第一次提交
1、hpi的可用代码； 2、修复了多次点击曝光后，福亮度数据错误的问题； 3、定标方式为大的蓝菲积分球的标准能量曲线，而不是基于asd的能量曲线；
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tangchao0503
230
record_system_v11/1record_system_v1.1.py
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record_system_v11/1record_system_v1.1.py
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from ximea import xiapi
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import cv2
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import numpy as np
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import matplotlib.pyplot as plt
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from osgeo import gdal #读写影像数据
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import os, sys, traceback
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import datetime # 几种Python执行时间的计算方法：https://blog.csdn.net/wangshuang1631/article/details/54286551
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#create instance for first connected camera
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cam = xiapi.Camera()
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#start communication to open specific device, use: cam.open_device_by_SN('41305651')
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print('Opening first camera...')
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cam.open_device()
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# 打开相机后，显示相机信息
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print('SN: %s'% str(cam.get_device_sn(), encoding = "utf-8"))
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print('Device name: %s'% str(cam.get_device_name(), encoding = "utf-8"))
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print('Device name: %s'% str(cam.get_device_type(), encoding = "utf-8"))
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print('Instance path: %s'% str(cam.get_device_inst_path(), encoding = "utf-8")) # Returns device instance path in operating system.
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print('Location path: %s'% str(cam.get_device_loc_path(), encoding = "utf-8"))
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# debug_level、线程数和horizontal flip
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print('Debug level: %s' % cam.get_debug_level())
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print('Default number of threads per image processor: %d' % cam.get_proc_num_threads())
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cam.set_proc_num_threads(8)
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print('Current number of threads per image processor: %d' % cam.get_proc_num_threads())
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print('Is horizontal flip enabled?, %s' % str(cam.is_horizontal_flip()))
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#采集模式：This mode is supported by selected camera families: CB, MC, MT, MX
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cam.set_acq_timing_mode('XI_ACQ_TIMING_MODE_FRAME_RATE_LIMIT')
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mode_used = cam.get_acq_timing_mode()
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if mode_used == 'XI_ACQ_TIMING_MODE_FRAME_RATE_LIMIT':
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print('Mode is XI_ACQ_TIMING_MODE_FRAME_RATE_LIMIT')
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else:
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print('Mode is not XI_ACQ_TIMING_MODE_FRAME_RATE_LIMIT')
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sys.exit()
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#settings,cam.set_param("exposure",10000)
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exposure = 50
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framerate = 2
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cam.set_exposure(exposure)#曝光时间单位为微秒，1s共有1000000微秒
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cam.set_framerate(framerate)
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print('Exposure was set to %i us' % cam.get_exposure())
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print('Framerate was set to %i FPS' % cam.get_framerate())
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# # 设置binning参数
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# # XI_BIN_MODE_SUM（默认）: The response from the combined pixels will be added, resulting in increased sensitivity.
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# # XI_BIN_MODE_AVERAGE: The response from the combined pixels will be averaged, resulting in increased signal/noise ratio.
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bin_spec = 2
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bin_spatial = 2
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cam.set_binning_selector('XI_BIN_SELECT_HOST_CPU') # 默认为XI_BIN_SELECT_SENSOR(会报错)，不可用：XI_BIN_SELECT_DEVICE_FPGA
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cam.set_binning_horizontal_mode('XI_BIN_MODE_SUM')
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cam.set_binning_horizontal(bin_spatial)
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cam.set_binning_vertical_mode('XI_BIN_MODE_SUM')
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cam.set_binning_vertical(bin_spec)
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# cam.get_width_increment() -->16
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# cam.get_height_increment() -->2
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# cam.get_offsetX_increment() -->16
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# cam.get_offsetY_increment() -->2
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# 设置有效输出窗口
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if bin_spatial == 1:
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cam.set_width(1392)
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cam.set_offsetX(272)
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cam.set_height(302)
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cam.set_offsetY(338)
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elif bin_spatial == 2:
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cam.set_width(696)
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cam.set_offsetX(128)
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cam.set_height(151)
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cam.set_offsetY(168)
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# 创建img = xiapi.Image()前需要设置一系列img参数，例如：格式、位深度、
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# 如果格式设置为XI_RAW8，image_raw_numpy.dtype -> dtype('uint8'), uint8
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# 如果格式设置为XI_RAW16，image_raw_numpy.dtype -> dtype('<u2'), uint16
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cam.set_imgdataformat('XI_RAW16')
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cam.set_output_bit_depth("XI_BPP_16")
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# cam.set_image_data_bit_depth("XI_BPP_16")
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# cam.set_sensor_bit_depth("XI_BPP_16") # 报错
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# cam.get_buffer_policy()
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# create instance of Image to store image data and metadata
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img = xiapi.Image()
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# start data acquisition
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print('Starting data acquisition...')
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starttime = datetime.datetime.now()
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framenumber = 10
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image_container = np.empty((int(300 / bin_spec), framenumber, int(1364 / bin_spatial)))
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if bin_spec == 1:
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startRow = 1
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endRow = 301
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startColumn = 12
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endColumn = 1376
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if bin_spec == 2:
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startRow = 169
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endRow = 319
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startColumn = 143
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endColumn = 824
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if bin_spec == 4:
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startRow = 84
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endRow = 159
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startColumn = 71
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endColumn = 412
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# cam.set_framerate(int(framerate))
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# cam.set_aeag_roi_offset_x(startColumn)
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# cam.set_aeag_roi_offset_y(startRow)
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# cam.set_aeag_roi_height(endRow - startRow)
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# cam.set_aeag_roi_width(endColumn - startColumn)
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# cam.enable_aeag() # 开启自动曝光
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# cam.start_acquisition()
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# for i in range(10):
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# cam.get_image(img) # get data and pass them from camera to img
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# cam.stop_acquisition()
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# cam.disable_aeag() # 关闭自动曝光
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print('Framerate was set to %i FPS' % cam.get_framerate())
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print('Exposure was set to %i us' % cam.get_exposure())
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print('Gain was set to %i dB' % cam.get_gain())
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cam.start_acquisition()
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for i in range(framenumber):
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#get data and pass them from camera to img
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cam.get_image(img)
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#get raw data from camera
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#for Python2.x function returns string
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#for Python3.x function returns bytes
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# data_raw = img.get_image_data_raw()
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# print("第%d帧"% i)
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# image_raw_numpy.dtype -> dtype('<u2'), uint16
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# cornning 410的位深度为12，所以尽管uint16能表达16位的数据，但是相机返回的数据最大值依然为4095
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image_raw_numpy = img.get_image_data_numpy()
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print()
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try:
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if bin_spec == 1:
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image_container[:, i, :] = image_raw_numpy[startRow:endRow, startColumn:endColumn]
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if bin_spec == 2:
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image_container[:, i, :] = image_raw_numpy[startRow:endRow, startColumn:endColumn]
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if bin_spec == 4:
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image_container[:, i, :] = image_raw_numpy[startRow:endRow, startColumn:endColumn]
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except Exception:
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traceback.print_exc()
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# if i == 0:
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# image_container[:, 0, :] = image_raw_numpy[340:640, 285:1650]
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# elif i != 0:
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# image_container = np.concatenate((image_container, image_raw_numpy[340:640, 285:1650].reshape(300, 1, 1365)), axis=1)
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print(i)
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# print(cam.get_framerate())
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#stop data acquisition
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print('Stopping acquisition...')
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cam.stop_acquisition()
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endtime = datetime.datetime.now()
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print('影像采集用时：%d'%(endtime - starttime).seconds)
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#stop communication
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cam.close_device()
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class GRID:
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#读图像文件
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def read_img(self, filename):
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try:
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dataset = gdal.Open(filename) # 打开文件
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im_width = dataset.RasterXSize # 栅格矩阵的列数
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im_height = dataset.RasterYSize # 栅格矩阵的行数
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num_bands = dataset.RasterCount # 栅格矩阵的波段数
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im_geotrans = dataset.GetGeoTransform() # 仿射矩阵
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im_proj = dataset.GetProjection() # 地图投影信息
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im_data = dataset.ReadAsArray(0, 0, im_width, im_height) # 将数据写成数组，对应栅格矩阵
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del dataset
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return im_proj, im_geotrans, im_data
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except:
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sys.exit()
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#写文件，以写成tif为例
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def write_img(self, dst_filename, data):
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format = "ENVI"
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driver = gdal.GetDriverByName(format)
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RasterXSize = data.shape[2] # 遥感影像的sample（列数）
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RasterYSize = data.shape[1] # 遥感影像的line（行数）
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band = data.shape[0]
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# driver.Create()函数中RasterXSize代表影像的sample（列数），RasterYSize代表影像的line（行数）
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dst_ds = driver.Create(dst_filename, RasterXSize, RasterYSize, band, gdal.GDT_Float32)
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for i in range(band):
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dst_ds.GetRasterBand(i + 1).WriteArray(data[i, :, :]) # gdal的band从1开始，所以dst_ds.GetRasterBand(i+1)
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dst_ds = None
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starttime_xie = datetime.datetime.now()
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# dark_
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im_driver = GRID()
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# im_driver.write_img('jangnandaxue-焦距22' + str(exposure)+'_'+str(framerate)+'_'+str(34), image_container)
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im_driver.write_img('ar-ren-bin2a---------------------', image_container)
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endtime_xie = datetime.datetime.now()
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print('影像写入磁盘用时：%d'%(endtime_xie - starttime_xie).seconds)
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print('计算波长')
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def calculate_wavelength(x):
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wavelength = x*1.999564 - 279.893
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return wavelength
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wavelength = np.empty(300)
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for i in range(340, 640):
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wavelength[i-340] = calculate_wavelength(i)
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plt.imshow(image_container[100, :, :])
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plt.show()
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print('Done.')
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