import sensor, image, time, display
from pyb import millis, UART
from os import listdir
i = 0
uart = UART(3, 115200)
path = ‘/library_province’
library_province = listdir(path) #读取省份模板文件名
library_province.sort()#排序
province_template =
#省份模板图片
province_similarity =
#每次识别后所有模板的相似度########
for n in range(len(library_province)):
province_template.append(image.Image(‘/library_province/’+library_province[n]))#存入省份模板图
province_template[n].invert()
province_similarity.append(0) #每次识别后所有模板的相似度
library_alphanumeric = listdir(‘/library_alphanumeric’)#读取数字字母模板文件名
library_alphanumeric.sort()#排序
alphanumeric_template =
#数字字母模板图片
alphanumeric_similarity =
#每次识别后所有模板的相似度
for n in range(len(library_alphanumeric)):
alphanumeric_template.append(image.Image(‘/library_alphanumeric/’+library_alphanumeric[n]))#存入数字字母模板图
alphanumeric_template[n].invert()
alphanumeric_similarity.append(0) #每次识别后所有模板的相似度
license_number =
#存储识别到的结果
for n in range(7):
license_number.append(’ ')#省位的h
sensor.reset() # Initialize the camera sensor.
sensor.set_framesize(sensor.QQVGA2) # 128x160大小的特定液晶屏。
sensor.set_pixformat(sensor.RGB565) # or sensor.RGB565
sensor.set_framesize(sensor.QVGA) # or sensor.QVGA (or others)
sensor.set_windowing(320,172)
sensor.set_contrast(2)
clock = time.clock() # Tracks FPS.
img_GRAYSCALE = sensor.alloc_extra_fb(320,172,sensor.GRAYSCALE)
img_GRAYSCALE_2 = sensor.alloc_extra_fb(320,172,sensor.GRAYSCALE)
img_targets =
img_targets.append(sensor.alloc_extra_fb(35,55,sensor.GRAYSCALE))
for n in range(6):
img_targets.append(sensor.alloc_extra_fb(28,45,sensor.GRAYSCALE))
invert = False#是否反转颜色，以适应黑色字符的车牌
lcd = display.SPIDisplay()
while(True):
clock.tick() # Track elapsed milliseconds between snapshots().
target_blob_max=None
img = sensor.snapshot() # Take a picture and return the image.
#一、处理图像并选中数字字母区域
img_GRAYSCALE.draw_image(img,0,0) #原图绘制到灰度画布上，用于定位字符
if invert:
img_GRAYSCALE.invert()
img_GRAYSCALE_2.draw_image(img_GRAYSCALE,0,0) #复制第二份灰度图，用于识别
\# img_GRAYSCALE_2.binary(\[(10,255)\]) #按阈值二值化
img_GRAYSCALE.laplacian(3) #通过拉普拉斯变换，突出色彩分界线（数值越大效果越好，但越慢。所以用最小值，再提高画面亮度）
img_GRAYSCALE.gamma_corr(gamma=1.2,contrast=25) #提高画面伽马值、对比度、亮度
#识别浅色区域，加上尺寸、连续度的限制
blobs = img_GRAYSCALE.find_blobs(\[(2,255)\], x_stride=4,y_stride=2,pixels_threshold=80, area_threshold=80, margin=10)
#二、通过设置条件判断，筛选出符合车牌号特征的区域集，并排序，待模板匹配使用
timer = millis()#用于计这段消耗的时间，如果耗时过长，需要优化或移植到底层（C语言）
#1.遍历筛选所有识别结果,筛选条件：自己和其他4个以上元素 高度 和 y坐标 相互相似的目标
target_blobs=\[\]
for n1 in range(len(blobs)):
find_out_times = 0
for n2 in range(len(blobs)):
#判断高度差、Y轴差异度
if abs(blobs\[n1\].h() - blobs\[n2\].h()) < (blobs\[n1\].h() \* 0.2) and \\
abs(blobs\[n1\].cy() - blobs\[n2\].cy()) < (blobs\[n1\].h() \* 0.3):
find_out_times += 1
if find_out_times > 4:#超过5次符合，记录
target_blobs.append(blobs\[n1\])
break
#2.结果按y轴排序
target_blobs.sort(key = lambda b: b.y())#按选择框cy排序
#3.在结果中记录每行的结束序号，比如 \[(45,3),(85,3),(78,20),(23,20)\],第二个元素为第一行的结束序号
line_ending = \[\]#记录每一行目标最后一项的序号
for n in range(0,len(target_blobs)-1):
if abs(target_blobs\[n\].cy() - target_blobs\[n+1\].cy()) > target_blobs\[n\].h()\*0.3:#两个Y差大于40%
line_ending.append(n)
line_ending.append(len(target_blobs))#上述方法不能记录最后一行，在此加入最后一行结束点。如果target_blobs没有内容，会加入0
#4.分割每行，并排序、淘汰每行少于5个的元素
target_blob_lines = \[\]#存储以行为单位，完成排序的目标坐标结果
if line_ending and line_ending != \[0\]:#如果有目标
for n in range(len(line_ending)):#循环遍历
if n == 0:#首行
if line_ending\[n\] - 0 > 5: #如果大于5个元素，存储内容，否则抛弃内容
target_blob_lines.append(target_blobs\[ : line_ending\[n\]\])#转存内容
target_blob_lines\[-1\].sort(key = lambda b: b\[0\])#按选择框x坐标排序
elif line_ending\[n\] - line_ending\[n-1\] > 5: #非首行。如果大于5个元素，存储内容，否则抛弃内容
target_blob_lines.append(target_blobs\[line_ending\[n-1\] + 1 : line_ending\[n\]\])#转存内容
target_blob_lines\[-1\].sort(key = lambda b: b\[0\])#按选择框x坐标排序
#5.进一步筛选掉每行，前后出现的，间距不符的元素
n = 0
while True:
for n in range(len(target_blob_lines)):#行遍历，如果最后一位与倒数第二位间距，不符合倒数第二、第三位间距，则删除最后一位
if (target_blob_lines\[n\]\[-2\].cx() - target_blob_lines\[n\]\[-3\].cx())\*0.8 <\\
(target_blob_lines\[n\]\[-1\].cx() - target_blob_lines\[n\]\[-2\].cx()) > \\
(target_blob_lines\[n\]\[-2\].cx() - target_blob_lines\[n\]\[-3\].cx())\*1.2:
del target_blob_lines\[n\]\[-1\]
break
if n >= len(target_blob_lines)-1:
break
for n in range(len(target_blob_lines)):#行遍历，删除 从后向前数6个以外的其他元素
if len(target_blob_lines\[n\]) > 6:
del target_blob_lines\[n\]\[0 : len(target_blob_lines\[n\]) - 6\]
#6.只保留像素最大的一行
if target_blob_lines:#如果有目标
target_blob_max = max(target_blob_lines, key = lambda b: b\[0\]\[4\])#每行第一位面积为判断，保留最大的一行
#7.补充省位选择框
h_average = 0 #平均高度
spacing_average = 0 #平均间距(后段相邻部分)
y_difference_average = 0 #平均y坐标差(后段相邻部分)
length = len(target_blob_max)
for n in range(length):
h_average += target_blob_max\[n\].h()
if n > 1:
spacing_average += target_blob_max\[n\].cx() - target_blob_max\[n-1\].cx()
y_difference_average += target_blob_max\[n\].cy() - target_blob_max\[n-1\].cy()
h_average = round(h_average / length)
w_average = round(h_average / 2)
spacing_average = round(spacing_average / (length - 2 ))
y_difference_average = round(y_difference_average / (length - 2))
target_blob_max.insert(0, \[round(target_blob_max\[0\].x() - (spacing_average \* 1.2)),\\
round(target_blob_max\[0\].y() - (y_difference_average \* 1.1)-2),\\
round(w_average \* 1.4),\\
round(h_average \* 1.2)\])
else: #没找到目标
invert = not invert #通知反转画面，以待识别黑色字符
if target_blob_max:
try: #此处常遇报错，用try跳过报错
for n in range(len(target_blob_max)):
img_targets\[n\].clear()
if n == 0:
img_targets\[n\].draw_image(img_GRAYSCALE_2, 0, 0, x_scale = 40 / h_average, y_scale = 40 / h_average,\\
roi = (target_blob_max\[n\]\[0\] - 2,\\
target_blob_max\[n\]\[1\] - 1,\\
target_blob_max\[n\]\[2\] + 5,\\
target_blob_max\[n\]\[3\] + 5))
else:
img_targets\[n\].draw_image(img_GRAYSCALE_2, 0, 0, x_scale = 40 / target_blob_max\[n\]\[3\], y_scale = 40 / target_blob_max\[n\]\[3\],\\
roi = (target_blob_max\[n\]\[0\] - 1,\\
target_blob_max\[n\]\[1\] - 1,\\
target_blob_max\[n\]\[2\] + 5,\\
target_blob_max\[n\]\[3\] + 5))
img.draw_rectangle(target_blob_max\[n\]\[:4\], color=(255,0,0))
\# img.draw_image(img_targets\[n\], n\*30, 0,) #将剪切结果绘制到主画布上，以观察效果
except:
continue
for n in range(len(target_blob_max)):
if n == 0:
matching_rate = 0.75 #初始匹配率
result = None #识别结果
while True:
blob_num = 0 #识别到的目标数量
for n1 in range(len(library_province)):
blob = img_targets\[n\].find_template(province_template\[n1\], matching_rate, step=2, search = image.SEARCH_EX)
if blob: #如果识别到目标
blob_num += 1 #目标数量+1
result = n1 #保存结果
if blob_num == 1: #如果唯一目标
license_number\[n\] = library_province\[result\]\[:-4\] #保存结果
#print('成功匹配，匹配率：'+str(matching_rate))
break #结束循环
elif blob_num == 0 and matching_rate > 0.4: #如果匹配率>0.4 且 没找到目标
matching_rate -= 0.1 #匹配率降低
#print('降低匹配率'+str(matching_rate))
else: #多个结果（小概率事件），0个结果 或 匹配率降低到0.4
break #结束循环
else:
matching_rate = 0.75 #初始匹配率
result = None #识别结果
while True:
if target_blob_max\[n\]\[3\] / target_blob_max\[n\]\[2\] > 3: #窄体，只有I
license_number\[n\] = 'I'
break
blob_num = 0
for n1 in range(len(library_alphanumeric)):
blob = img_targets\[n\].find_template(alphanumeric_template\[n1\],0.75, step=2, search = image.SEARCH_EX)
if blob and library_alphanumeric\[n1\]\[:-4\] != 'I' and library_alphanumeric\[n1\]\[:-4\] != 'O':
blob_num += 1 #目标数量+1
result = n1 #保存结果
if blob_num == 1: #如果唯一目标
license_number\[n\] = library_alphanumeric\[result\]\[:-4\] #保存结果
break #结束循环
elif blob_num == 0 and matching_rate > 0.4: #如果匹配率>0.4 且 没找到目标
matching_rate -= 0.2 #匹配率降低
#print('降低匹配率'+str(matching_rate))
\# if license_number\[1\] >= 'A' and license_number\[1\] <= 'Z':
\# for a in license_number:
\# if a == 'I' or a == ' ':
\# i += 1
\# if i < 3:
\# i = 0
uart.write(license_number\[0\] + license_number\[1\] + license_number\[2\] +license_number\[3\] + license_number\[4\] + license_number\[5\] + license_number\[6\])
print("省份："+license_number\[0\] +"车牌号："+ license_number\[1\] + license_number\[2\] +license_number\[3\] + license_number\[4\] + license_number\[5\] + license_number\[6\])
else: #多个结果（小概率事件），0个结果 或 匹配率降低到0.4
break #结束循环
img.draw_image(img_targets\[0\], 10, 0, )
a = license_number\[1\] + license_number\[2\] +license_number\[3\] + license_number\[4\] + license_number\[5\] + license_number\[6\]
img.draw_string(60, 10, a, scale=1.4, color=(0, 0, 0))
lcd.write(img)
为什么我这个运行不了呀
Hi, can you reduce the program down the simplest example of the error and please use English?
Thank you,
Traceback (most recent call last):
File “”, line 7, in
OSError: [Errno 19] ENODEV
OpenMV v4.7.0; MicroPython v1.25.0-r0; OPENMV4 with STM32H743
Type “help()” for more information.
That’s the error code. Please reduce the program.
path = ‘/sdcard/library_province’
library_province = listdir(path)
The specific error is this line，It seems like it cannot detect my province module
Hi, this would then mean that there’s some issue with reading that folder.
If the sdcard is mounted then you should be able to read /sdcard. If that works, then there’s something wrong with the folder library_province