.pyplot as plt
import os
import cv2
from scipy.stats import ttest_ind
def read_images(path):
images = []
for filename in os.listdir(path):
if filename.endswith('.jpg'):
img = cv2.imread(os.path.join(path, filename), cv2.IMREAD_GRAYSCALE)
img = cv2.resize(img, (256, 256))
images.append(img)
return np.array(images)
```
接下来,我们可以调用该函数来读取超声图像和自然图像的数据:
```python
us_path = 'D:/zzz/us6'
na_path = 'D:/zzz/na6'
us_images = read_images(us_path)
na_images = read_images(na_path)
```
然后,我们可以使用numpy的histogram函数来计算每个图像的像素值分布,并绘制直方图:
```python
us_histograms = [np.histogram(img, bins=256, range=(0, 255))[0] for img in us_images]
na_histograms = [np.histogram(img, bins=256, range=(0, 255))[0] for img in na_images]
plt.hist(us_histograms, bins=256, range=(0, 255), alpha=0.5, label='Ultrasound')
plt.hist(na_histograms, bins=256, range=(0, 255), alpha=0.5, label='Natural')
plt.legend(loc='upper right')
plt.show()
```
接下来,我们可以使用t检验来判断超声图像和自然图像的像素值分布是否存在显著差异。具体代码如下:
```python
p_values = [ttest_ind(us_histograms[i], na_histograms[i], equal_var=False)[1] for i in range(256)]
significant_bins = np.where(np.array(p_values) < 0.05)[0]
if len(significant_bins) > 0:
print('There are significant differences in the following bins:')
print(significant_bins)
else:
print('There are no significant differences in the pixel value distributions.')
```
该代码将计算每个像素值的t检验p值,并找到显著差异的像素值。最后,我们可以输出结果:
```python
if len(significant_bins) > 0:
print('There are significant differences in the following bins:')
print(significant_bins)
else:
print('There are no significant differences in the pixel value distributions.')
```
完整代码如下:
```python
import numpy as np
import matplotlib.pyplot as plt
import os
import cv2
from scipy.stats import ttest_ind
def read_images(path):
images = []
for filename in os.listdir(path):
if filename.endswith('.jpg'):
img = cv2.imread(os.path.join(path, filename), cv2.IMREAD_GRAYSCALE)
img = cv2.resize(img, (256, 256))
images.append(img)
return np.array(images)
us_path = 'D:/zzz/us6'
na_path = 'D:/zzz/na6'
us_images = read_images(us_path)
na_images = read_images(na_path)
us_histograms = [np.histogram(img, bins=256, range=(0, 255))[0] for img in us_images]
na_histograms = [np.histogram(img, bins=256, range=(0, 255))[0] for img in na_images]
plt.hist(us_histograms, bins=256, range=(0, 255), alpha=0.5, label='Ultrasound')
plt.hist(na_histograms, bins=256, range=(0, 255), alpha=0.5, label='Natural')
plt.legend(loc='upper right')
plt.show()
p_values = [ttest_ind(us_histograms[i], na_histograms[i], equal_var=False)[1] for i in range(256)]
significant_bins = np.where(np.array(p_values) < 0.05)[0]
if len(significant_bins) > 0:
print('There are significant differences in the following bins:')
print(significant_bins)
else:
print('There are no significant differences in the pixel value distributions.')
```
注意:该代码仅供参考,需要根据实际情况进行调整和优化。
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