def read_show_img(image_name, show_img=False):
# raster = gdal.Open('C:\\test.jpg')
# image_name = 'C:\hyperspectral\AVIRISReflectanceSubset.dat'
raster = gdal.Open(image_name)
# raster = gdal.Open('e:\qwer.img')
print('Reading the image named:', image_name)
# print('geotrans is ', raster.GetGeoTransform())
# print('projection is ',raster.GetProjection())
xsize = raster.RasterXSize # RasterXSize是图像宽
ysize = raster.RasterYSize # RasterYSize是图像的高
band_num = raster.RasterCount # RasterCount是图像的波段数
im_GeoTrans = raster.GetGeoTransform() #仿射矩阵，左上角像素的大地坐标和像素分辨率
# (左上角x, x分辨率，仿射变换，左上角y坐标，y分辨率，仿射变换)
im_proj = raster.GetProjection() #地图投影信息，字符串表示
print('in read and show x,y', xsize, ysize)
# 获取rgb对应波段数值，传递给拉伸显示模块
raster_array = raster.ReadAsArray() # raster_array.shape=(191,128,155)对应（band_num,Ysize,Xsize),
# Ysize代表行数，Xsize代表列数
raster_array_np = np.array(raster_array)
#
# print('rnp',np.shape(rnp))
print('raster_array shape in read_show img', raster_array_np.shape)
if band_num >= 3:
r = raster_array[0]
g = raster_array[1]
b = raster_array[2]
print('type of r in read_show_img:', type(r))
if show_img: # 判断是否需要显示图像
strechimg(xsize, ysize, r, g, b) # 调用图像拉伸显示模块
# strechimg(92, 92, r[0:92,0:92], g[0:92,0:92], b[0:92,0:92]) # 调用图像拉伸显示模块
else:
img_data = raster.GetRasterBand(1)
img_data = img_data.ReadAsArray()
# print(np.shape(img_data))
if show_img:
strechimg_pan(xsize, ysize, img_data)
# print()
return raster, raster_array, xsize, ysize, band_num, im_GeoTrans, im_proj
def read_show_tiff(tiff_path):
raster = gdal.Open(tiff_path)
xsize = raster.RasterXSize
ysize = raster.RasterYSize
band_num = raster.RasterCount
raster_array = raster.ReadAsArray()
if band_num >= 3:
# plt.imshow(raster_array[0,:,:])
# plt.show()
r = raster_array[40]
g = raster_array[20]
b = raster_array[10]
stretch.strechimg(xsize, ysize, r, g, b)
elif band_num == 1:
# plt.imshow(raster_array)
# plt.show()
im = raster_array
print('max in show tiff', np.max(im))
stretch.strechimg_pan(xsize, ysize, im, False)
def read_from_img(image_name, show_img=False):
# 用于将遥感影像读入到数组，整体影像。
# 输入值为影像完整路径，输出为raster,raster_array,xsize, ysize,band_num
raster = gdal.Open(image_name)
#raster = gdal.Open('e:\qwer.img')
print('Read from img function,Reading the image named:', image_name)
xsize = raster.RasterXSize # RasterXSize是图像的行数，也就是图的高，即Y
ysize = raster.RasterYSize # RasterYSize是图像的列数，图像的宽度
band_num = raster.RasterCount #RasterCount是图像的波段数
# 获取rgb对应波段数值，传递给拉伸显示模块
raster_array = raster.ReadAsArray(
) #raster_array.shape=(191,128,155)对应（band_num,Ysize,Xsize),
# Ysize代表行数，Xsize代表列数
#raster_array_np = np.array(raster_array)
#print('raster_array shape in read_show img',raster_array_np.shape)
#
if band_num >= 3:
# r = raster.GetRasterBand(29)#raster_array[0]
# g = raster.GetRasterBand(20)#raster_array[1]
# b = raster.GetRasterBand(12)#raster_array[2]
# r = r.ReadAsArray()
# g = g.ReadAsArray()
# b = b.ReadAsArray()
r = raster_array[0]
g = raster_array[1]
b = raster_array[2]
print('type of r in read_show_img:', type(r))
if show_img: #判断是否需要显示图像
strechimg(xsize, ysize, r, g, b) #调用图像拉伸显示模块
#strechimg(92, 92, r[0:92,0:92], g[0:92,0:92], b[0:92,0:92]) # 调用图像拉伸显示模块
else:
img_data = raster.GetRasterBand(1)
img_data = img_data.ReadAsArray()
print(np.shape(img_data))
if show_img:
strechimg_pan(xsize, ysize, img_data)
def read_show_img(image_name,show_img = False):
#raster = gdal.Open('C:\\test.jpg')
#image_name = 'C:\hyperspectral\AVIRISReflectanceSubset.dat'
raster = gdal.Open(image_name)
#raster = gdal.Open('e:\qwer.img')
print('Reading the image named:',image_name)
xsize = raster.RasterXSize # RasterXSize是图像的行数，也就是图的高，即Y
ysize = raster.RasterYSize # RasterYSize是图像的列数，图像的宽度
band_num = raster.RasterCount #RasterCount是图像的波段数
# 获取rgb对应波段数值，传递给拉伸显示模块
raster_array = raster.ReadAsArray() #raster_array.shape=(191,128,155)对应（band_num,Ysize,Xsize),
# Ysize代表行数，Xsize代表列数
raster_array_np = np.array(raster_array)
#
#print('rnp',np.shape(rnp))
print('raster_array shape in read_show img',raster_array_np.shape)
if band_num >= 3:
r = raster_array[0]
g = raster_array[1]
b = raster_array[2]
print('type of r in read_show_img:', type(r))
if show_img: #判断是否需要显示图像
strechimg(xsize,ysize,r,g,b) #调用图像拉伸显示模块
#strechimg(92, 92, r[0:92,0:92], g[0:92,0:92], b[0:92,0:92]) # 调用图像拉伸显示模块
else:
img_data = raster.GetRasterBand(1)
img_data = img_data.ReadAsArray()
print(np.shape(img_data))
if show_img:
strechimg_pan(xsize,ysize,img_data)
#print()
return raster,raster_array,xsize, ysize,band_num
#tensorflow中的数据shape是[batch,height/Ysize,width/Xsize,band_num]
y_data = y_data.reshape(sample_num, class_num) #[batch,class_num]
#print('after y_data')
xs = tf.placeholder(tf.float32,
[None, sample_size_Y, sample_size_X, band_num]) #
ys = tf.placeholder(tf.float32, [None, class_num])
keep_prob = tf.placeholder(tf.float32)
train = False
if train:
train_cnn()
else:
test_xs, test_ys = get_next_batch(x_data, y_data, num_per_class,
sample_num, 60)
print(test_xs.shape)
x_img = np.reshape(test_xs[0, :, :, :],
[band_num, sample_size_Y, sample_size_X])
strechimg(sample_size_X, sample_size_Y, x_img[0, :, :], x_img[1, :, :],
x_img[2, :, :])
predicted_label = test_cnn(test_xs)
#predicted_label = np.array(predicted_label)
print('predicted_label:', predicted_label)
print('real label:', test_ys.argmax(1))
real_label = test_ys.argmax(1)
# real_label =[2,3]
result = (predicted_label == real_label) #判断两个数组的对应元素是否相同
#result =np.array(result)
print(result, np.sum(result == True)) #打印对应元素相等的数量