总是第一个for循环报错，我运行的时候总会提示快速帧缓冲堆内存不足，降低图像分辨率调整算法来绕过这个问题？
# Edge Impulse - OpenMV Image Classification Example
import sensor, image, time, os, tf
sensor.reset() # Reset and initialize the sensor.
sensor.set_pixformat(sensor.RGB565) # Set pixel format to RGB565 (or GRAYSCALE)
sensor.set_framesize(sensor.QVGA) # Set frame size to QVGA (320x240)
sensor.set_windowing((240, 240)) # Set 240x240 window.
sensor.skip_frames(time=2000) # Let the camera adjust.
net = "trained.tflite"
labels = [line.rstrip('\n') for line in open("labels.txt")]
clock = time.clock()
while(True):
clock.tick()
img = sensor.snapshot()
# default settings just do one detection... change them to search the image...
for obj in tf.classify(net, img, min_scale=1.0, scale_mul=0.8, x_overlap=0.5, y_overlap=0.5):
print("**********\nPredictions at [x=%d,y=%d,w=%d,h=%d]" % obj.rect())
img.draw_rectangle(obj.rect())
# This combines the labels and confidence values into a list of tuples
predictions_list = list(zip(labels, obj.output()))
for i in range(len(predictions_list)):
print("%s = %f" % (predictions_list[i][0], predictions_list[i][1]))
print(clock.fps(), "fps")
 
MemoryError: Out of fast Frame Buffer Stack Memory!
# Edge Impulse - OpenMV Image Classification Example
import sensor, image, time, os, tf
sensor.reset() # Reset and initialize the sensor.
sensor.set_pixformat(sensor.RGB565) # Set pixel format to RGB565 (or GRAYSCALE)
sensor.set_framesize(sensor.QVGA) # Set frame size to QVGA (320x240)
sensor.set_windowing((240, 240)) # Set 240x240 window.
sensor.skip_frames(time=2000) # Let the camera adjust.
net = "trained.tflite"
labels = [line.rstrip('\n') for line in open("labels.txt")]
clock = time.clock()
while(True):
clock.tick()
img = sensor.snapshot()
# default settings just do one detection... change them to search the image...
for obj in tf.classify(net, img, min_scale=1.0, scale_mul=0.8, x_overlap=0.5, y_overlap=0.5):
print("**********\nPredictions at [x=%d,y=%d,w=%d,h=%d]" % obj.rect())
img.draw_rectangle(obj.rect())
# This combines the labels and confidence values into a list of tuples
predictions_list = list(zip(labels, obj.output()))
for i in range(len(predictions_list)):
print("%s = %f" % (predictions_list[i][0], predictions_list[i][1]))
print(clock.fps(), "fps")
识别数字时出现OSError
代码：# Edge Impulse - OpenMV Image Classification Example
import sensor, image, time, os, tf
sensor.reset() # Reset and initialize the sensor.
sensor.set_pixformat(sensor.RGB565) # Set pixel format to RGB565 (or GRAYSCALE)
sensor.set_framesize(sensor.QVGA) # Set frame size to QVGA (320x240)
sensor.set_windowing((240, 240)) # Set 240x240 window.
sensor.skip_frames(time=2000) # Let the camera adjust.
net = "trained.tflite"
labels = [line.rstrip('\n') for line in open("labels.txt")]
clock = time.clock()
while(True):
clock.tick()
img = sensor.snapshot()
# default settings just do one detection... change them to search the image...
for obj in tf.classify(net, img, min_scale=1.0, scale_mul=0.8, x_overlap=0.5, y_overlap=0.5):
print("**********\nPredictions at [x=%d,y=%d,w=%d,h=%d]" % obj.rect())
img.draw_rectangle(obj.rect())
# This combines the labels and confidence values into a list of tuples
predictions_list = list(zip(labels, obj.output()))
for i in range(len(predictions_list)):
print("%s = %f" % (predictions_list[i][0], predictions_list[i][1]))
print(clock.fps(), "fps")MemoryError: Out of fast Frame Buffer Stack Memory!
# Edge Impulse - OpenMV Image Classification Example
import sensor, image, time, os, tf
import gc
gc.threshold(100000)
sensor.reset() # Reset and initialize the sensor.
sensor.set_pixformat(sensor.RGB565) # Set pixel format to RGB565 (or GRAYSCALE)
sensor.set_framesize(sensor.QQVGA) # Set frame size to QVGA (320x240)
sensor.set_windowing((240, 240)) # Set 240x240 window.
sensor.skip_frames(time=2000) # Let the camera adjust.
net = "trained.tflite"#模型文件
labels = [line.rstrip('\n') for line in open("labels.txt")]
#1 -
clock = time.clock()
while(True):
clock.tick()
gc.collect()
img = sensor.snapshot()
# default settings just do one detection... change them to search the image...
for obj in tf.classify(net, img, min_scale=1.0, scale_mul=0.8, x_overlap=0.5, y_overlap=0.5):
print("**********\nPredictions at [x=%d,y=%d,w=%d,h=%d]" % obj.rect())
img.draw_rectangle(obj.rect())
# This combines the labels and confidence values into a list of tuples
predictions_list = list(zip(labels, obj.output()))
for i in range(len(predictions_list)):
print("%s = %f" % (predictions_list[i][0], predictions_list[i][1]))
print(clock.fps(), "fps")
为什么会出现错误OSError：0，是我代码错了还是买到盗版openmv了，我都是按着视频操作来的
Edge Impulse - OpenMV Image Classification Example
import sensor, image, time, os, tf
sensor.reset() # Reset and initialize the sensor.
sensor.set_pixformat(sensor.RGB565) # Set pixel format to RGB565 (or GRAYSCALE)
sensor.set_framesize(sensor.QVGA) # Set frame size to QVGA (320x240)
sensor.set_windowing((240, 240)) # Set 240x240 window.
sensor.skip_frames(time=2000) # Let the camera adjust.
net = "trained.tflite"
labels = [line.rstrip('\n') for line in open("labels.txt")]
clock = time.clock()
while(True):
clock.tick()
img = sensor.snapshot()
# default settings just do one detection... change them to search the image...
for obj in tf.classify(net, img, min_scale=1.0, scale_mul=0.8, x_overlap=0.5, y_overlap=0.5):
print("**********\nPredictions at [x=%d,y=%d,w=%d,h=%d]" % obj.rect())
img.draw_rectangle(obj.rect())
# This combines the labels and confidence values into a list of tuples
predictions_list = list(zip(labels, obj.output()))
for i in range(len(predictions_list)):
print("%s = %f" % (predictions_list[i][0], predictions_list[i][1]))
print(clock.fps(), "fps")
部署自己训练的神经网络模型后出现问题，带SD卡会自动断开连接，不带SD卡运行模型自带程序后也会断开连接。为什么？
# Edge Impulse - OpenMV Image Classification Example
import sensor, image, time, os, tf
sensor.reset() # Reset and initialize the sensor.
sensor.set_pixformat(sensor.RGB565) # Set pixel format to RGB565 (or GRAYSCALE)
sensor.set_framesize(sensor.QVGA) # Set frame size to QVGA (320x240)
sensor.set_windowing((240, 240)) # Set 240x240 window.
sensor.skip_frames(time=2000) # Let the camera adjust.
net = "something.tflite"
labels = [line.rstrip('\n') for line in open("labels.txt")]
clock = time.clock()
while(True):
clock.tick()
img = sensor.snapshot()
# default settings just do one detection... change them to search the image...
for obj in tf.classify(net, img, min_scale=1.0, scale_mul=0.8, x_overlap=0.5, y_overlap=0.5):
print("**********\nPredictions at [x=%d,y=%d,w=%d,h=%d]" % obj.rect())
img.draw_rectangle(obj.rect())
# This combines the labels and confidence values into a list of tuples
predictions_list = list(zip(labels, obj.output()))
for i in range(len(predictions_list)):
print("%s = %f" % (predictions_list[i][0], predictions_list[i][1]))
print(clock.fps(), "fps")
神经网络训练模型问题
Edge Impulse - OpenMV Image Classification Example
import sensor, image, time, os, tf, uos, gc
sensor.reset() # Reset and initialize the sensor.
sensor.set_pixformat(sensor.RGB565) # Set pixel format to RGB565 (or GRAYSCALE)
sensor.set_framesize(sensor.QVGA) # Set frame size to QVGA (320x240)
sensor.set_windowing((240, 240)) # Set 240x240 window.
sensor.skip_frames(time=2000) # Let the camera adjust.
net = None
labels = None
try:
# Load built in model
labels, net = tf.load_builtin_model('trained')
except Exception as e:
raise Exception(e)
clock = time.clock()
while(True):
clock.tick()
img = sensor.snapshot()
# default settings just do one detection... change them to search the image...
for obj in net.classify(img, min_scale=1.0, scale_mul=0.8, x_overlap=0.5, y_overlap=0.5):
print("**********\nPredictions at [x=%d,y=%d,w=%d,h=%d]" % obj.rect())
img.draw_rectangle(obj.rect())
# This combines the labels and confidence values into a list of tuples
predictions_list = list(zip(labels, obj.output()))
for i in range(len(predictions_list)):
print("%s = %f" % (predictions_list[i][0], predictions_list[i][1]))
print(clock.fps(), "fps")
这是模型里跑出来的代码
报错AttributeError:'str'object has no attribute'classify'如何解决？
@kidswong999 报错：AttributeError: 'str' object has no attribute'classify'
代码如下：
# Edge Impulse - OpenMV Image Classification Example
import sensor, image, time, os, tf, uos, gc
sensor.reset() # Reset and initialize the sensor.
sensor.set_pixformat(sensor.RGB565) # Set pixel format to RGB565 (or GRAYSCALE)
sensor.set_framesize(sensor.QVGA) # Set frame size to QVGA (320x240)
sensor.set_windowing((240, 240)) # Set 240x240 window.
sensor.skip_frames(time=2000) # Let the camera adjust.
net = "trained.tflite"
labels = [line.rstrip('\n') for line in open("labels.txt")]
clock = time.clock()
while(True):
clock.tick()
img = sensor.snapshot()
# default settings just do one detection... change them to search the image...
for obj in net.classify(img, min_scale=1.0, scale_mul=0.8, x_overlap=0.5, y_overlap=0.5):
print("**********\nPredictions at [x=%d,y=%d,w=%d,h=%d]" % obj.rect())
img.draw_rectangle(obj.rect())
# This combines the labels and confidence values into a list of tuples
predictions_list = list(zip(labels, obj.output()))
for i in range(len(predictions_list)):
print("%s = %f" % (predictions_list[i][0], predictions_list[i][1]))
print(clock.fps(), "fps")
如何只对一张图片进行二值化处理
我要做什么：
我想采集一张图片就停下来，然后对它做后续各种处理比如二值化。
我遇到的问题：
只操作一张图片进行二值化framebuffer里面没有反应。
我想得到什么帮助：
如何只对一张图片进行二值化，而不是循环采集
import sensor, image, time
sensor.reset() # Reset and initialize the sensor.
sensor.set_pixformat(sensor.RGB565) # Set pixel format to RGB565 (or GRAYSCALE)
sensor.set_framesize(sensor.QVGA) # Set frame size to QVGA (320x240)
sensor.skip_frames(time = 2000) # Wait for settings take effect.
# 获得一帧图像
img = sensor.snapshot()
threshold = (0,100, -128,127, 20,50)#二值化的分割阈值
img.binary([threshold])
在LED等下拍照频闪
你这不可能是LED，LED频率很快的，不会影响OpenMV，估计是荧光灯。
暂时的办法：
运行下面的代码：先改exposure_us，直到不频闪，注意画面亮度会改变。然后再增加gain_db把画面亮度变大。
import sensor, image, time
sensor.reset() # Reset and initialize the sensor.
sensor.set_pixformat(sensor.RGB565) # Set pixel format to RGB565 (or GRAYSCALE)
sensor.set_framesize(sensor.QVGA) # Set frame size to QVGA (320x240)
sensor.set_auto_exposure(False, \
exposure_us = 100000)
sensor.set_auto_gain(False, gain_db=1)
sensor.skip_frames(time = 2000) # Wait for settings take effect.
clock = time.clock() # Create a clock object to track the FPS.
while(True):
clock.tick() # Update the FPS clock.
img = sensor.snapshot() # Take a picture and return the image.
print(clock.fps())
如果有条件，建议换个灯，或者加一个补光灯。https://singtown.com/product/50776/openmv-light-shield/