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||||
<workItem from="1740446685371" duration="609000" />
|
||||
<workItem from="1740450495203" duration="303000" />
|
||||
<workItem from="1740456642604" duration="7575000" />
|
||||
<workItem from="1740476732575" duration="3886000" />
|
||||
</task>
|
||||
<task id="LOCAL-00001" summary="1">
|
||||
<option name="closed" value="true" />
|
||||
@ -307,7 +458,23 @@
|
||||
<option name="project" value="LOCAL" />
|
||||
<updated>1736125962873</updated>
|
||||
</task>
|
||||
<option name="localTasksCounter" value="4" />
|
||||
<task id="LOCAL-00004" summary="1">
|
||||
<option name="closed" value="true" />
|
||||
<created>1736126943869</created>
|
||||
<option name="number" value="00004" />
|
||||
<option name="presentableId" value="LOCAL-00004" />
|
||||
<option name="project" value="LOCAL" />
|
||||
<updated>1736126943869</updated>
|
||||
</task>
|
||||
<task id="LOCAL-00005" summary="1">
|
||||
<option name="closed" value="true" />
|
||||
<created>1736126953818</created>
|
||||
<option name="number" value="00005" />
|
||||
<option name="presentableId" value="LOCAL-00005" />
|
||||
<option name="project" value="LOCAL" />
|
||||
<updated>1736126953818</updated>
|
||||
</task>
|
||||
<option name="localTasksCounter" value="6" />
|
||||
<servers />
|
||||
</component>
|
||||
<component name="TypeScriptGeneratedFilesManager">
|
||||
@ -329,8 +496,12 @@
|
||||
<option name="LAST_COMMIT_MESSAGE" value="1" />
|
||||
</component>
|
||||
<component name="com.intellij.coverage.CoverageDataManagerImpl">
|
||||
<SUITE FILE_PATH="coverage/Embedded_game$len_B_sai_2.coverage" NAME="len_B_sai_2 覆盖结果" MODIFIED="1739865950794" SOURCE_PROVIDER="com.intellij.coverage.DefaultCoverageFileProvider" RUNNER="coverage.py" COVERAGE_BY_TEST_ENABLED="false" COVERAGE_TRACING_ENABLED="false" WORKING_DIRECTORY="$PROJECT_DIR$/002_B_Car/xdl" />
|
||||
<SUITE FILE_PATH="coverage/Embedded_game$_.coverage" NAME="日志处理文件_真 覆盖结果" MODIFIED="1736126782615" SOURCE_PROVIDER="com.intellij.coverage.DefaultCoverageFileProvider" RUNNER="coverage.py" COVERAGE_BY_TEST_ENABLED="false" COVERAGE_TRACING_ENABLED="false" WORKING_DIRECTORY="$PROJECT_DIR$" />
|
||||
<SUITE FILE_PATH="coverage/Embedded_game$img2bin.coverage" NAME="img2bin 覆盖结果" MODIFIED="1740116992349" SOURCE_PROVIDER="com.intellij.coverage.DefaultCoverageFileProvider" RUNNER="coverage.py" COVERAGE_BY_TEST_ENABLED="false" COVERAGE_TRACING_ENABLED="false" WORKING_DIRECTORY="$PROJECT_DIR$" />
|
||||
<SUITE FILE_PATH="coverage/Embedded_game$LAB.coverage" NAME="获取颜色阈值LAB 覆盖结果" MODIFIED="1740459557626" SOURCE_PROVIDER="com.intellij.coverage.DefaultCoverageFileProvider" RUNNER="coverage.py" COVERAGE_BY_TEST_ENABLED="false" COVERAGE_TRACING_ENABLED="false" WORKING_DIRECTORY="$PROJECT_DIR$/002_B_Car" />
|
||||
<SUITE FILE_PATH="coverage/Embedded_game$test.coverage" NAME="test 覆盖结果" MODIFIED="1735795840761" SOURCE_PROVIDER="com.intellij.coverage.DefaultCoverageFileProvider" RUNNER="coverage.py" COVERAGE_BY_TEST_ENABLED="false" COVERAGE_TRACING_ENABLED="false" WORKING_DIRECTORY="$PROJECT_DIR$/002_B_Car/xdl" />
|
||||
<SUITE FILE_PATH="coverage/Embedded_game$.coverage" NAME="日志处理文件 覆盖结果" MODIFIED="1736126870474" SOURCE_PROVIDER="com.intellij.coverage.DefaultCoverageFileProvider" RUNNER="coverage.py" COVERAGE_BY_TEST_ENABLED="false" COVERAGE_TRACING_ENABLED="false" WORKING_DIRECTORY="$PROJECT_DIR$" />
|
||||
<SUITE FILE_PATH="coverage/Embedded_game$xml2txt.coverage" NAME="xml2txt 覆盖结果" MODIFIED="1740122801815" SOURCE_PROVIDER="com.intellij.coverage.DefaultCoverageFileProvider" RUNNER="coverage.py" COVERAGE_BY_TEST_ENABLED="false" COVERAGE_TRACING_ENABLED="false" WORKING_DIRECTORY="$PROJECT_DIR$" />
|
||||
<SUITE FILE_PATH="coverage/Embedded_game$.coverage" NAME="找圆 覆盖结果" MODIFIED="1740462384229" SOURCE_PROVIDER="com.intellij.coverage.DefaultCoverageFileProvider" RUNNER="coverage.py" COVERAGE_BY_TEST_ENABLED="false" COVERAGE_TRACING_ENABLED="false" WORKING_DIRECTORY="$PROJECT_DIR$/002_B_Car/xdl" />
|
||||
</component>
|
||||
</project>
|
||||
@ -1,24 +1,4 @@
|
||||
'''
|
||||
image caupture tool, capture images to SD card with directory switch support
|
||||
@usage 1. Change camera and lcd configs according to your hardware like `lcd.rotation`
|
||||
2. Prepare a SD card with SPI mode support, and format to FAT32 with MBR(msdos) partition
|
||||
3. Copy this boot.py to your SD card root directory
|
||||
4. Power off board, then insert SD card to board
|
||||
5. Power on, it will automatically create a directory like `cap_images_1`,
|
||||
the next time will be `cap_images_2` to avoid rewrite
|
||||
6. Capture images for one class,
|
||||
push `boot` button on board and release to capture one image,
|
||||
this will save images as `cap_images_1/0/0.jpg`,
|
||||
the name of image will automaitcally increase, like `0.jpg` `1.jpg` ...
|
||||
7. Long push `boot` botton on board to switch class,
|
||||
this will create a new directory like `cap_images_1/1/`,
|
||||
and later captured images will be saved to this directory like `cap_images_1/1/0.jpg`
|
||||
8. Power off board, pop out SD card, mount on your PC, now you get your images in your file brower
|
||||
@update https://maixhub.com/app/3
|
||||
@version v1.0.0
|
||||
@author neucrack@sipeed
|
||||
@license MIT
|
||||
'''
|
||||
|
||||
|
||||
import sensor, lcd
|
||||
from Maix import GPIO
|
||||
|
||||
Binary file not shown.
|
Before Width: | Height: | Size: 51 KiB After Width: | Height: | Size: 16 KiB |
112
002_B_Car/model-178789.nncase/main.py
Normal file
112
002_B_Car/model-178789.nncase/main.py
Normal file
@ -0,0 +1,112 @@
|
||||
# generated by maixhub, tested on maixpy3 v0.4.8
|
||||
# copy files to TF card and plug into board and power on
|
||||
import sensor, image, lcd, time
|
||||
import KPU as kpu
|
||||
from machine import UART
|
||||
import gc, sys
|
||||
from fpioa_manager import fm
|
||||
|
||||
input_size = (224, 224)
|
||||
labels = ['light', 'yellow', 'green', 'red']
|
||||
anchors = [0.63, 0.69, 0.81, 0.91, 0.75, 2.47, 0.94, 3.78, 0.69, 2.78]
|
||||
|
||||
def lcd_show_except(e):
|
||||
import uio
|
||||
err_str = uio.StringIO()
|
||||
sys.print_exception(e, err_str)
|
||||
err_str = err_str.getvalue()
|
||||
img = image.Image(size=input_size)
|
||||
img.draw_string(0, 10, err_str, scale=1, color=(0xff,0x00,0x00))
|
||||
lcd.display(img)
|
||||
|
||||
class Comm:
|
||||
def __init__(self, uart):
|
||||
self.uart = uart
|
||||
|
||||
def send_detect_result(self, objects, labels):
|
||||
msg = ""
|
||||
for obj in objects:
|
||||
pos = obj.rect()
|
||||
p = obj.value()
|
||||
idx = obj.classid()
|
||||
label = labels[idx]
|
||||
msg += "{}:{}:{}:{}:{}:{:.2f}:{}, ".format(pos[0], pos[1], pos[2], pos[3], idx, p, label)
|
||||
if msg:
|
||||
msg = msg[:-2] + "\n"
|
||||
self.uart.write(msg.encode())
|
||||
|
||||
def init_uart():
|
||||
fm.register(10, fm.fpioa.UART1_TX, force=True)
|
||||
fm.register(11, fm.fpioa.UART1_RX, force=True)
|
||||
|
||||
uart = UART(UART.UART1, 115200, 8, 0, 0, timeout=1000, read_buf_len=256)
|
||||
return uart
|
||||
|
||||
def main(anchors, labels = None, model_addr="/sd/m.kmodel", sensor_window=input_size, lcd_rotation=0, sensor_hmirror=False, sensor_vflip=False):
|
||||
sensor.reset()
|
||||
sensor.set_pixformat(sensor.RGB565)
|
||||
sensor.set_framesize(sensor.QVGA)
|
||||
sensor.set_windowing(sensor_window)
|
||||
sensor.set_hmirror(sensor_hmirror)
|
||||
sensor.set_vflip(sensor_vflip)
|
||||
sensor.run(1)
|
||||
|
||||
lcd.init(type=1)
|
||||
lcd.rotation(lcd_rotation)
|
||||
lcd.clear(lcd.WHITE)
|
||||
|
||||
if not labels:
|
||||
with open('labels.txt','r') as f:
|
||||
exec(f.read())
|
||||
if not labels:
|
||||
print("no labels.txt")
|
||||
img = image.Image(size=(320, 240))
|
||||
img.draw_string(90, 110, "no labels.txt", color=(255, 0, 0), scale=2)
|
||||
lcd.display(img)
|
||||
return 1
|
||||
try:
|
||||
img = image.Image("startup.jpg")
|
||||
lcd.display(img)
|
||||
except Exception:
|
||||
img = image.Image(size=(320, 240))
|
||||
img.draw_string(90, 110, "loading model...", color=(255, 255, 255), scale=2)
|
||||
lcd.display(img)
|
||||
|
||||
uart = init_uart()
|
||||
comm = Comm(uart)
|
||||
|
||||
try:
|
||||
task = None
|
||||
task = kpu.load(model_addr)
|
||||
kpu.init_yolo2(task, 0.7, 0.3, 5, anchors) # threshold:[0,1], nms_value: [0, 1]
|
||||
while(True):
|
||||
img = sensor.snapshot()
|
||||
t = time.ticks_ms()
|
||||
objects = kpu.run_yolo2(task, img)
|
||||
t = time.ticks_ms() - t
|
||||
if objects:
|
||||
for obj in objects:
|
||||
pos = obj.rect()
|
||||
img.draw_rectangle(pos)
|
||||
img.draw_string(pos[0], pos[1], "%s : %.2f" %(labels[obj.classid()], obj.value()), scale=2, color=(255, 0, 0))
|
||||
comm.send_detect_result(objects, labels)
|
||||
img.draw_string(0, 200, "t:%dms" %(t), scale=2, color=(255, 0, 0))
|
||||
img.draw_string(0, 2, "Upgrade to MaixCAM to use YOLOv8", scale=1.2, color=(255, 0, 0))
|
||||
img.draw_string(0, 30, "wiki.sipeed.com/maixcam", scale=1.2, color=(255, 0, 0))
|
||||
lcd.display(img)
|
||||
except Exception as e:
|
||||
raise e
|
||||
finally:
|
||||
if not task is None:
|
||||
kpu.deinit(task)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
try:
|
||||
# main(anchors = anchors, labels=labels, model_addr=0x300000, lcd_rotation=0)
|
||||
main(anchors = anchors, labels=labels, model_addr="/sd/model-178789.kmodel")
|
||||
except Exception as e:
|
||||
sys.print_exception(e)
|
||||
lcd_show_except(e)
|
||||
finally:
|
||||
gc.collect()
|
||||
BIN
002_B_Car/model-178789.nncase/model-178789.kmodel
Normal file
BIN
002_B_Car/model-178789.nncase/model-178789.kmodel
Normal file
Binary file not shown.
1748
002_B_Car/model-178789.nncase/report.json
Normal file
1748
002_B_Car/model-178789.nncase/report.json
Normal file
File diff suppressed because it is too large
Load Diff
112
002_B_Car/model-178950.nncase/main.py
Normal file
112
002_B_Car/model-178950.nncase/main.py
Normal file
@ -0,0 +1,112 @@
|
||||
# generated by maixhub, tested on maixpy3 v0.4.8
|
||||
# copy files to TF card and plug into board and power on
|
||||
import sensor, image, lcd, time
|
||||
import KPU as kpu
|
||||
from machine import UART
|
||||
import gc, sys
|
||||
from fpioa_manager import fm
|
||||
|
||||
input_size = (224, 224)
|
||||
labels = ['light']
|
||||
anchors = [1.06, 1.05, 0.91, 0.97, 0.72, 0.72, 0.84, 0.84, 1.06, 0.88]
|
||||
|
||||
def lcd_show_except(e):
|
||||
import uio
|
||||
err_str = uio.StringIO()
|
||||
sys.print_exception(e, err_str)
|
||||
err_str = err_str.getvalue()
|
||||
img = image.Image(size=input_size)
|
||||
img.draw_string(0, 10, err_str, scale=1, color=(0xff,0x00,0x00))
|
||||
lcd.display(img)
|
||||
|
||||
class Comm:
|
||||
def __init__(self, uart):
|
||||
self.uart = uart
|
||||
|
||||
def send_detect_result(self, objects, labels):
|
||||
msg = ""
|
||||
for obj in objects:
|
||||
pos = obj.rect()
|
||||
p = obj.value()
|
||||
idx = obj.classid()
|
||||
label = labels[idx]
|
||||
msg += "{}:{}:{}:{}:{}:{:.2f}:{}, ".format(pos[0], pos[1], pos[2], pos[3], idx, p, label)
|
||||
if msg:
|
||||
msg = msg[:-2] + "\n"
|
||||
self.uart.write(msg.encode())
|
||||
|
||||
def init_uart():
|
||||
fm.register(10, fm.fpioa.UART1_TX, force=True)
|
||||
fm.register(11, fm.fpioa.UART1_RX, force=True)
|
||||
|
||||
uart = UART(UART.UART1, 115200, 8, 0, 0, timeout=1000, read_buf_len=256)
|
||||
return uart
|
||||
|
||||
def main(anchors, labels = None, model_addr="/sd/m.kmodel", sensor_window=input_size, lcd_rotation=0, sensor_hmirror=False, sensor_vflip=False):
|
||||
sensor.reset()
|
||||
sensor.set_pixformat(sensor.RGB565)
|
||||
sensor.set_framesize(sensor.QVGA)
|
||||
sensor.set_windowing(sensor_window)
|
||||
sensor.set_hmirror(sensor_hmirror)
|
||||
sensor.set_vflip(sensor_vflip)
|
||||
sensor.run(1)
|
||||
|
||||
lcd.init(type=1)
|
||||
lcd.rotation(lcd_rotation)
|
||||
lcd.clear(lcd.WHITE)
|
||||
|
||||
if not labels:
|
||||
with open('labels.txt','r') as f:
|
||||
exec(f.read())
|
||||
if not labels:
|
||||
print("no labels.txt")
|
||||
img = image.Image(size=(320, 240))
|
||||
img.draw_string(90, 110, "no labels.txt", color=(255, 0, 0), scale=2)
|
||||
lcd.display(img)
|
||||
return 1
|
||||
try:
|
||||
img = image.Image("startup.jpg")
|
||||
lcd.display(img)
|
||||
except Exception:
|
||||
img = image.Image(size=(320, 240))
|
||||
img.draw_string(90, 110, "loading model...", color=(255, 255, 255), scale=2)
|
||||
lcd.display(img)
|
||||
|
||||
uart = init_uart()
|
||||
comm = Comm(uart)
|
||||
|
||||
try:
|
||||
task = None
|
||||
task = kpu.load(model_addr)
|
||||
kpu.init_yolo2(task, 0.4, 0.3, 5, anchors) # threshold:[0,1], nms_value: [0, 1]
|
||||
while(True):
|
||||
img = sensor.snapshot()
|
||||
t = time.ticks_ms()
|
||||
objects = kpu.run_yolo2(task, img)
|
||||
t = time.ticks_ms() - t
|
||||
if objects:
|
||||
for obj in objects:
|
||||
pos = obj.rect()
|
||||
img.draw_rectangle(pos)
|
||||
img.draw_string(pos[0], pos[1], "%s : %.2f" %(labels[obj.classid()], obj.value()), scale=2, color=(255, 0, 0))
|
||||
comm.send_detect_result(objects, labels)
|
||||
img.draw_string(0, 200, "t:%dms" %(t), scale=2, color=(255, 0, 0))
|
||||
img.draw_string(0, 2, "Upgrade to MaixCAM to use YOLOv8", scale=1.2, color=(255, 0, 0))
|
||||
img.draw_string(0, 30, "wiki.sipeed.com/maixcam", scale=1.2, color=(255, 0, 0))
|
||||
lcd.display(img)
|
||||
except Exception as e:
|
||||
raise e
|
||||
finally:
|
||||
if not task is None:
|
||||
kpu.deinit(task)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
try:
|
||||
# main(anchors = anchors, labels=labels, model_addr=0x300000, lcd_rotation=0)
|
||||
main(anchors = anchors, labels=labels, model_addr="/sd/model-178950.kmodel")
|
||||
except Exception as e:
|
||||
sys.print_exception(e)
|
||||
lcd_show_except(e)
|
||||
finally:
|
||||
gc.collect()
|
||||
BIN
002_B_Car/model-178950.nncase/model-178950.kmodel
Normal file
BIN
002_B_Car/model-178950.nncase/model-178950.kmodel
Normal file
Binary file not shown.
1503
002_B_Car/model-178950.nncase/report.json
Normal file
1503
002_B_Car/model-178950.nncase/report.json
Normal file
File diff suppressed because it is too large
Load Diff
BIN
002_B_Car/model-179117.nncase.zip
Normal file
BIN
002_B_Car/model-179117.nncase.zip
Normal file
Binary file not shown.
BIN
002_B_Car/model-179147.nncase.zip
Normal file
BIN
002_B_Car/model-179147.nncase.zip
Normal file
Binary file not shown.
113
002_B_Car/model-179299.nncase/main.py
Normal file
113
002_B_Car/model-179299.nncase/main.py
Normal file
@ -0,0 +1,113 @@
|
||||
# generated by maixhub, tested on maixpy3 v0.4.8
|
||||
# copy files to TF card and plug into board and power on
|
||||
import sensor, image, lcd, time
|
||||
import KPU as kpu
|
||||
from machine import UART
|
||||
import gc, sys
|
||||
from fpioa_manager import fm
|
||||
|
||||
input_size = (224, 224)
|
||||
labels = ['light']
|
||||
anchors = [0.84, 0.5, 0.78, 0.81, 0.84, 0.91, 0.78, 0.34, 0.72, 0.72]
|
||||
|
||||
def lcd_show_except(e):
|
||||
import uio
|
||||
err_str = uio.StringIO()
|
||||
sys.print_exception(e, err_str)
|
||||
err_str = err_str.getvalue()
|
||||
img = image.Image(size=input_size)
|
||||
img.draw_string(0, 10, err_str, scale=1, color=(0xff,0x00,0x00))
|
||||
lcd.display(img)
|
||||
|
||||
class Comm:
|
||||
def __init__(self, uart):
|
||||
self.uart = uart
|
||||
|
||||
def send_detect_result(self, objects, labels):
|
||||
msg = ""
|
||||
for obj in objects:
|
||||
pos = obj.rect()
|
||||
p = obj.value()
|
||||
idx = obj.classid()
|
||||
label = labels[idx]
|
||||
msg += "{}:{}:{}:{}:{}:{:.2f}:{}, ".format(pos[0], pos[1], pos[2], pos[3], idx, p, label)
|
||||
if msg:
|
||||
msg = msg[:-2] + "\n"
|
||||
self.uart.write(msg.encode())
|
||||
|
||||
def init_uart():
|
||||
fm.register(10, fm.fpioa.UART1_TX, force=True)
|
||||
fm.register(11, fm.fpioa.UART1_RX, force=True)
|
||||
|
||||
uart = UART(UART.UART1, 115200, 8, 0, 0, timeout=1000, read_buf_len=256)
|
||||
return uart
|
||||
|
||||
def main(anchors, labels = None, model_addr="/sd/m.kmodel", sensor_window=input_size, lcd_rotation=0, sensor_hmirror=False, sensor_vflip=False):
|
||||
sensor.reset()
|
||||
sensor.set_pixformat(sensor.RGB565)
|
||||
sensor.set_framesize(sensor.QVGA)
|
||||
sensor.set_windowing(sensor_window)
|
||||
sensor.set_hmirror(sensor_hmirror)
|
||||
sensor.set_vflip(sensor_vflip)
|
||||
sensor.run(1)
|
||||
|
||||
lcd.init(type=1)
|
||||
lcd.rotation(lcd_rotation)
|
||||
lcd.clear(lcd.WHITE)
|
||||
|
||||
if not labels:
|
||||
with open('labels.txt','r') as f:
|
||||
exec(f.read())
|
||||
if not labels:
|
||||
print("no labels.txt")
|
||||
img = image.Image(size=(320, 240))
|
||||
img.draw_string(90, 110, "no labels.txt", color=(255, 0, 0), scale=2)
|
||||
lcd.display(img)
|
||||
return 1
|
||||
try:
|
||||
img = image.Image("startup.jpg")
|
||||
lcd.display(img)
|
||||
except Exception:
|
||||
img = image.Image(size=(320, 240))
|
||||
img.draw_string(90, 110, "loading model...", color=(255, 255, 255), scale=2)
|
||||
lcd.display(img)
|
||||
|
||||
uart = init_uart()
|
||||
comm = Comm(uart)
|
||||
|
||||
try:
|
||||
task = None
|
||||
task = kpu.load(model_addr)
|
||||
kpu.init_yolo2(task, 0.3, 0.3, 5, anchors) # threshold:[0,1], nms_value: [0, 1]
|
||||
while(True):
|
||||
t = time.ticks_ms()
|
||||
img = sensor.snapshot()
|
||||
img = img.rotation_corr(z_rotation=180) # 旋转90度
|
||||
objects = kpu.run_yolo2(task, img)
|
||||
t = time.ticks_ms() - t
|
||||
if objects:
|
||||
for obj in objects:
|
||||
pos = obj.rect()
|
||||
img.draw_rectangle(pos)
|
||||
img.draw_string(pos[0], pos[1], "%s : %.2f" %(labels[obj.classid()], obj.value()), scale=2, color=(255, 0, 0))
|
||||
comm.send_detect_result(objects, labels)
|
||||
img.draw_string(0, 200, "t:%dms" %(t), scale=2, color=(255, 0, 0))
|
||||
# img.draw_string(0, 2, "Upgrade to MaixCAM to use YOLOv8", scale=1.2, color=(255, 0, 0))
|
||||
# img.draw_string(0, 30, "wiki.sipeed.com/maixcam", scale=1.2, color=(255, 0, 0))
|
||||
lcd.display(img)
|
||||
except Exception as e:
|
||||
raise e
|
||||
finally:
|
||||
if not task is None:
|
||||
kpu.deinit(task)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
try:
|
||||
# main(anchors = anchors, labels=labels, model_addr=0x300000, lcd_rotation=0)
|
||||
main(anchors = anchors, labels=labels, model_addr="/sd/model-179299.kmodel")
|
||||
except Exception as e:
|
||||
sys.print_exception(e)
|
||||
lcd_show_except(e)
|
||||
finally:
|
||||
gc.collect()
|
||||
BIN
002_B_Car/model-179299.nncase/model-179299.kmodel
Normal file
BIN
002_B_Car/model-179299.nncase/model-179299.kmodel
Normal file
Binary file not shown.
1503
002_B_Car/model-179299.nncase/report.json
Normal file
1503
002_B_Car/model-179299.nncase/report.json
Normal file
File diff suppressed because it is too large
Load Diff
@ -128,7 +128,7 @@ class Mainlen():
|
||||
self.tracking(img)
|
||||
elif Flag_qr: # 二维码#
|
||||
print("识别二维码")
|
||||
self.discem_QR(img)#r任务9
|
||||
self.discem_QR(img)
|
||||
# Flag_qr = False
|
||||
elif Flag_light: # 红绿灯
|
||||
print("识别红绿灯")
|
||||
@ -252,16 +252,14 @@ class Mainlen():
|
||||
# result = self.remove_chinese_chars(result) # 剔除中文
|
||||
print(result)
|
||||
|
||||
if len(result) > 10: # 车牌
|
||||
result = self.jiajin(result)
|
||||
result = self.binary_to_hexadecimal(result)
|
||||
count = 2
|
||||
|
||||
else: # 公式
|
||||
if len(result) < 10: # 车牌
|
||||
# result = self.jiajin(result)
|
||||
# result = self.binary_to_hexadecimal(result)
|
||||
count = 1
|
||||
|
||||
if res == len(res_QR) - 1:
|
||||
if_end = 0x00
|
||||
else: # 公式
|
||||
continue
|
||||
|
||||
|
||||
print("count: ", count)
|
||||
print("if_end: ", if_end)
|
||||
@ -275,12 +273,8 @@ class Mainlen():
|
||||
self.uart.write(bytes([0x06]))
|
||||
print(len(result))
|
||||
self.uart.write(bytes([count]))
|
||||
self.uart.write(bytes([if_end]))
|
||||
if count==2:
|
||||
self.uart.write(bytes([2]))
|
||||
self.uart.write(bytes([int(result[1]+result[0], 16)]))
|
||||
self.uart.write(bytes([int(result[3]+result[2], 16)]))
|
||||
else:
|
||||
# self.uart.write(bytes([if_end]))
|
||||
if count==1:
|
||||
self.uart.write(bytes([len(result)]))
|
||||
for qr_data in result:
|
||||
self.uart.write(bytes([ord(qr_data)]))
|
||||
|
||||
@ -1,28 +0,0 @@
|
||||
import sensor, image, lcd, time
|
||||
from Maix import UART
|
||||
from fpioa_manager import fm
|
||||
|
||||
# 初始化摄像头
|
||||
sensor.reset()
|
||||
sensor.set_pixformat(sensor.RGB565)
|
||||
sensor.set_framesize(sensor.QVGA)
|
||||
sensor.skip_frames(time=2000)
|
||||
|
||||
# 初始化 LCD
|
||||
lcd.init()
|
||||
lcd.rotation(2)
|
||||
|
||||
# 初始化 UART
|
||||
fm.register(7, fm.fpioa.UART1_TX) # TX 引脚映射
|
||||
fm.register(6, fm.fpioa.UART1_RX) # RX 引脚映射
|
||||
uart = UART(UART.UART1, 115200, read_buf_len=4096)
|
||||
|
||||
print("按下按钮捕获图片并发送")
|
||||
|
||||
while True:
|
||||
img = sensor.snapshot()
|
||||
lcd.display(img)
|
||||
if uart.read(1): # 等待串口信号（例如从电脑发送字符）
|
||||
print("开始发送图片...")
|
||||
uart.write(img.compress(quality=90).to_bytes()) # 压缩并发送图片
|
||||
print("图片发送完成")
|
||||
@ -8,7 +8,7 @@ from machine import Timer, PWM
|
||||
lcd.init(freq=15000000) # 初始化LCD
|
||||
sensor.reset() # 复位和初始化摄像头
|
||||
sensor.set_vflip(1) # 将摄像头设置成后置方式（所见即所得）
|
||||
sensor.set_pixformat(sensor.RGB565) # 设置像素格式为彩色 RGB565
|
||||
sensor.set_pixformat(sensor.RGB565)# 设置像素格式为彩色 RGB565
|
||||
sensor.set_framesize(sensor.QVGA) # 设置帧大小为 QVGA (320x240)
|
||||
sensor.set_auto_gain(False)
|
||||
sensor.set_auto_whitebal(False)
|
||||
@ -18,12 +18,8 @@ sensor.skip_frames(time=200) # 等待设置生效
|
||||
def draw_circles():
|
||||
"""
|
||||
检测并绘制图像中的圆形，仅在图像的右半部分进行检测，
|
||||
累计识别条件为超过4秒或识别到3个圆
|
||||
"""
|
||||
result_cache = [] # 缓存每次有结果的识别
|
||||
max_results = 3 # 累计识别到的圆形数量
|
||||
start_time = time.ticks_ms() # 记录开始时间（毫秒）
|
||||
|
||||
def circles_overlap(c1, c2):
|
||||
"""判断两个圆是否重叠"""
|
||||
dx = c1.x() - c2.x()
|
||||
@ -35,109 +31,111 @@ def draw_circles():
|
||||
"""尝试合并新检测到的圆与缓存中的圆"""
|
||||
for existing in cache:
|
||||
if circles_overlap(new_circle, existing):
|
||||
return # 如果重叠，不加入新的圆
|
||||
return False # 如果重叠，不加入新的圆
|
||||
cache.append(new_circle) # 如果没有重叠，添加到缓存中
|
||||
return True
|
||||
|
||||
def calculate_lab(area, img):
|
||||
"""计算给定区域的中间 1/3 的平均 LAB 值"""
|
||||
mean_l, mean_a, mean_b = 0, 0, 0
|
||||
pixel_count = 0
|
||||
start_y = area[1] + area[3] // 3
|
||||
end_y = start_y + area[3] // 3
|
||||
for y in range(start_y, end_y):
|
||||
for x in range(area[0], area[0] + area[2]):
|
||||
l, a, b = img.get_statistics(roi=(x, y, 1, 1)).l_mean(), \
|
||||
img.get_statistics(roi=(x, y, 1, 1)).a_mean(), \
|
||||
img.get_statistics(roi=(x, y, 1, 1)).b_mean()
|
||||
mean_l += l
|
||||
mean_a += a
|
||||
mean_b += b
|
||||
pixel_count += 1
|
||||
mean_l //= pixel_count
|
||||
mean_a //= pixel_count
|
||||
mean_b //= pixel_count
|
||||
return mean_l, mean_a, mean_b
|
||||
|
||||
def determine_farthest_circle(circles, img):
|
||||
"""找到与其他圆 LAB 差异最大的圆"""
|
||||
start_time = time.ticks_ms() # 开始时间
|
||||
|
||||
max_distance = -1
|
||||
farthest_circle = None
|
||||
farthest_lab = None
|
||||
|
||||
for i, c1 in enumerate(circles):
|
||||
area1 = (c1.x() - c1.r(), c1.y() - c1.r(), 2 * c1.r(), 2 * c1.r())
|
||||
lab1 = calculate_lab(area1, img)
|
||||
total_distance = 0
|
||||
|
||||
for j, c2 in enumerate(circles):
|
||||
if i != j:
|
||||
area2 = (c2.x() - c2.r(), c2.y() - c2.r(), 2 * c2.r(), 2 * c2.r())
|
||||
lab2 = calculate_lab(area2, img)
|
||||
total_distance += ((lab1[0] - lab2[0]) ** 2 + (lab1[1] - lab2[1]) ** 2 + (lab1[2] - lab2[2]) ** 2) ** 0.5
|
||||
|
||||
if total_distance > max_distance:
|
||||
max_distance = total_distance
|
||||
farthest_circle = c1
|
||||
farthest_lab = lab1
|
||||
|
||||
elapsed_time = time.ticks_diff(time.ticks_ms(), start_time) # 计算耗时
|
||||
print("determine_farthest_circle 耗时:" ,elapsed_time," ms")
|
||||
return farthest_circle, farthest_lab
|
||||
"""计算给定区域的平均 LAB 值"""
|
||||
stats = img.get_statistics(roi=area)
|
||||
return stats.l_mean(), stats.a_mean(), stats.b_mean()
|
||||
|
||||
def determine_color_from_lab(lab):
|
||||
"""根据 LAB 值判断颜色倾向"""
|
||||
"""
|
||||
返回最接近的颜色以及对应的距离
|
||||
|
||||
:param lab: (l, a, b) 三元组
|
||||
:return: (closest_color, min_distance)
|
||||
"""
|
||||
# 你可以根据实际情况添加或修改更多预设颜色及其 LAB
|
||||
predefined_colors = {
|
||||
"红色": (53, 80, 67),
|
||||
"黄色": (97, -21, 94),
|
||||
"绿色": (87, -86, 83)
|
||||
"红色": (50, 38, 1),
|
||||
"黄色": (86, 2, 27),
|
||||
"绿色": (75, -29, 15)
|
||||
}
|
||||
min_distance = float('inf')
|
||||
closest_color = "灰色"
|
||||
for color_name, (l, a, b) in predefined_colors.items():
|
||||
distance = ((lab[0] - l) ** 2 + (lab[1] - a) ** 2 + (lab[2] - b) ** 2) ** 0.5
|
||||
closest_color = "灰色" # 默认或者未匹配时
|
||||
|
||||
# 打印一下实际检测到的LAB，便于调试
|
||||
# print("检测到的LAB:", lab)
|
||||
|
||||
for color_name, (l_ref, a_ref, b_ref) in predefined_colors.items():
|
||||
distance = ((lab[0] - l_ref) ** 2 +
|
||||
(lab[1] - a_ref) ** 2 +
|
||||
(lab[2] - b_ref) ** 2) ** 0.5
|
||||
if distance < min_distance:
|
||||
min_distance = distance
|
||||
closest_color = color_name
|
||||
return closest_color
|
||||
if min_distance < 10:
|
||||
print("最接近的颜色={}, 距离={:.2f}".format(closest_color, min_distance))
|
||||
# print("最接近的颜色={}, 距离={:.2f}".format(closest_color, min_distance))
|
||||
return closest_color, min_distance
|
||||
|
||||
count=0
|
||||
|
||||
min_color='red'
|
||||
min_distance=50
|
||||
while True:
|
||||
img = sensor.snapshot()
|
||||
roi = (img.width() // 2, 0, img.width() // 2, img.height()) # 只处理右半部分
|
||||
# 查找圆形
|
||||
circles = img.find_circles(roi=roi, threshold=2500, x_margin=10, y_margin=10, r_margin=10,
|
||||
r_min=2, r_max=100, r_step=2)
|
||||
# 只处理右半部分
|
||||
roi = (img.width() // 2, 0, img.width() // 2, img.height())
|
||||
|
||||
# 在此根据整体亮度简单调参，阈值可自行调整
|
||||
threshold = max(1000, min(5000, int(img.get_statistics().l_mean() * 50)))
|
||||
|
||||
# find_circles 参数可根据实际效果调整
|
||||
circles = img.find_circles(roi=roi,
|
||||
threshold=2500,
|
||||
x_margin=20,
|
||||
y_margin=20,
|
||||
r_margin=20,
|
||||
r_min=15,
|
||||
r_max=30,
|
||||
r_step=2)
|
||||
|
||||
if circles:
|
||||
# 如果有结果，尝试合并到结果缓存
|
||||
closest_circle = None
|
||||
closest_distance = float('inf')
|
||||
closest_color = "未知"
|
||||
|
||||
for c in circles:
|
||||
merge_circles(c, result_cache)
|
||||
area = (c.x() - c.r(), c.y() - c.r(), 2*c.r(), 2*c.r())
|
||||
lab_val = calculate_lab(area, img)
|
||||
color_name, distance = determine_color_from_lab(lab_val)
|
||||
# 找出最小距离（与预设颜色最相似）的那个圆
|
||||
if distance < closest_distance:
|
||||
closest_distance = distance
|
||||
closest_color = color_name
|
||||
closest_circle = c
|
||||
if closest_circle is None:
|
||||
print('没有识别到')
|
||||
continue
|
||||
elif closest_distance<20:
|
||||
area = (closest_circle.x() - closest_circle.r(),
|
||||
closest_circle.y() - closest_circle.r(),
|
||||
2*closest_circle.r(), 2*closest_circle.r())
|
||||
# 画框和画圆
|
||||
img.draw_rectangle(area, color=(255, 0, 0))
|
||||
img.draw_circle(closest_circle.x(), closest_circle.y(), closest_circle.r(), color=(255, 0, 0))
|
||||
print("最终识别到的颜色={}, 距离={:.2f}".format(closest_color, closest_distance))
|
||||
min_distance=50
|
||||
print("{}轮检测耗时: {} ms".format(count,time.ticks_diff(time.ticks_ms(), start_time)))
|
||||
start_time = time.ticks_ms()
|
||||
count+=1
|
||||
|
||||
# 检查是否达到累计条件或超时
|
||||
current_time = time.ticks_ms()
|
||||
elapsed_time = time.ticks_diff(current_time, start_time) # 计算耗时
|
||||
if len(result_cache) >= max_results or elapsed_time > 4000:
|
||||
# 找到 LAB 最不一样的圆
|
||||
farthest_circle, farthest_lab = determine_farthest_circle(result_cache, img)
|
||||
else:
|
||||
if closest_distance<min_distance:
|
||||
min_color=closest_color
|
||||
min_distance=closest_distance
|
||||
if time.ticks_diff(time.ticks_ms(), start_time) > 5000:
|
||||
print("最终识别到的颜色={}, 距离={:.2f}".format(min_color, min_distance))
|
||||
print("{}轮检测耗时: {} ms".format(count,time.ticks_diff(time.ticks_ms(), start_time)))
|
||||
start_time = time.ticks_ms()
|
||||
|
||||
# 绘制并输出结果
|
||||
if farthest_circle:
|
||||
area = (farthest_circle.x() - farthest_circle.r(), farthest_circle.y() - farthest_circle.r(),
|
||||
2 * farthest_circle.r(), 2 * farthest_circle.r())
|
||||
color = determine_color_from_lab(farthest_lab)
|
||||
if color in ["红色", "黄色", "绿色"]:
|
||||
img.draw_rectangle(area, color=(255, 255, 255)) # 画圆外接矩形
|
||||
img.draw_circle(farthest_circle.x(), farthest_circle.y(), farthest_circle.r(), color=(255, 0, 0))
|
||||
print("颜色倾向: ", color)
|
||||
min_distance=50
|
||||
count+=1
|
||||
|
||||
current_time = time.ticks_ms()
|
||||
elapsed_time = time.ticks_diff(current_time, start_time) # 计算耗时
|
||||
print("保留的圆形数量：", len(result_cache))
|
||||
print("耗时:", elapsed_time, "ms")
|
||||
result_cache = [] # 清空缓存
|
||||
start_time = time.ticks_ms() # 重置开始时间
|
||||
|
||||
time.sleep(0.1)
|
||||
|
||||
# 主函数调用
|
||||
if __name__ == "__main__":
|
||||
|
||||
142
002_B_Car/xdl/找圆2.py
Normal file
142
002_B_Car/xdl/找圆2.py
Normal file
@ -0,0 +1,142 @@
|
||||
# -*- coding:utf-8 -*-
|
||||
# @Author len
|
||||
# @Create 2023/11/30 11:08
|
||||
|
||||
import sensor, lcd, time
|
||||
from machine import Timer, PWM
|
||||
|
||||
lcd.init(freq=15000000) # 初始化LCD
|
||||
sensor.reset() # 复位和初始化摄像头
|
||||
sensor.set_vflip(1) # 将摄像头设置成后置方式（所见即所得）
|
||||
sensor.set_pixformat(sensor.RGB565)# 设置像素格式为彩色 RGB565
|
||||
sensor.set_framesize(sensor.QVGA) # 设置帧大小为 QVGA (320x240)
|
||||
sensor.set_auto_gain(False)
|
||||
sensor.set_auto_whitebal(False)
|
||||
sensor.set_auto_gain(0, 0)
|
||||
sensor.skip_frames(time=200) # 等待设置生效
|
||||
|
||||
def draw_circles():
|
||||
"""
|
||||
检测并绘制图像中的圆形，仅在图像的右半部分进行检测，
|
||||
"""
|
||||
start_time = time.ticks_ms() # 记录开始时间（毫秒）
|
||||
def circles_overlap(c1, c2):
|
||||
"""判断两个圆是否重叠"""
|
||||
dx = c1.x() - c2.x()
|
||||
dy = c1.y() - c2.y()
|
||||
distance = (dx**2 + dy**2)**0.5
|
||||
return distance < (c1.r() + c2.r())
|
||||
|
||||
def merge_circles(new_circle, cache):
|
||||
"""尝试合并新检测到的圆与缓存中的圆"""
|
||||
for existing in cache:
|
||||
if circles_overlap(new_circle, existing):
|
||||
return False # 如果重叠，不加入新的圆
|
||||
cache.append(new_circle) # 如果没有重叠，添加到缓存中
|
||||
return True
|
||||
|
||||
def calculate_lab(area, img):
|
||||
"""计算给定区域的平均 LAB 值"""
|
||||
stats = img.get_statistics(roi=area)
|
||||
return stats.l_mean(), stats.a_mean(), stats.b_mean()
|
||||
|
||||
def determine_color_from_lab(lab):
|
||||
"""
|
||||
返回最接近的颜色以及对应的距离
|
||||
|
||||
:param lab: (l, a, b) 三元组
|
||||
:return: (closest_color, min_distance)
|
||||
"""
|
||||
# 你可以根据实际情况添加或修改更多预设颜色及其 LAB
|
||||
predefined_colors = {
|
||||
"红色": (50, 38, 1),
|
||||
"黄色": (86, 2, 27),
|
||||
"绿色": (75, -29, 15)
|
||||
}
|
||||
min_distance = float('inf')
|
||||
closest_color = "灰色" # 默认或者未匹配时
|
||||
|
||||
# 打印一下实际检测到的LAB，便于调试
|
||||
# print("检测到的LAB:", lab)
|
||||
|
||||
for color_name, (l_ref, a_ref, b_ref) in predefined_colors.items():
|
||||
distance = ((lab[0] - l_ref) ** 2 +
|
||||
(lab[1] - a_ref) ** 2 +
|
||||
(lab[2] - b_ref) ** 2) ** 0.5
|
||||
if distance < min_distance:
|
||||
min_distance = distance
|
||||
closest_color = color_name
|
||||
if min_distance < 10:
|
||||
print("最接近的颜色={}, 距离={:.2f}".format(closest_color, min_distance))
|
||||
# print("最接近的颜色={}, 距离={:.2f}".format(closest_color, min_distance))
|
||||
return closest_color, min_distance
|
||||
|
||||
count=0
|
||||
|
||||
min_color='red'
|
||||
min_distance=50
|
||||
while True:
|
||||
img = sensor.snapshot()
|
||||
# 只处理右半部分
|
||||
roi = (img.width() // 2, 0, img.width() // 2, img.height())
|
||||
|
||||
# 在此根据整体亮度简单调参，阈值可自行调整
|
||||
threshold = max(1000, min(5000, int(img.get_statistics().l_mean() * 50)))
|
||||
|
||||
# find_circles 参数可根据实际效果调整
|
||||
circles = img.find_circles(roi=roi,
|
||||
threshold=2500,
|
||||
x_margin=20,
|
||||
y_margin=20,
|
||||
r_margin=20,
|
||||
r_min=15,
|
||||
r_max=30,
|
||||
r_step=2)
|
||||
|
||||
if circles:
|
||||
closest_circle = None
|
||||
closest_distance = float('inf')
|
||||
closest_color = "未知"
|
||||
|
||||
for c in circles:
|
||||
area = (c.x() - c.r(), c.y() - c.r(), 2*c.r(), 2*c.r())
|
||||
lab_val = calculate_lab(area, img)
|
||||
color_name, distance = determine_color_from_lab(lab_val)
|
||||
# 找出最小距离（与预设颜色最相似）的那个圆
|
||||
if distance < closest_distance:
|
||||
closest_distance = distance
|
||||
closest_color = color_name
|
||||
closest_circle = c
|
||||
if closest_circle is None:
|
||||
print('没有识别到')
|
||||
continue
|
||||
elif closest_distance<20:
|
||||
area = (closest_circle.x() - closest_circle.r(),
|
||||
closest_circle.y() - closest_circle.r(),
|
||||
2*closest_circle.r(), 2*closest_circle.r())
|
||||
# 画框和画圆
|
||||
img.draw_rectangle(area, color=(255, 0, 0))
|
||||
img.draw_circle(closest_circle.x(), closest_circle.y(), closest_circle.r(), color=(255, 0, 0))
|
||||
print("最终识别到的颜色={}, 距离={:.2f}".format(closest_color, closest_distance))
|
||||
min_distance=50
|
||||
print("{}轮检测耗时: {} ms".format(count,time.ticks_diff(time.ticks_ms(), start_time)))
|
||||
start_time = time.ticks_ms()
|
||||
count+=1
|
||||
|
||||
else:
|
||||
if closest_distance<min_distance:
|
||||
min_color=closest_color
|
||||
min_distance=closest_distance
|
||||
if time.ticks_diff(time.ticks_ms(), start_time) > 5000:
|
||||
print("最终识别到的颜色={}, 距离={:.2f}".format(min_color, min_distance))
|
||||
print("{}轮检测耗时: {} ms".format(count,time.ticks_diff(time.ticks_ms(), start_time)))
|
||||
start_time = time.ticks_ms()
|
||||
|
||||
min_distance=50
|
||||
count+=1
|
||||
|
||||
|
||||
|
||||
# 主函数调用
|
||||
if __name__ == "__main__":
|
||||
draw_circles()
|
||||
@ -141,6 +141,8 @@ class Mainlen():
|
||||
sensor.set_pixformat(sensor.RGB565) # 设置像素格式为彩色 RGB565
|
||||
# sensor.set_pixformat(sensor.GRAYSCALE) # 设置像素格式为灰色
|
||||
sensor.set_framesize(sensor.QVGA) # 设置帧大小为QVGA（320×240）
|
||||
# sensor.set_framesize(sensor.FRAME_240X240 ) # 设置帧大小为QVGA（320×240）
|
||||
|
||||
sensor.set_vflip(1) # 后置模式
|
||||
sensor.skip_frames(30) # # 跳过前30帧
|
||||
|
||||
@ -151,6 +153,8 @@ class Mainlen():
|
||||
sensor.set_pixformat(sensor.RGB565) # 设置像素格式为彩色 RGB565
|
||||
# sensor.set_pixformat(sensor.GRAYSCALE) # 设置像素格式为灰色
|
||||
sensor.set_framesize(sensor.QVGA) # 设置帧大小为 QVGA (320x240)
|
||||
# sensor.set_framesize(sensor.FRAME_240X240) # 设置帧大小为 QVGA (320x240)
|
||||
|
||||
# sensor.set_windowing((224, 224)) # 设置摄像头的窗口大小
|
||||
sensor.set_auto_gain(False)
|
||||
sensor.set_auto_whitebal(False)
|
||||
|
||||
@ -11,9 +11,10 @@ sensor.set_auto_gain(0,0)
|
||||
sensor.skip_frames(time = 200) # 等待设置生效
|
||||
|
||||
# 阈值设置
|
||||
thresholds = [(35, 100, 6, 127, 0, 127), # 红色阈值
|
||||
(25, 100, -6, 127, 5, 43), # 黄色阈值
|
||||
(87, 100, -59, 127, -10, 127)] # 绿色阈值
|
||||
thresholds = [(123, 143, 161, 181, 136, 156), # 红色阈值
|
||||
(207, 227, 126, 146, 155, 175), # 黄色阈值
|
||||
(196, 216, 103, 123, 135, 155)] # 绿色阈值
|
||||
|
||||
|
||||
# while True:
|
||||
img = sensor.snapshot()
|
||||
|
||||
@ -34,9 +34,9 @@ Servo(15)
|
||||
# (25, 100, -6, 127, 5, 43, "黄色"), # 黄色阈值
|
||||
# (52, 100, -128, -5, 5, 127, "绿色")] # 绿色阈值
|
||||
|
||||
thresholds = [(51, 62, 33, 101, 14, 127, "红色"), # 红色阈值
|
||||
(39, 98, -17, 21, 17, 57, "黄色"), # 黄色阈值
|
||||
(44, 90, -61, -13, -3, 30, "绿色")] # 绿色阈值
|
||||
thresholds = [(43, 68, 7, 74, 1, 46, "红色"), # 红色阈值
|
||||
(48, 100, -23, 19, 4, 55, "黄色"), # 黄色阈值
|
||||
(40, 93, -57, -7, -8, 29, "绿色")] # 绿色阈值
|
||||
|
||||
while True:
|
||||
img = sensor.snapshot()
|
||||
@ -45,9 +45,9 @@ while True:
|
||||
max_blob_color = ""
|
||||
max_blob_color_index = 0
|
||||
|
||||
# 找圆
|
||||
circles = img.find_circles(threshold=3500, x_margin=10, y_margin=10, r_margin=10,
|
||||
r_min=2, r_max=100, r_step=2)
|
||||
# # 找圆
|
||||
# circles = img.find_circles(threshold=3500, x_margin=10, y_margin=10, r_margin=10,
|
||||
# r_min=2, r_max=100, r_step=2)
|
||||
|
||||
for index, threshold in enumerate(thresholds):
|
||||
# 查找每种颜色的色块
|
||||
@ -60,15 +60,17 @@ while True:
|
||||
max_blob_size = blob.pixels()
|
||||
max_blob_color = threshold[-1] # 颜色标签
|
||||
max_blob_color_index = index
|
||||
print(circles)
|
||||
# print(circles)
|
||||
# 画圆
|
||||
for c in circles:
|
||||
area = (c.x() - c.r(), c.y() - c.r(), 2 * c.r(), 2 * c.r())
|
||||
img.draw_rectangle(area, color=(255, 255, 255))
|
||||
# for c in circles:
|
||||
# area = (c.x() - c.r(), c.y() - c.r(), 2 * c.r(), 2 * c.r())
|
||||
# img.draw_rectangle(area, color=(255, 255, 255))
|
||||
# 绘制最大色块的矩形和中心十字，并输出颜色
|
||||
if max_blob:
|
||||
img.draw_rectangle(max_blob[0:4])
|
||||
img.draw_cross(max_blob[5], max_blob[6])
|
||||
print("最大色块的颜色是:", max_blob_color, max_blob_color_index)
|
||||
else:
|
||||
print("没有找到色块")
|
||||
|
||||
lcd.display(img)
|
||||
|
||||
@ -22,22 +22,22 @@ def Servo(angle):
|
||||
S1 = PWM(tim_pwm, freq=50, duty=0, pin=17)
|
||||
S1.duty((angle+90)/180*10+2.5)
|
||||
|
||||
Servo(15)
|
||||
# Servo(15)
|
||||
# 摄像头初始化
|
||||
sensor.reset()
|
||||
sensor.set_pixformat(sensor.RGB565)
|
||||
sensor.set_framesize(sensor.QVGA)
|
||||
sensor.skip_frames(40)
|
||||
sensor.set_vflip(1) # 摄像头后置方式
|
||||
sensor.set_pixformat(sensor.RGB565) # 设置像素格式为彩色 RGB565
|
||||
#sensor.set_pixformat(sensor.GRAYSCALE) # 设置像素格式为灰色
|
||||
sensor.set_framesize(sensor.QVGA) # 设置帧大小为 QVGA (320x240)
|
||||
sensor.set_auto_gain(False)
|
||||
sensor.set_auto_whitebal(False)
|
||||
sensor.set_auto_gain(0,0)
|
||||
|
||||
|
||||
|
||||
lcd.init() # LCD初始化
|
||||
# lcd.init() # LCD初始化
|
||||
|
||||
# 初始化 BOOT 键
|
||||
boot_pin = 16 # 根据您的板子修改对应的引脚号
|
||||
@ -51,35 +51,7 @@ taking_pictures = False
|
||||
|
||||
count = 0
|
||||
while(True):
|
||||
img = sensor.snapshot()
|
||||
|
||||
# 检查 BOOT 键是否被按下
|
||||
if key.value() == 0:
|
||||
# 等待按键释放
|
||||
while key.value() == 0:
|
||||
time.sleep_ms(20)
|
||||
# 改变拍照状态
|
||||
taking_pictures = not taking_pictures
|
||||
if taking_pictures:
|
||||
print("Start taking pictures")
|
||||
else:
|
||||
print("Stop taking pictures")
|
||||
|
||||
# 根据拍照状态决定是否保存图片
|
||||
if taking_pictures:
|
||||
img = img.resize(224, 224)
|
||||
image_save = "/sd/img/example{}.jpg".format(count)
|
||||
img.save(image_save)
|
||||
print("Image saved:", image_save)
|
||||
count += 1
|
||||
a = lcd.display(img)
|
||||
|
||||
else:
|
||||
a = lcd.display(img)
|
||||
|
||||
# 打印FPS
|
||||
print(clock.fps())
|
||||
|
||||
# 稍作延迟，避免过快的循环
|
||||
time.sleep_ms(20)
|
||||
|
||||
sensor.snapshot().save("/sd/imgs/{}.jpg".format(count))
|
||||
count+=1
|
||||
print(count)
|
||||
time.sleep_ms(100)
|
||||
|
||||
@ -6,28 +6,57 @@ import cv2
|
||||
import numpy as np
|
||||
|
||||
# 读取图像
|
||||
image_path = 'xdl/1.png' # 请替换为您图像的实际路径
|
||||
image_path =r"E:\imgs\4.jpg"
|
||||
image_bgr = cv2.imread(image_path)
|
||||
|
||||
# 全局变量
|
||||
roi = []
|
||||
drawing = False
|
||||
|
||||
def draw_rectangle(event, x, y, flags, param):
|
||||
global roi, drawing, image_bgr_copy
|
||||
|
||||
if event == cv2.EVENT_LBUTTONDOWN:
|
||||
roi = [(x, y)]
|
||||
drawing = True
|
||||
|
||||
elif event == cv2.EVENT_MOUSEMOVE:
|
||||
if drawing:
|
||||
image_bgr_copy = image_bgr.copy()
|
||||
cv2.rectangle(image_bgr_copy, roi[0], (x, y), (0, 0, 255), 2)
|
||||
cv2.imshow('Select ROI', image_bgr_copy)
|
||||
|
||||
elif event == cv2.EVENT_LBUTTONUP:
|
||||
roi.append((x, y))
|
||||
drawing = False
|
||||
cv2.rectangle(image_bgr, roi[0], roi[1], (0, 0, 255), 2)
|
||||
cv2.imshow('Select ROI', image_bgr)
|
||||
|
||||
# 检查图像是否成功加载
|
||||
if image_bgr is not None:
|
||||
# 将图像从 BGR 颜色空间转换到 LAB 颜色空间
|
||||
image_bgr_copy = image_bgr.copy()
|
||||
cv2.imshow('Select ROI', image_bgr_copy)
|
||||
cv2.setMouseCallback('Select ROI', draw_rectangle)
|
||||
|
||||
while True:
|
||||
key = cv2.waitKey(1) & 0xFF
|
||||
if key == ord('q'):
|
||||
break
|
||||
|
||||
cv2.destroyAllWindows()
|
||||
|
||||
if len(roi) == 2:
|
||||
x1, y1 = roi[0]
|
||||
x2, y2 = roi[1]
|
||||
x, y, w, h = min(x1, x2), min(y1, y2), abs(x2 - x1), abs(y2 - y1)
|
||||
|
||||
# 转换到 LAB 颜色空间
|
||||
image_lab = cv2.cvtColor(image_bgr, cv2.COLOR_BGR2LAB)
|
||||
|
||||
# 定义红框区域的坐标 (x, y, width, height)
|
||||
# 请用实际的红框坐标替换
|
||||
x, y, w, h = 280, 340, 60, 60 # 替换为红框的实际坐标
|
||||
|
||||
# 裁剪 LAB 图像以获取红框内的区域
|
||||
cropped_lab = image_lab[y:y+h, x:x+w]
|
||||
|
||||
# 计算裁剪区域的平均 LAB 值
|
||||
avg_lab = np.mean(cropped_lab.reshape(-1, 3), axis=0)
|
||||
|
||||
# 定义阈值范围
|
||||
threshold_range = 10
|
||||
lower_red_threshold = np.clip(avg_lab - threshold_range, 0, 255)
|
||||
upper_red_threshold = np.clip(avg_lab + threshold_range, 0, 255)
|
||||
# 计算裁剪区域的 LAB 颜色阈值下界和上界
|
||||
lower_red_threshold = np.min(cropped_lab.reshape(-1, 3), axis=0)
|
||||
upper_red_threshold = np.max(cropped_lab.reshape(-1, 3), axis=0)
|
||||
|
||||
# 输出红色 LAB 颜色阈值
|
||||
lower = lower_red_threshold.astype(int)
|
||||
@ -36,15 +65,9 @@ if image_bgr is not None:
|
||||
print('红色的 LAB 颜色阈值上界:', upper)
|
||||
print("{}, {}, {}, {}, {}, {}".format(lower[0], upper[0], lower[1], upper[1], lower[2], upper[2]))
|
||||
|
||||
# 在原图像上绘制红框
|
||||
cv2.rectangle(image_bgr, (x, y), (x + w, y + h), (0, 0, 255), 2)
|
||||
|
||||
# 显示图像
|
||||
cv2.imshow('Image with Red Rectangle', image_bgr)
|
||||
cv2.waitKey(0)
|
||||
cv2.destroyAllWindows()
|
||||
|
||||
# 保存处理后的图像
|
||||
cv2.imwrite("image_with_red_rectangle.jpg", image_bgr)
|
||||
else:
|
||||
print("未正确标注区域。")
|
||||
else:
|
||||
print("图像无法加载。")
|
||||
|
||||
@ -28,9 +28,9 @@ clock=time.clock()
|
||||
# thresholds = [(59, 100, 40, 127, 5, 127), # 红色阈值
|
||||
# (87, 100, -59, 127, -10, 127), # 绿色阈值
|
||||
# (0, 30, 0, 64, -128, -20)] # 蓝色阈值
|
||||
thresholds = [(35, 100, 6, 127, 0, 127), # 红色阈值
|
||||
(25, 100, -6, 127, 5, 43), # 黄色阈值
|
||||
(87, 100, -59, 127, -10, 127)] # 绿色阈值
|
||||
thresholds = [(123, 143, 161, 181, 136, 156), # 红色阈值
|
||||
(207, 227, 126, 146, 155, 175), # 黄色阈值
|
||||
(196, 216, 103, 123, 135, 155)] # 绿色阈值
|
||||
|
||||
while True:
|
||||
|
||||
|
||||
127
img2bin.py
127
img2bin.py
@ -1,102 +1,91 @@
|
||||
# -*- coding:utf-8 -*-
|
||||
# @Author len
|
||||
# @Create 2023/10/26 9:18
|
||||
|
||||
from PIL import Image
|
||||
import numpy as np
|
||||
import os
|
||||
|
||||
def convert_jpg_to_bin(input_file, output_file, max_width, max_height):
|
||||
# 打开JPEG图像
|
||||
image = Image.open(input_file)
|
||||
def convert_jpg_to_bin(input_file, output_file):
|
||||
print(f"Processing: {input_file}")
|
||||
# 打开已经调整尺寸并转换为 JPEG 的图像
|
||||
image = Image.open(input_file).convert("RGB")
|
||||
|
||||
# 限制图像尺寸
|
||||
image.thumbnail((max_width, max_height))
|
||||
# 转换为 NumPy 数组
|
||||
img_array = np.array(image, dtype=np.uint16)
|
||||
r = img_array[:, :, 0]
|
||||
g = img_array[:, :, 1]
|
||||
b = img_array[:, :, 2]
|
||||
|
||||
# 将图像转换为RGB模式
|
||||
image = image.convert("RGB")
|
||||
# 计算 RGB565 格式的值
|
||||
rgb565 = ((r & 0xF8) << 8) | ((g & 0xFC) << 3) | (b >> 3)
|
||||
|
||||
# 创建一个空白的16位彩色图像
|
||||
result_image = np.zeros((image.size[1], image.size[0]), dtype=np.uint16)
|
||||
# 展平数组并保存为 BIN 文件
|
||||
rgb565.flatten().tofile(output_file)
|
||||
|
||||
# 遍历图像的每个像素
|
||||
for y in range(image.size[1]):
|
||||
for x in range(image.size[0]):
|
||||
# 获取RGB值
|
||||
r, g, b = image.getpixel((x, y))
|
||||
|
||||
# 将RGB值转换为RGB565格式
|
||||
pixel_value = ((r & 0b11111000) << 8) | ((g & 0b11111100) << 3) | (b >> 3)
|
||||
|
||||
# 将RGB565值存储到16位彩色图像中
|
||||
result_image[y, x] = pixel_value
|
||||
|
||||
# 将三维数组展平为一维数组
|
||||
flattened_array = result_image.flatten()
|
||||
|
||||
# 保存为BIN文件
|
||||
with open(output_file, "wb") as file:
|
||||
file.write(flattened_array.tobytes())
|
||||
|
||||
def ensure_jpg_format(input_path, temp_dir):
|
||||
# 确保文件是JPEG格式，如果不是则转换为JPEG
|
||||
def prepare_image(input_path, temp_dir, target_width, target_height):
|
||||
"""
|
||||
先调整图片尺寸，保持原始比例，然后将缩放后的图片粘贴到固定尺寸的画布上，
|
||||
最终保存为 JPEG 格式，确保输出尺寸为 target_width x target_height。
|
||||
"""
|
||||
with Image.open(input_path) as img:
|
||||
if img.format != "JPEG":
|
||||
img = img.convert("RGB")
|
||||
# 按比例缩放，使图片适应目标尺寸，但不会超出目标尺寸
|
||||
img.thumbnail((target_width, target_height))
|
||||
|
||||
# 创建一个新的目标尺寸图像，背景填充黑色（可以根据需要修改背景色）
|
||||
new_img = Image.new("RGB", (target_width, target_height), (0, 0, 0))
|
||||
# 计算居中粘贴的位置
|
||||
left = (target_width - img.width) // 2
|
||||
top = (target_height - img.height) // 2
|
||||
new_img.paste(img, (left, top))
|
||||
|
||||
base_name = os.path.splitext(os.path.basename(input_path))[0]
|
||||
jpg_path = os.path.join(temp_dir, f"{base_name}.jpg")
|
||||
img = img.convert("RGB") # 转换为RGB模式
|
||||
img.save(jpg_path, "JPEG")
|
||||
print(f"Converted to JPEG: {jpg_path}")
|
||||
new_img.save(jpg_path, "JPEG")
|
||||
print(f"Converted, resized and padded to JPEG: {jpg_path}")
|
||||
return jpg_path
|
||||
return input_path
|
||||
|
||||
def get_sort_key(filename):
|
||||
# 提取文件名前两位数字用于排序
|
||||
base_name = os.path.splitext(filename)[0]
|
||||
try:
|
||||
# 尝试将前两位数字转换为整数
|
||||
return int(base_name[:2])
|
||||
except ValueError:
|
||||
# 如果无法转换为数字，则返回一个较大的默认值
|
||||
return float('inf')
|
||||
|
||||
# 使用示例
|
||||
input_file = r"C:\Users\10561\Desktop\002\任务2" # 图片地址
|
||||
temp_dir = os.path.join(input_file, "temp") # 临时目录
|
||||
output_dir = os.path.join(input_file, "bin") # 输出目录
|
||||
def main():
|
||||
input_dir = r"C:\Users\10561\Desktop\20250221\tft图片" # 图片目录
|
||||
temp_dir = os.path.join(input_dir, "temp") # 临时目录
|
||||
output_dir = os.path.join(input_dir, "bin") # 输出目录
|
||||
|
||||
os.makedirs(temp_dir, exist_ok=True) # 创建临时目录
|
||||
os.makedirs(output_dir, exist_ok=True) # 创建输出目录
|
||||
os.makedirs(temp_dir, exist_ok=True)
|
||||
os.makedirs(output_dir, exist_ok=True)
|
||||
|
||||
max_width = 800
|
||||
max_height = 480
|
||||
target_width, target_height = 800, 480
|
||||
|
||||
# 获取文件列表并忽略目录，同时按照前两位数字排序
|
||||
files = sorted(
|
||||
[file for file in os.listdir(input_file) if os.path.isfile(os.path.join(input_file, file))],
|
||||
# 获取所有文件，并按照文件名前两位数字排序
|
||||
files = sorted(
|
||||
[f for f in os.listdir(input_dir) if os.path.isfile(os.path.join(input_dir, f))],
|
||||
key=get_sort_key
|
||||
)
|
||||
)
|
||||
|
||||
for file in files:
|
||||
print(file)
|
||||
for file in files:
|
||||
if file in ["System Volume Information"] or ".bin" in file:
|
||||
continue
|
||||
|
||||
portion = os.path.splitext(file)
|
||||
input001 = os.path.join(input_file, file)
|
||||
file_base = os.path.splitext(file)[0]
|
||||
input_path = os.path.join(input_dir, file)
|
||||
|
||||
# 确保输入文件是JPEG格式
|
||||
jpg_file = ensure_jpg_format(input001, temp_dir)
|
||||
# 调整尺寸、填充背景并转换为 JPEG
|
||||
jpg_file = prepare_image(input_path, temp_dir, target_width, target_height)
|
||||
|
||||
# 生成输出文件路径
|
||||
out001 = os.path.join(output_dir, f"{portion[0]}.bin")
|
||||
print(f"Output BIN: {out001}")
|
||||
# 生成输出 BIN 文件路径
|
||||
output_file = os.path.join(output_dir, f"{file_base}.bin")
|
||||
print(f"Output BIN: {output_file}")
|
||||
|
||||
# 转换为BIN文件
|
||||
convert_jpg_to_bin(jpg_file, out001, max_width, max_height)
|
||||
# 转换为 BIN 文件
|
||||
convert_jpg_to_bin(jpg_file, output_file)
|
||||
|
||||
# 清理临时目录
|
||||
for temp_file in os.listdir(temp_dir):
|
||||
os.remove(os.path.join(temp_dir, temp_file))
|
||||
os.rmdir(temp_dir)
|
||||
# 如有需要，可清理临时目录
|
||||
# for temp_file in os.listdir(temp_dir):
|
||||
# os.remove(os.path.join(temp_dir, temp_file))
|
||||
# os.rmdir(temp_dir)
|
||||
|
||||
if __name__ == '__main__':
|
||||
main()
|
||||
|
||||
154
txt2xml.py
Normal file
154
txt2xml.py
Normal file
@ -0,0 +1,154 @@
|
||||
import os
|
||||
import xml.etree.ElementTree as ET
|
||||
from xml.dom import minidom
|
||||
from PIL import Image
|
||||
|
||||
def convert_txt_to_xml(txt_path, image_path, xml_path):
|
||||
# 打开图像，获取尺寸及通道数
|
||||
with Image.open(image_path) as img:
|
||||
img_width, img_height = img.size
|
||||
mode = img.mode
|
||||
if mode == "RGB":
|
||||
depth = 3
|
||||
elif mode == "L":
|
||||
depth = 1
|
||||
else:
|
||||
depth = 3 # 默认3
|
||||
|
||||
# 读取TXT文件中的标注信息
|
||||
with open(txt_path, "r") as f:
|
||||
lines = f.readlines()
|
||||
|
||||
# 类别编号到标签名称的映射
|
||||
id_to_label = {
|
||||
0: "light",
|
||||
1: "red",
|
||||
2: "yellow",
|
||||
3: "green"
|
||||
}
|
||||
|
||||
# 构建XML结构
|
||||
annotation = ET.Element("annotation", verified="yes")
|
||||
|
||||
folder_elem = ET.SubElement(annotation, "folder")
|
||||
folder_elem.text = os.path.basename(os.path.dirname(image_path))
|
||||
|
||||
filename_elem = ET.SubElement(annotation, "filename")
|
||||
image_filename = os.path.basename(image_path)
|
||||
filename_elem.text = image_filename
|
||||
|
||||
path_elem = ET.SubElement(annotation, "path")
|
||||
path_elem.text = os.path.abspath(image_path)
|
||||
|
||||
source_elem = ET.SubElement(annotation, "source")
|
||||
database_elem = ET.SubElement(source_elem, "database")
|
||||
database_elem.text = "Unknown"
|
||||
|
||||
size_elem = ET.SubElement(annotation, "size")
|
||||
width_elem = ET.SubElement(size_elem, "width")
|
||||
width_elem.text = str(img_width)
|
||||
height_elem = ET.SubElement(size_elem, "height")
|
||||
height_elem.text = str(img_height)
|
||||
depth_elem = ET.SubElement(size_elem, "depth")
|
||||
depth_elem.text = str(depth)
|
||||
|
||||
segmented_elem = ET.SubElement(annotation, "segmented")
|
||||
segmented_elem.text = "0"
|
||||
|
||||
# 逐行处理TXT标注
|
||||
for line in lines:
|
||||
parts = line.strip().split()
|
||||
if len(parts) != 5:
|
||||
print(f"跳过格式不正确的行: {line.strip()}")
|
||||
continue
|
||||
class_id_str, x_center_norm_str, y_center_norm_str, width_norm_str, height_norm_str = parts
|
||||
try:
|
||||
class_id = int(class_id_str)
|
||||
x_center_norm = float(x_center_norm_str)
|
||||
y_center_norm = float(y_center_norm_str)
|
||||
width_norm = float(width_norm_str)
|
||||
height_norm = float(height_norm_str)
|
||||
except Exception as e:
|
||||
print(f"转换数据出错 {line.strip()}: {e}")
|
||||
continue
|
||||
|
||||
if class_id not in id_to_label:
|
||||
print(f"未知标签编号 {class_id}，跳过该行")
|
||||
continue
|
||||
|
||||
label = id_to_label[class_id]
|
||||
|
||||
# 计算绝对坐标
|
||||
x_center = x_center_norm * img_width
|
||||
y_center = y_center_norm * img_height
|
||||
bbox_width = width_norm * img_width
|
||||
bbox_height = height_norm * img_height
|
||||
|
||||
xmin = int(x_center - bbox_width / 2)
|
||||
ymin = int(y_center - bbox_height / 2)
|
||||
xmax = int(x_center + bbox_width / 2)
|
||||
ymax = int(y_center + bbox_height / 2)
|
||||
|
||||
# 构建object节点
|
||||
object_elem = ET.SubElement(annotation, "object")
|
||||
name_elem = ET.SubElement(object_elem, "name")
|
||||
name_elem.text = label
|
||||
pose_elem = ET.SubElement(object_elem, "pose")
|
||||
pose_elem.text = "Unspecified"
|
||||
truncated_elem = ET.SubElement(object_elem, "truncated")
|
||||
truncated_elem.text = "0"
|
||||
difficult_elem = ET.SubElement(object_elem, "difficult")
|
||||
difficult_elem.text = "0"
|
||||
|
||||
bndbox_elem = ET.SubElement(object_elem, "bndbox")
|
||||
xmin_elem = ET.SubElement(bndbox_elem, "xmin")
|
||||
xmin_elem.text = str(xmin)
|
||||
ymin_elem = ET.SubElement(bndbox_elem, "ymin")
|
||||
ymin_elem.text = str(ymin)
|
||||
xmax_elem = ET.SubElement(bndbox_elem, "xmax")
|
||||
xmax_elem.text = str(xmax)
|
||||
ymax_elem = ET.SubElement(bndbox_elem, "ymax")
|
||||
ymax_elem.text = str(ymax)
|
||||
|
||||
# 美化输出的XML字符串
|
||||
xml_str = ET.tostring(annotation, encoding="utf-8")
|
||||
parsed_str = minidom.parseString(xml_str)
|
||||
pretty_xml_str = parsed_str.toprettyxml(indent=" ")
|
||||
|
||||
with open(xml_path, "w", encoding="utf-8") as f:
|
||||
f.write(pretty_xml_str)
|
||||
|
||||
def batch_convert_txt_to_xml(txt_dir, image_dir, output_dir, image_ext=".jpg"):
|
||||
"""
|
||||
批量将TXT转换为XML：
|
||||
txt_dir: TXT文件所在目录
|
||||
image_dir: 对应图像所在目录（图片文件名与TXT同名，后缀可指定）
|
||||
output_dir: XML文件保存目录
|
||||
image_ext: 图像文件扩展名（默认 .jpg）
|
||||
"""
|
||||
if not os.path.exists(output_dir):
|
||||
os.makedirs(output_dir)
|
||||
|
||||
for file in os.listdir(txt_dir):
|
||||
if file.lower().endswith(".txt"):
|
||||
txt_path = os.path.join(txt_dir, file)
|
||||
base_name = os.path.splitext(file)[0]
|
||||
image_filename = base_name + image_ext
|
||||
image_path = os.path.join(image_dir, image_filename)
|
||||
if not os.path.exists(image_path):
|
||||
print(f"图像文件不存在: {image_path}，跳过 {txt_path}")
|
||||
continue
|
||||
xml_filename = base_name + ".xml"
|
||||
xml_path = os.path.join(output_dir, xml_filename)
|
||||
try:
|
||||
convert_txt_to_xml(txt_path, image_path, xml_path)
|
||||
print(f"成功转换: {txt_path} --> {xml_path}")
|
||||
except Exception as e:
|
||||
print(f"处理 {txt_path} 时出错: {e}")
|
||||
|
||||
if __name__ == "__main__":
|
||||
# 请修改以下目录为实际路径
|
||||
txt_directory = "path_to_txt_directory" # TXT文件所在目录
|
||||
image_directory = "path_to_image_directory" # 图像文件所在目录
|
||||
output_directory = "path_to_output_xml_directory" # 输出XML文件保存目录
|
||||
batch_convert_txt_to_xml(txt_directory, image_directory, output_directory, image_ext=".jpg")
|
||||
82
xml2txt.py
Normal file
82
xml2txt.py
Normal file
@ -0,0 +1,82 @@
|
||||
import os
|
||||
import xml.etree.ElementTree as ET
|
||||
|
||||
def convert_xml_to_txt(xml_path, txt_path):
|
||||
# 解析XML文件
|
||||
tree = ET.parse(xml_path)
|
||||
root = tree.getroot()
|
||||
|
||||
# 获取图像尺寸信息
|
||||
size = root.find("size")
|
||||
img_width = int(size.find("width").text)
|
||||
img_height = int(size.find("height").text)
|
||||
|
||||
# 标签映射字典
|
||||
label_mapping = {
|
||||
"light": 0,
|
||||
"red": 1,
|
||||
"yellow": 2,
|
||||
"green": 3
|
||||
}
|
||||
|
||||
lines = []
|
||||
# 遍历每个object节点
|
||||
for obj in root.findall("object"):
|
||||
# 获取并转换标签名称，统一转为小写
|
||||
class_name = obj.find("name").text.strip().lower()
|
||||
if class_name in label_mapping:
|
||||
class_id = label_mapping[class_name]
|
||||
else:
|
||||
print(f"未知标签 {class_name}，文件 {xml_path} 中跳过该对象")
|
||||
continue
|
||||
|
||||
# 获取边界框坐标
|
||||
bndbox = obj.find("bndbox")
|
||||
xmin = float(bndbox.find("xmin").text)
|
||||
ymin = float(bndbox.find("ymin").text)
|
||||
xmax = float(bndbox.find("xmax").text)
|
||||
ymax = float(bndbox.find("ymax").text)
|
||||
|
||||
# 计算边界框中心点和宽高
|
||||
x_center = (xmin + xmax) / 2.0
|
||||
y_center = (ymin + ymax) / 2.0
|
||||
bbox_width = xmax - xmin
|
||||
bbox_height = ymax - ymin
|
||||
|
||||
# 归一化
|
||||
x_center_norm = x_center / img_width
|
||||
y_center_norm = y_center / img_height
|
||||
width_norm = bbox_width / img_width
|
||||
height_norm = bbox_height / img_height
|
||||
|
||||
# 每行格式：类别编号 x_center y_center width height
|
||||
line = f"{class_id} {x_center_norm:.6f} {y_center_norm:.6f} {width_norm:.6f} {height_norm:.6f}"
|
||||
lines.append(line)
|
||||
|
||||
# 将转换结果写入TXT文件
|
||||
with open(txt_path, "w") as f:
|
||||
for line in lines:
|
||||
f.write(line + "\n")
|
||||
|
||||
def batch_convert_xml_to_txt(input_dir, output_dir):
|
||||
# 如果输出目录不存在则创建
|
||||
if not os.path.exists(output_dir):
|
||||
os.makedirs(output_dir)
|
||||
|
||||
# 遍历输入目录下所有XML文件
|
||||
for file in os.listdir(input_dir):
|
||||
if file.lower().endswith(".xml"):
|
||||
xml_path = os.path.join(input_dir, file)
|
||||
txt_file = os.path.splitext(file)[0] + ".txt"
|
||||
txt_path = os.path.join(output_dir, txt_file)
|
||||
try:
|
||||
convert_xml_to_txt(xml_path, txt_path)
|
||||
print(f"成功转换: {xml_path} --> {txt_path}")
|
||||
except Exception as e:
|
||||
print(f"处理 {xml_path} 时出错: {e}")
|
||||
|
||||
if __name__ == "__main__":
|
||||
# 请替换下面的目录为实际路径
|
||||
input_directory = r"C:\Users\10561\Downloads\Compressed\xml" # XML文件所在的目录
|
||||
output_directory = r"C:\Users\10561\Downloads\Compressed\txt" # 转换后txt文件保存的目录
|
||||
batch_convert_xml_to_txt(input_directory, output_directory)
|
||||
51
xml删除标签.py
Normal file
51
xml删除标签.py
Normal file
@ -0,0 +1,51 @@
|
||||
import os
|
||||
import xml.etree.ElementTree as ET
|
||||
|
||||
def remove_tag_from_element(element, tag):
|
||||
"""
|
||||
递归遍历element的所有子节点，删除所有匹配指定标签的子节点。
|
||||
"""
|
||||
for child in list(element): # 用 list() 避免在遍历时修改子节点列表导致问题
|
||||
if child.tag == tag:
|
||||
element.remove(child)
|
||||
else:
|
||||
remove_tag_from_element(child, tag)
|
||||
|
||||
def remove_tag_from_xml(xml_file, tag_to_remove, output_file):
|
||||
"""
|
||||
解析单个 XML 文件，删除其中所有指定的标签，然后保存为新的 XML 文件。
|
||||
"""
|
||||
try:
|
||||
tree = ET.parse(xml_file)
|
||||
root = tree.getroot()
|
||||
# 如果根节点本身就是要删除的标签，则提示并跳过处理
|
||||
if root.tag == tag_to_remove:
|
||||
print(f"警告：{xml_file} 的根标签就是要删除的标签({tag_to_remove})，跳过该文件。")
|
||||
return
|
||||
|
||||
remove_tag_from_element(root, tag_to_remove)
|
||||
tree.write(output_file, encoding="utf-8", xml_declaration=True)
|
||||
print(f"处理完成: {xml_file} -> {output_file}")
|
||||
except Exception as e:
|
||||
print(f"处理 {xml_file} 时出错: {e}")
|
||||
|
||||
def batch_remove_tag(input_dir, output_dir, tag_to_remove):
|
||||
"""
|
||||
批量处理：遍历 input_dir 下所有 XML 文件，删除指定标签后，将新文件保存到 output_dir。
|
||||
"""
|
||||
if not os.path.exists(output_dir):
|
||||
os.makedirs(output_dir)
|
||||
|
||||
for file in os.listdir(input_dir):
|
||||
if file.lower().endswith(".xml"):
|
||||
input_path = os.path.join(input_dir, file)
|
||||
output_path = os.path.join(output_dir, file)
|
||||
remove_tag_from_xml(input_path, tag_to_remove, output_path)
|
||||
|
||||
if __name__ == "__main__":
|
||||
# 请修改以下路径和要删除的标签名称
|
||||
input_directory = r"C:\Users\10561\Downloads\Compressed\xml" # 输入 XML 文件所在目录
|
||||
output_directory = "path_to_output_xml_directory" # 处理后 XML 文件保存目录
|
||||
tag_to_delete = "segmented" # 例如：删除 <segmented> 标签，可改为你需要删除的标签名
|
||||
|
||||
batch_remove_tag(input_directory, output_directory, tag_to_delete)
|
||||
20
图片分批.py
Normal file
20
图片分批.py
Normal file
@ -0,0 +1,20 @@
|
||||
import os
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
dir_path=r"C:\Users\10561\Desktop\frames-3000"
|
||||
output_dir=r"C:\Users\10561\Desktop\frames-3000-批次"
|
||||
if not os.path.exists(output_dir):
|
||||
os.mkdir(output_dir)
|
||||
images=os.listdir(dir_path)
|
||||
images=[os.path.join(dir_path,i) for i in images]
|
||||
for i in range(len(images)//100):
|
||||
out=os.path.join(output_dir,f"{i}")
|
||||
os.mkdir(out)
|
||||
if i == len(images)//100:
|
||||
#复制
|
||||
for j in range(i*100,len(images)):
|
||||
os.system(f"copy {images[j]} {out}")
|
||||
else:
|
||||
for j in range(i*100,(i+1)*100):
|
||||
os.system(f"copy {images[j]} {out}")
|
||||
21
图片尺寸检查.py
Normal file
21
图片尺寸检查.py
Normal file
@ -0,0 +1,21 @@
|
||||
import os
|
||||
from PIL import Image
|
||||
|
||||
# 指定要检查的目录路径
|
||||
directory = r"C:\Users\10561\Downloads\Compressed\images" # 替换为你的目录路径
|
||||
|
||||
# 目标尺寸
|
||||
target_size = (224, 224)
|
||||
|
||||
# 遍历目录中的所有文件
|
||||
for filename in os.listdir(directory):
|
||||
# 只处理图片文件（根据文件后缀判断）
|
||||
if filename.lower().endswith(('.png', '.jpg', '.jpeg', '.bmp', '.gif')):
|
||||
file_path = os.path.join(directory, filename)
|
||||
try:
|
||||
with Image.open(file_path) as img:
|
||||
# 检查图片尺寸是否与目标尺寸不一致
|
||||
if img.size != target_size:
|
||||
print(f"{filename}: {img.size}")
|
||||
except Exception as e:
|
||||
print(f"处理 {filename} 时出错: {e}")
|
||||
4
抽帧.py
4
抽帧.py
@ -38,8 +38,8 @@ def extract_frames(video_path, output_dir, interval=0.5,output_name='frame'):
|
||||
cap.release()
|
||||
print(f"共保存了 {saved_count} 帧.")
|
||||
|
||||
video_dir=r'C:\Users\10561\Desktop\bcar红绿灯\1'
|
||||
output_directory = r"C:\Users\10561\Desktop\frames" # 替换为你希望保存的目录路径
|
||||
video_dir=r'C:\Users\10561\Desktop\bcar红绿灯\3'
|
||||
output_directory = r"C:\Users\10561\Desktop\frames123" # 替换为你希望保存的目录路径
|
||||
|
||||
for file in os.listdir(video_dir):
|
||||
if file.endswith('.mp4'):
|
||||
|
||||
Loading…
Reference in New Issue
Block a user