共 499 条结果匹配 "sensor"，(耗时 0.06 秒)
openmv h7 r2 是openmv3吗
标准名：OpenMV Cam H7，代表芯片架构是H7；缩写OpenMV4，因为是第4代OpenMV Cam。
标准名：OpenMV Cam H7 Plus，代表芯片架构H7，并升级内存；缩写OpenMV4p。
作为对比，已经停产的OpenMV Cam M7，代表芯片架构是M7；缩写OpenMV3，因为是第3代OpenMV Cam。
r2代表的是小改款，OpenMV Cam H7 r1是用的ov7725的sensor，OpenMV Cam H7 r2用的是mt9m114的sensor。
求助： 'module' object has no attribute 'classify'
4.5.6之后把tf删除了，替换成了ml库。
# This code run in OpenMV4 H7 Plus
import sensor, image, time, os, ml
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.
model = ml.Model("trained.tflite", load_to_fb=True)
labels = [line.rstrip() for line in open("labels.txt")]
class_num = len(labels)
clock = time.clock()
while True:
clock.tick()
img = sensor.snapshot()
scores = model.predict([img])[0].flatten().tolist()
for i in range(class_num):
print("%s = %f" % (labels[i], scores[i]))
print(clock.fps(), "fps")
Openmv使用星瞳AI训练出识别水果的模型后，运行时检测到水果后，卡住了，然后断开连接，该如何解决？
# This code run in OpenMV4 H7 Plus
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.
labels = [line.rstrip() for line in open("labels.txt")]
class_num = len(labels)
clock = time.clock()
while(True):
clock.tick()
img = sensor.snapshot()
for obj in tf.classify("trained.tflite", img, min_scale=1.0, scale_mul=0.8, x_overlap=0.5, y_overlap=0.5):
img.draw_rectangle(obj.rect())
output = obj.output()
for i in range(class_num):
print("%s = %f" % (labels[i], output[i]))
print(clock.fps(), "fps")
调用difference函数问题
效果还是挺好的
import sensor, image, time
sensor.reset()
sensor.set_pixformat(sensor.GRAYSCALE)
sensor.set_framesize(sensor.QQVGA)
sensor.skip_frames(time = 2000) # Wait for settings take effect.
sensor.snapshot().save("/template.bmp")
while(True):
img = sensor.snapshot()
diff = img.difference("/template.bmp")
大佬们 请问为什么按照例程手动设置白平衡后 rgb的增益不变 现在在蓝布上拍图泛蓝光 请问用什么方法来显示正常的颜色
import sensor, image, time
sensor.reset()
sensor.set_pixformat(sensor.RGB565)
sensor.set_framesize(sensor.QVGA)
sensor.set_brightness(500)
sensor.set_auto_gain(False)
sensor.set_auto_whitebal(False, rgb_gain_db = (25,25,0))
sensor.set_auto_exposure(True)
sensor.skip_frames(time = 2000)
clock = time.clock()
while(True):
clock.tick()
img=sensor.snapshot()
print(clock.fps(),\
sensor.get_rgb_gain_db())
SD卡保存图片速度越来越慢，如何去解决？
import sensor, image, time,pyb
sensor.reset()
sensor.set_pixformat(sensor.RGB565)
sensor.set_framesize(sensor.QVGA)
sensor.skip_frames(time = 2000)
clock = time.clock()
jps_cnt=10000000000
while(True):
clock.tick()
if jps_cnt <=10000001000:
time_start = pyb.millis()
img = sensor.snapshot()
img.save("./pic2/%d.jpg" % (jps_cnt))
time_end = pyb.millis()
jps_cnt +=1
print("time:%d"%(time_end-time_start))
print(clock.fps())
摄像头采集画面为长方形320*240,如何保持宽度320像素不变，让其变为正方形画面呢？大佬们，求解答？？
图片缩放一下就是正方形了。
import sensor, image, time
sensor.reset()
sensor.set_pixformat(sensor.RGB565)
sensor.set_framesize(sensor.QVGA)
sensor.skip_frames(time = 2000)
clock = time.clock()
while(True):
clock.tick()
img = sensor.snapshot().scale(x_size=240, y_size=240, copy_to_fb=True)
print(img.width(),img.height())
print(clock.fps())
请问如何让find_features函数返回人脸中心区域的坐标值，而且find_features函数的rio是什么意思
import sensor, time, image, pyb
led = pyb.LED(3)
Reset sensor
sensor.reset()
Sensor settings
sensor.set_contrast(1)
sensor.set_gainceiling(16)
HQVGA and GRAYSCALE are the best for face tracking.
sensor.set_framesize(sensor.HQVGA)
sensor.set_pixformat(sensor.GRAYSCALE)
face_cascade = image.HaarCascade("frontalface", stages=25)
#stages值未传入时使用默认的stages。stages值设置的小一些可以加速匹配，但会降低准确率。
print(face_cascade)
clock = time.clock()
while (True):
clock.tick()
img = sensor.snapshot()
#注意：比例因子越低，图像越小，检测的对象越小。
#阈值越高，检测率越高，误报越多。
objects = img.find_features(face_cascade, threshold=0.75, scale=1.35, roi)
#image.find_features(cascade, threshold=0.5, scale=1.5),thresholds越大，
#匹配速度越快，错误率也会上升。scale可以缩放被匹配特征的大小。
#在找到的目标上画框，标记出来
for r in objects:
img.draw_rectangle(r)
a = r.x()
print(a)
#time.sleep(100) #延时100ms
print(clock.fps())
关于自己训练的xml文件运行的问题
下面是我的代码：
import sensor, time, image, uio
import sys,os,uos
iiii = {0};
# Reset sensor
sensor.reset()
# Sensor settings
sensor.set_contrast(3)
sensor.set_gainceiling(16)
# Set resolution to VGA.
sensor.set_framesize(sensor.VGA)
sensor.set_brightness(-3)
# Bin/Crop image to 200x100, which gives more details with less data to process
sensor.set_windowing((220, 190, 200, 100))
sensor.set_pixformat(sensor.GRAYSCALE)
# Load Haar Cascade
# By default this will use all stages, lower satges is faster but less accurate.
#eyes_cascade = image.HaarCascade("eye", stages=24)
#eyes_cascade = image.HaarCascade("frontalface.cascade", stages=24)
eyes_cascade = image.HaarCascade("haarcascade_eye.cascade", stages=24)
print(eyes_cascade)
#print("frontalface")
print("haarcascade_eye")
# FPS clock
clock = time.clock()
while (True):
clock.tick()
# Capture snapshot
img = sensor.snapshot()
time.sleep(250)
# Find eyes !
# Note: Lower scale factor scales-down the image more and detects smaller objects.
# Higher threshold results in a higher detection rate, with more false positives.
eyes = img.find_features(eyes_cascade, threshold=0.1, scale_factor=500)
# Find iris
for e in eyes:
iris = img.find_eye(e)
img.draw_rectangle(e)
img.draw_cross(iris[0], iris[1])
# Print FPS.
# Note: Actual FPS is higher, streaming the FB makes it slower.
#print(eyes_cascade)