dymb 发布的帖子

好像是的欸，不能超过六个吗

Exception: Failed to load "trained.tflite", did you copy the .tflite and labels.txt file onto the mass-storage device? (Arena size is too small for all buffers. Needed 225840 but only 219920 was available.

# Edge Impulse - OpenMV Object Detection Example

import sensor, image, time, os, tf, math, 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
min_confidence = 0.5

try:
    # load the model, alloc the model file on the heap if we have at least 64K free after loading
    net = tf.load("trained.tflite", load_to_fb=uos.stat('trained.tflite')[6] > (gc.mem_free() - (64*1024)))
except Exception as e:
    raise Exception('Failed to load "trained.tflite", did you copy the .tflite and labels.txt file onto the mass-storage device? (' + str(e) + ')')

try:
    labels = [line.rstrip('\n') for line in open("labels.txt")]
except Exception as e:
    raise Exception('Failed to load "labels.txt", did you copy the .tflite and labels.txt file onto the mass-storage device? (' + str(e) + ')')

colors = [ # Add more colors if you are detecting more than 7 types of classes at once.
    (255,   0,   0),
    (  0, 255,   0),
    (255, 255,   0),
    (  0,   0, 255),
    (255,   0, 255),
    (  0, 255, 255),
    (255, 255, 255),
]

clock = time.clock()
while(True):
    clock.tick()

    img = sensor.snapshot()

    # detect() returns all objects found in the image (splitted out per class already)
    # we skip class index 0, as that is the background, and then draw circles of the center
    # of our objects

    for i, detection_list in enumerate(net.detect(img, thresholds=[(math.ceil(min_confidence * 255), 255)])):
        if (i == 0): continue # background class
        if (len(detection_list) == 0): continue # no detections for this class?

        print("********** %s **********" % labels[i])
        for d in detection_list:
            [x, y, w, h] = d.rect()
            center_x = math.floor(x + (w / 2))
            center_y = math.floor(y + (h / 2))
            print('x %d\ty %d' % (center_x, center_y))
            img.draw_circle((center_x, center_y, 12), color=colors[i], thickness=2)

    print(clock.fps(), "fps", end="\n\n")

img.draw_circle((center_x, center_y, 12), color=colors[i], thickness=2)这句话报错

就是画面中同时出现三个物品，判断他们中有哪些是我需要的。如果不行的话，是否可以把画面分割成三个部分，三个画面分别识别。如果可以的话，能不能教我一下代码，，，求教大佬解答了，万分感谢！！！！

OSError: Could not find the file
可是就是按视频里放的啊，之前单模板识别就没错

怎么样让openmv可以倒装，但是画面又是正的

在Edge Impuse上生成的神经网络模型只能全屏识别物品。怎么样可以像模板识别一样用小一点的矩形框选出识别到的物品
这里附上生成的代码：# Edge Impulse - OpenMV Image Classification Example

import sensor, image, time, os, tf, uos, gc
flag=0
a=0
b=0
pre=0
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.
from pyb import UART
net = None
labels = None
uart = UART(3, 115200)
while(True):
#a=uart.read()
#if a==b'o' :
try:
# load the model, alloc the model file on the heap if we have at least 64K free after loading
net = tf.load("trained.tflite", load_to_fb=uos.stat('trained.tflite')[6] > (gc.mem_free() - (64*1024)))
except Exception as e:
print(e)
raise Exception('Failed to load "trained.tflite", did you copy the .tflite and labels.txt file onto the mass-storage device? (' + str(e) + ')')

    try:
        labels = [line.rstrip('\n') for line in open("labels.txt")]
    except Exception as e:
        raise Exception('Failed to load "labels.txt", did you copy the .tflite and labels.txt file onto the mass-storage device? (' + str(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()) #, roi=(10, 0, 60, 60))
            # 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]))

        if predictions_list[0][1]>0.85:
            flag=1

        elif predictions_list[1][1]>0.85:
            flag=2

        elif predictions_list[0][1]<0.85 and predictions_list[1][1]<0.85:
            flag=3
        if pre!=flag:
            b=0
            pre=flag
        if flag==1:
            if b<=2:
                uart.write("z1")
                b+=1
            
        if flag==2:
            if b<=2:
                uart.write("z2")
                b+=1
            
        if flag==3:
            if b<=2:
                uart.write("g")
                b+=1
       print(clock.fps(), "fps")

求大佬解答

@kidswong999 那那您知道怎么让他变不卡吗，我看好像说是释放内存，但是怎么释放 啊

应该怎么解决 啊大佬

import time, sensor, image
from image import SEARCH_EX, SEARCH_DS

# Reset sensor
sensor.reset()

# Set sensor settings
sensor.set_contrast(1)
sensor.set_gainceiling(16)
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)
from pyb import UART
uart = UART(3, 115200)
# Load template.
# Template should be a small (eg. 32x32 pixels) grayscale image.
template1 = image.Image("/1.pgm")
template2 = image.Image("/2.pgm")
template3 = image.Image("/3.pgm")
template4 = image.Image("/4.pgm")
template5 = image.Image("/5.pgm")
template6 = image.Image("/6.pgm")
template7 = image.Image("/7.pgm")
template8 = image.Image("/8.pgm")
template9 = image.Image("/9.pgm")


template14 = image.Image("/m.pgm")



template18 = image.Image("/1L2.pgm")
template19 = image.Image("/1L3.pgm")

template21 = image.Image("/1R2.pgm")
template22 = image.Image("/1R3.pgm")


template25 = image.Image("/2L2.pgm")
template26 = image.Image("/2L3.pgm")


template29 = image.Image("/2R2.pgm")
template30 = image.Image("/3L1.pgm")
template31 = image.Image("/3L2.pgm")

template33 = image.Image("/3R1.pgm")
template34 = image.Image("/3R2.pgm")


template37 = image.Image("/4L1.pgm")
template38 = image.Image("/4L2.pgm")

template40 = image.Image("/4R1.pgm")
template41 = image.Image("/4R1.pgm")

flag=0
flag1=0



clock = time.clock()


# Run template matching
while (True):
    clock.tick()
    img = sensor.snapshot()



    # 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.
    r1 = img.find_template(template1, 0.70, step=4, search=SEARCH_EX) #, roi=(10, 0, 60, 60))
    r2 = img.find_template(template2, 0.70, step=4, search=SEARCH_EX) #, roi=(10, 0, 60, 60))
    r3 = img.find_template(template3, 0.70, step=4, search=SEARCH_EX) #, roi=(10, 0, 60, 60))
    r4 = img.find_template(template4, 0.70, step=4, search=SEARCH_EX) #, roi=(10, 0, 60, 60))
    r5 = img.find_template(template5, 0.70, step=4, search=SEARCH_EX) #, roi=(10, 0, 60, 60))
    r6 = img.find_template(template6, 0.70, step=4, search=SEARCH_EX) #, roi=(10, 0, 60, 60))
    r7 = img.find_template(template7, 0.70, step=4, search=SEARCH_EX) #, roi=(10, 0, 60, 60))
    r8 = img.find_template(template8, 0.70, step=4, search=SEARCH_EX) #, roi=(10, 0, 60, 60))
    r9 = img.find_template(template9, 0.70, step=4, search=SEARCH_EX) #, roi=(10, 0, 60, 60))

    
    r14= img.find_template(template14, 0.70, step=4, search=SEARCH_EX) #, roi=(10, 0, 60, 60))



    r18= img.find_template(template18, 0.70, step=4, search=SEARCH_EX) #, roi=(10, 0, 60, 60))
    r19= img.find_template(template19, 0.70, step=4, search=SEARCH_EX) #, roi=(10, 0, 60, 60))

    r22= img.find_template(template22, 0.70, step=4, search=SEARCH_EX) #, roi=(10, 0, 60, 60))


    r25= img.find_template(template25, 0.70, step=4, search=SEARCH_EX) #, roi=(10, 0, 60, 60))
    r26= img.find_template(template26, 0.70, step=4, search=SEARCH_EX) #, roi=(10, 0, 60, 60))


    r29= img.find_template(template29, 0.70, step=4, search=SEARCH_EX) #, roi=(10, 0, 60, 60))
    r30= img.find_template(template30, 0.70, step=4, search=SEARCH_EX) #, roi=(10, 0, 60, 60))
    r31= img.find_template(template31, 0.70, step=4, search=SEARCH_EX) #, roi=(10, 0, 60, 60))
    r33= img.find_template(template33, 0.70, step=4, search=SEARCH_EX) #, roi=(10, 0, 60, 60))


    
    r37= img.find_template(template37, 0.70, step=4, search=SEARCH_EX) #, roi=(10, 0, 60, 60))
    r38= img.find_template(template38, 0.70, step=4, search=SEARCH_EX) #, roi=(10, 0, 60, 60))

    r40= img.find_template(template40, 0.70, step=4, search=SEARCH_EX) #, roi=(10, 0, 60, 60))
    r41= img.find_template(template41, 0.70, step=4, search=SEARCH_EX) #, roi=(10, 0, 60, 60))
 
    if flag1==0:
        if r14:
            uart.write("s")
            flag1=1
       
        
    if flag==0:
        if r1:
            img.draw_rectangle(r1)
            flag=1
            uart.write("1")

        if r2:
            img.draw_rectangle(r2)
            flag=2
            uart.write("2")

        if r3:
            img.draw_rectangle(r3)
            flag=3
            uart.write("3")

        if r4:
            img.draw_rectangle(r4)
            flag=4
            uart.write("4")

        if r5:
            img.draw_rectangle(r5)
            uart.write("5")


        if r6:
            img.draw_rectangle(r6)
            uart.write("6")

        if r7:
            img.draw_rectangle(r7)
            uart.write("7")

        if r8:
             img.draw_rectangle(r8)
             uart.write("8")

        if r9:
             img.draw_rectangle(r9)
             uart.write("9")
        
        
        
            

    if flag==1:

        if r14:
            if r18 or r19:
                uart.write("l")
            if r22:
                uart.write("r")
            else:
                uart.write("m")
                import time
                time.sleep(2)
                flag1=0            
        
            
            
    if flag==2:
        if r14:
            if r25 or r26:
                img.draw_rectangle("l")
            if r29:
                uart.write("r")
            else:
                uart.write("m") 
                import time
                time.sleep(2)
                flag1=0
        
            
    if flag==3:
        if r14:
            if r30 or r31:
                img.draw_rectangle("l")
            if r33:
                uart.write("r")
            else:
                uart.write("m") 
                import time
                time.sleep(2)
                flag1=0
            
    if flag==4:
        if r14:
            if r37 or r38:
                uart.write("l")
            if r40 or r41:
                uart.write("r")
            else:
                uart.write("m")
                import time
                time.sleep(2)
                flag1=0 
        



        print(clock.fps())

@dymb 最下面是要开始判断左右了，但是不会，请求大佬帮助一下

@kidswong999 # 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
from image import SEARCH_EX, SEARCH_DS

# 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)
from pyb import UART
uart = UART(3, 115200)
# Load template.
# Template should be a small (eg. 32x32 pixels) grayscale image.
template1 = image.Image("/1.pgm")
template2 = image.Image("/2.pgm")

clock = time.clock()
flag=0

# Run template matching
while (True):
    clock.tick()
    img = sensor.snapshot()



    # 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.



    
    if flag==0:
        r1 = img.find_template(template1, 0.70, step=4, search=SEARCH_EX) #, roi=(10, 0, 60, 60))
        r2 = img.find_template(template2, 0.70, step=4, search=SEARCH_EX) #, roi=(10, 0, 60, 60))
        if r1:
            img.draw_rectangle(r1)
            flag=1
        if r2:
            img.draw_rectangle(r2)
            flag=2
    print(clock.fps())

    if flag==1:
        uart.write("1")
        flag=999

    if flag==2:
        uart.write("2")
        flag=998
        
    r1 = img.find_template(template1, 0.70, step=4, search=SEARCH_EX) #, roi=(10, 0, 60, 60))
    r2 = img.find_template(template2, 0.70, step=4, search=SEARCH_EX) #, roi=(10, 0, 60, 60))
    if flag==999
        if r1:
            img.draw_rectangle(r1)
                if

@kidswong999 哦哦已经想到了用坐标，但是不知道用什么语句

或者说识别到多数字时怎么判断想要的数字是在左边还是右边

怎么解决OSError:Region of interest is smaller thantemplate