RuntimeError：Sensor control failed.怎么解决，固件版本是最新的，运行示例代码没问题
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# Template Matching Example - Normalized Cross Correlation (NCC) # # This example shows off how to use the NCC feature of your OpenMV Cam to match # image patches to parts of an image... expect for extremely controlled enviorments # NCC is not all to useful. # # WARNING: NCC supports needs to be reworked! As of right now this feature needs # a lot of work to be made into somethin useful. This script will reamin to show # that the functionality exists, but, in its current state is inadequate. import time, sensor, image,ustruct from pyb import UART,LED from image import SEARCH_EX, SEARCH_DS #从imgae模块引入SEARCH_EX和SEARCH_DS。使用from import仅仅引入SEARCH_EX, #SEARCH_DS两个需要的部分，而不把image模块全部引入。 # Reset sensor sensor.reset() # Set sensor settings sensor.set_contrast(1) sensor.set_gainceiling(16) # Max resolution for template matching with SEARCH_EX is QQVGA sensor.set_framesize(sensor.QQVGA) # You can set windowing to reduce the search image. #sensor.set_windowing(((640-80)//2, (480-60)//2, 80, 60)) sensor.set_pixformat(sensor.GRAYSCALE) LED(1).on() LED(2).on() LED(3).on() # Load template. # Template should be a small (eg. 32x32 pixels) grayscale image. template1 = image.Image("/1.pgm") template2 = image.Image("/2.pgm") templates3 = ["/3.pgm","/3_1.pgm","/3_2.pgm","/3_3.pgm","/3_4.pgm","/3_5.pgm","/3_6.pgm","/3_7.pgm","/3_8.pgm"] templates4 = ["/4.pgm","/4_1.pgm","/4_2.pgm","/4_3.pgm","/4_4.pgm","/4_5.pgm","/4_6.pgm","/4_7.pgm","/4_8.pgm"] templates5 = ["/5.pgm","/5_1.pgm","/5_2.pgm","/5_3.pgm","/5_4.pgm","/5_5.pgm","/5_6.pgm","/5_7.pgm","/5_8.pgm"] templates6 = ["/6.pgm","/6_1.pgm","/6_2.pgm","/6_3.pgm","/6_4.pgm","/6_5.pgm","/6_6.pgm","/6_7.pgm","/6_8.pgm"] templates7 = ["/7.pgm","/7_1.pgm","/7_2.pgm","/7_3.pgm","/7_4.pgm","/7_5.pgm","/7_6.pgm","/7_7.pgm","/7_8.pgm"] templates8 = ["/8.pgm","/8_1.pgm","/8_2.pgm","/8_3.pgm","/8_4.pgm","/8_5.pgm","/8_6.pgm","/8_7.pgm","/8_8.pgm"] #加载模板图片 clock = time.clock() uart = UART(3,115200) #定义串口3变量 uart.init(115200, bits=8, parity=None, stop=1) # init with given parameters def outuart(x,num): global uart #frame=[0x2C,18,cx%0xff,int(cx/0xff),cy%0xff,int(cy/0xff),0x5B]; #data = bytearray(frame) data = ustruct.pack("<bbhhhhb", #格式为俩个字符俩个短整型(2字节) 0x2C, #帧头1 0x12, #帧头2 int(x), # up sample by 4 #数据1 int(num), # up sample by 4 #数据2 int(0), # up sample by 4 #数据1 int(0), # up sample by 4 #数据2 0x5B) for x in range(5): uart.write(data)#必须要传入一个字节数组 time.sleep_ms(1) print(num) # Run template matching while (True): clock.tick() img = sensor.snapshot() num=0 # find_template(template, threshold, [roi, step, search]) # ROI: The region of interest tuple (x, y, w, h). # Step: The loop step used (y+=step, x+=step) use a bigger step to make it faster. # Search is either image.SEARCH_EX for exhaustive search or image.SEARCH_DS for diamond search # # Note1: ROI has to be smaller than the image and bigger than the template. # Note2: In diamond search, step and ROI are both ignored. r = img.find_template(template1, 0.70, step=5, search=SEARCH_EX) #, roi=(10, 0, 60, 60)) #find_template(template, threshold, [roi, step, search]),threshold中 #的0.7是相似度阈值,roi是进行匹配的区域（左上顶点为（10，0），长80宽60的矩形）， #注意roi的大小要比模板图片大，比frambuffer小。 #把匹配到的图像标记出来 if r: print(r) #img.draw_rectangle(r) print('1') num=1 outuart(0,num) for x in range(5): LED(1).on() LED(2).off() LED(3).off() time.sleep_ms(100) LED(1).on() LED(2).on() LED(3).on() time.sleep_ms(100) r2_0 = img.find_template(template2, 0.70, step=5, search=SEARCH_EX) if r2_0: print(r2_0) #img.draw_rectangle(r1_3) print('2') num=2 outuart(0,num) for x in range(5): LED(1).on() LED(2).off() LED(3).off() time.sleep_ms(100) LED(1).on() LED(2).on() LED(3).on() time.sleep_ms(100) r3_0 = img.find_template(image.Image(templates3[0]), 0.70, step=5, search=SEARCH_EX) if r3_0: print(r3_0) #img.draw_rectangle(r1_1) print('3') num=3 outuart(0,num) for x in range(5): LED(1).on() LED(2).off() LED(3).off() time.sleep_ms(100) LED(1).on() LED(2).on() LED(3).on() time.sleep_ms(100)
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传感器是OV2640
板子是OpenMV Cam H7