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
158
record_system_v13/1record_system_v1.3.py
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record_system_v13/1record_system_v1.3.py
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'''
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在V1.2的版本上改进：
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（1）自动曝光，（2）显示光谱仪温度
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'''
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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
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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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import sys, struct
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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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# # 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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# cam.set_binning_selector('XI_BIN_SELECT_HOST_CPU') #默认为XI_BIN_SELECT_SENSOR，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(2)
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# cam.set_binning_vertical_mode('XI_BIN_MODE_SUM')
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# cam.set_binning_vertical(2)
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#settings,cam.set_param("exposure",10000)
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framerate = 10
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cam.set_framerate(framerate)
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cam.set_aeag_roi_offset_x(286)
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cam.set_aeag_roi_offset_y(340)
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cam.set_aeag_roi_height(300)
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cam.set_aeag_roi_width(1365)
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# cam.set_exp_priority(1) # Exposure priority (0.8 - exposure 80%, gain 20%).XI_PRM_EXP_PRIORITY
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# cam.set_ae_max_limit(24000) # Maximum time (us) used for exposure in AEAG procedureXI_PRM_AE_MAX_LIMIT
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# cam.set_ag_max_limit(12)
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# cam.set_aeag_level(50) # Average intensity of output signal AEAG should achieve(in %)XI_PRM_AEAG_LEVEL
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cam.enable_aeag()
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# exposure = 1000
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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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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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#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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cam.set_imgdataformat('XI_RAW16')
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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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framenumber = 2000
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image_container = np.empty((3, framenumber, 1365))
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f = open('delete3', 'wb')
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cam.start_acquisition()
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starttime = datetime.datetime.now()
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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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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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#将每一帧有效数据写入文件,方法1：通过struct.pack
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'''
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-------------------------struct.pack缺点：----------------------------------------
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Based on experience, I'd strongly advise against the use of struct.pack.
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Its API requires you to demangle the array (i.e. use the * operator) into variables
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which is going affect performance drastically for large array sizes.
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Every element of the array will need a new, on-the-fly 64-bit pointer (on a 64-bit machine)
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to be created on the stack to point into that element.
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Just stick to tostring/tobytes/tofile to avoid this overhead.
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For example, in our case, we were dealing with 100 Mb arrays which leads to a whopping 6.4 Gb of RAM so struct.pack can do its work.
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'''
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# f.write(struct.pack('f' * len(image_raw_numpy[339:639, 285:1650].flatten()),
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# *image_raw_numpy[339:639, 285:1650].flatten())) # 这种通过struct的方式特别慢
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# 将每一帧有效数据写入文件,方法2：通过numpy.tobytes()
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'''
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丢帧说明：
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帧率 丢帧率
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50 0%
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100 13%
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150 17%
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'''
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# image_raw_numpy = img.get_image_data_numpy()
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# image_container[:, i, :] = image_raw_numpy[339:639, 285:1650][[36, 76, 121]]
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# f.write(image_raw_numpy[339:639, 285:1650].flatten().tobytes())
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# # f.write(image_raw_numpy.flatten().tobytes())
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# 将每一帧有效数据写入文件,方法3：通过直接读取相机返回的字节流
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'''丢帧说明：
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帧率 丢帧率
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50 0%
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100 20%
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150 60%
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'''
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# data_raw = img.get_image_data_raw() # data_raw是字节流，一帧的字节流长度为4708352，而一帧矩阵有多少个数：1216*1936 = 2354176；所以返回的数是2字节的短整型
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# f.write(data_raw[1313180:2473636])
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# f.write(data_raw)
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endtime = datetime.datetime.now()
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f.close()
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print('影像采集用时：%d'%(endtime - starttime).seconds)
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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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#stop communication
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cam.close_device()
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plt.imshow(np.dstack((image_container[2, :, :], image_container[1, :, :],
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image_container[0, :, :])))
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plt.show()
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print('Done.')
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