#
# NOTE: Please reset the camera after running this script to see the new file.
import sensor, time, image
# Reset sensor
sensor.reset()
# Sensor settings
sensor.set_contrast(3)
sensor.set_gainceiling(16)
sensor.set_framesize(sensor.VGA)
sensor.set_windowing((320, 240))
sensor.set_pixformat(sensor.GRAYSCALE)
sensor.skip_frames(time = 2000)
sensor.set_auto_gain(False, value=100)
FILE_NAME = "desc"
img = sensor.snapshot()
# NOTE: See the docs for other arguments
# NOTE: By default find_keypoints returns multi-scale keypoints extracted from an image pyramid.
kpts = img.find_keypoints(max_keypoints=150, threshold=10, scale_factor=1.2)
if (kpts == None):
raise(Exception("Couldn't find any keypoints!"))
image.save_descriptor(kpts, "/%s.orb"%(FILE_NAME))
img.save("/%s.pgm"%(FILE_NAME))
img.draw_keypoints(kpts)
sensor.snapshot()
e_lab_color_signatures = [] # original enabled threshold indexes
for i in range(len(lab_color_thresholds)):
if lab_color_thresholds[i][7]:
e_lab_color_thresholds.append(lab_color_thresholds[i][0:6])
e_lab_color_code.append(lab_color_thresholds[i][6])
e_lab_color_signatures.append(i + 1)
import image, math, pyb, sensor, struct, time
# Camera Setup
sensor.reset()
sensor.set_pixformat(sensor.RGB565)
sensor.set_framesize(sensor.QVGA)
sensor.skip_frames(time = 2000)
sensor.set_auto_gain(False)
sensor.set_auto_whitebal(False)
# LED Setup
red_led = pyb.LED(1)
green_led = pyb.LED(2)
blue_led = pyb.LED(3)
red_led.off()
green_led.off()
blue_led.off()
# DAC Setup
dac = pyb.DAC("P6") if analog_out_enable else None
# AprilTags Example
#
# This example shows the power of the OpenMV Cam to detect April Tags
# on the OpenMV Cam M7. The M4 versions cannot detect April Tags.
import machine, gc,utime
from pyb import LED
import sensor, image, time, math
import LPF2Class
sensor.reset()
sensor.set_pixformat(sensor.RGB565)
sensor.set_framesize(sensor.QQVGA) # we run out of memory if the resolution is much bigger...
sensor.skip_frames(time = 2000)
sensor.set_auto_gain(False) # must turn this off to prevent image washout...
sensor.set_auto_whitebal(False) # must turn this off to prevent image washout...
print("made it here")
# Note! Unlike find_qrcodes the find_apriltags method does not need lens correction on the image to work.
# The apriltag code supports up to 6 tag families which can be processed at the same time.
# Returned tag objects will have their tag family and id within the tag family.
tag_families = 0
tag_families |= image.TAG16H5 # comment out to disable this family
tag_families |= image.TAG25H7 # comment out to disable this family
tag_families |= image.TAG25H9 # comment out to disable this family
tag_families |= image.TAG36H10 # comment out to disable this family
tag_families |= image.TAG36H11 # comment out to disable this family (default family)
def decrease_exposure(self):
sensor.set_auto_exposure(False,
exposure_us=sensor.get_exposure_us() - 1000)
sensor.set_auto_gain(False)
import sensor
import machine
import image
import time
sensor.reset()
sensor.set_pixformat(sensor.RGB565)
sensor.set_framesize(sensor.QVGA)
sensor.skip_frames(time=2000)
sensor.set_auto_gain(False) # 必须关闭此功能，以防止图像冲洗…
clock = time.clock()
while (True):
clock.tick()
img = sensor.snapshot()
#img.lens_corr(1.8) # 1.8的强度参数对于2.8mm镜头来说是不错的。
for code in img.find_qrcodes():
img.draw_rectangle(code.rect(), color=(160, 0, 0))
#print(code)
message = code.payload()
#print(message)
if (code.payload() == '12'):
print('Extracted materials')
#print(clock.fps())
# 二维码例程
##红绿灯 颜色识别+形状识别(圆形)
#
# Camera Control Code
#
sensor.reset()
sensor.set_pixformat(
sensor.RGB565 if COLOR_LINE_FOLLOWING else sensor.GRAYSCALE)
sensor.set_framesize(FRAME_SIZE)
sensor.set_vflip(True)
sensor.set_hmirror(True)
sensor.set_windowing((int((sensor.width() / 2) - ((sensor.width() / 2) * FRAME_WIDE)), int(sensor.height() * (1.0 - FRAME_REGION)), \
int((sensor.width() / 2) + ((sensor.width() / 2) * FRAME_WIDE)), int(sensor.height() * FRAME_REGION)))
sensor.skip_frames(time=200)
if COLOR_LINE_FOLLOWING: sensor.set_auto_gain(False)
if COLOR_LINE_FOLLOWING: sensor.set_auto_whitebal(False)
clock = time.clock()
#sensor.set_auto_exposure(False, \
# exposure_us = 300)
###########
# Loop
###########
old_time = pyb.millis()
throttle_old_result = None
throttle_i_output = 0
throttle_output = THROTTLE_OFFSET
sensor.reset() sensor.set_pixformat(sensor.RGB565) sensor.set_framesize(sensor.QQVGA) #Resolution, QVGA = 42FPS,QQVGA = 85FPS sensor.skip_frames(time=500) sensor.set_auto_exposure(False) sensor.set_auto_whitebal(False) # Need to let the above settings get in... sensor.skip_frames(time=500) #sensor.set_windowing((35, 8, 112, 112)) # Robot 0 sensor.set_windowing((37, 0, 112, 112)) # Robot 1 # === GAIN === curr_gain = sensor.get_gain_db() sensor.set_auto_gain(False, gain_db=curr_gain) # === EXPOSURE === curr_exposure = sensor.get_exposure_us() sensor.set_auto_exposure(False, exposure_us=int(curr_exposure)) # === WHITE BAL === sensor.set_auto_whitebal(False, rgb_gain_db=((-6.02073, -2.144467, 5.12728))) #sensor.set_auto_whitebal(False, #rgb_gain_db=((-6.02073, -6.02073, 1.376936))) sensor.set_brightness(2) sensor.set_contrast(3) sensor.set_saturation(3)
threshold_index = 0 # 0 for red, 1 for green, 2 for blue
# Color Tracking Thresholds (L Min, L Max, A Min, A Max, B Min, B Max)
thresholds = [
(21, 84, 22, 90, -18, 58), # 橙红色块
(30, 100, -64, -8, -32, 32), # generic_green_thresholds
(0, 30, 0, 64, -128, 0)
] # generic_blue_thresholds
sensor.reset() # 传感器复位
sensor.set_pixformat(
sensor.RGB565
) # RGB565即一个彩色图像点由RGB三个分量组成，总共占据2Byte，高5位为R分量，中间6位为G分量，低5位为B分量
sensor.set_framesize(sensor.QQVGA) # 320*240
sensor.skip_frames(time=500) # 跳过，等待摄像头稳定
sensor.set_auto_gain(False) # 自动增益在颜色识别中一般关闭，不然会影响阈值
sensor.set_auto_whitebal(False) # 白平衡在颜色识别中一般关闭，不然会影响阈值
clock = time.clock() # 构造时钟对象
uart = UART(3, 115200)
uart.init(115200, bits=8, parity=None, stop=1,
timeout_char=1000) # 使用给定参数初始化 timeout_char是以毫秒计的等待字符间的超时时长
class ctrl_info(object):
WorkMode = 0x03 # 色块检测模式 0x01为固定单颜色识别 0x02为自主学习颜色识别 0x03 巡线
Threshold_index = 0x00 # 阈值编号
ctrl = ctrl_info() # 定义控制信息类
single_blob.InitSuccess_LED() # 初始化完成 Green LED 快闪2下
except:
return 0
def prntime(ms):
s = ms / 1000
m, s = divmod(s, 60)
h, m = divmod(m, 60)
d, h = divmod(h, 24)
return d, h, m, s
sensor.reset()
sensor.set_pixformat(sensor.RGB565)
sensor.set_framesize(sensor.QVGA)
sensor.set_auto_gain(1)
sensor.set_auto_exposure(1)
sensor.set_brightness(0)
sensor.set_saturation(0)
sensor.set_contrast(0)
#sensor.skip_frames(5)
sensor.set_hmirror(1) #设置摄像头镜像
sensor.set_vflip(1) #设置摄像头翻转
sensor.run(1)
#task = kpu.load(0x300000) # you need put model(face.kfpkg) in flash at address 0x300000
task = kpu.load("/sd/facedetect.kmodel")
anchor = (1.889, 2.5245, 2.9465, 3.94056, 3.99987, 5.3658, 5.155437, 6.92275,
6.718375, 9.01025)
a = kpu.init_yolo2(task, 0.5, 0.3, 5, anchor)
if not "temp" in os.listdir("/sd/"):
os.mkdir("/sd/temp/") # Make a temp directory
#Use bilt in LED, usefull for debugging purposes
# please note that additional light source reflected from polished bottle surface can confuse camera
red_led = LED(
1
) # use only for debugging / camera status, see previous line / not really used in code
sensor.reset() # Initialize the camera sensor
sensor.set_pixformat(
sensor.GRAYSCALE
) # set camera format to grayscale (color not important in this example)
sensor.set_framesize(sensor.QVGA) # set camera resolution to QVGA 320 x 240
sensor.set_auto_exposure(
False, exposure_us=user_exposure_time
) # set exposure time, user changable (user_exposure_time variable)
sensor.set_auto_gain(
False, gain_db=10
) #set camera gain, keep it as this value is optimised for given light source and exposure time
#these setting are manually set in order to prevent camera to change it during use (no automatic setting in machine vision!)
sensor.set_brightness(2) #set camera brightness
sensor.set_contrast(3) #set camera contrast
sensor.set_saturation(0) #set camera saturation
###################################################
### input tempate images
# Please use small (eg 50 x 100 px or smaller) images in PGM format, grayscale
# place in camera root (stored microSD card)
# note that copy_to_fb = True is not used, since extra fb is required for morphological operations
# newer / more advanced cameras like M7plus may have sufficient memory to store multiple frame buffers and further accelerate code
# this tempalte is compatible to most standard beer bottles available in stores
# if different beer bottle is used, please create your own template that should match provided template
i = 0
for b in bls:
if b.area() > 1000:
i += 1
img.draw_rectangle(b.x(), b.y(), b.w(), b.h(),
(255, 100 * i, 50 * i), 2)
todo = marks_recognition(img, bls)
except:
pass
return todo, a, a1
if __name__ == "__main__":
sensor.reset()
sensor.set_pixformat(sensor.RGB565)
sensor.set_framesize(sensor.QVGA)
sensor.set_auto_gain(False, 15)
sensor.skip_frames(time=2000)
clock = time.clock()
while (True):
clock.tick()
img = sensor.snapshot()
img.lens_corr(1.7, 1.3)
todo, a, a1 = marksss(img)
print(todo, a, a1)
#img.binary([(19, 61, -50, -24, -26, 31)], False)
def findCubeCenter():
"""
find the cube roi position
"""
global roi
sensor.set_auto_whitebal(False)
sensor.set_contrast(2)
cnt = 1
LAB_THRESHOLD = (
(0, 60, -40, 50, -40, 10), # blue
(0, 40, -50, 40, -60, 30), # yellow orange red white
(0, 50, -40, 15, -25, 70))
#(0, 70, -25, 15, -60, 30)) # green
CENTER_THRESHOLD = roi[2] / 3 / 2
gain = 0
while (True):
if cnt > 12:
sensor.set_auto_gain(gain)
img = sensor.snapshot()
if cnt % 60 == 0:
cnt = 1
gain += 10
if (int(cnt / 24)) % 2 == 1:
lab_threshold = LAB_THRESHOLD[int(cnt / 12) - 2]
img = img.binary([lab_threshold])
img = img.dilate(2)
img = img.erode(2)
lcd.display(img)
center_roi = list(
map(int, [
roi[0] + roi[2] / 2 - CENTER_THRESHOLD * 2,
roi[1] + roi[3] / 2 - CENTER_THRESHOLD * 2,
CENTER_THRESHOLD * 4, CENTER_THRESHOLD * 4
]))
squares = []
for r in img.find_rects(roi=center_roi, threshold=500):
if (isSquare(r)):
squares.append(r)
img = img.draw_rectangle(r.rect())
for p in r.corners():
img = img.draw_circle(p[0], p[1], 5, color=(0, 255, 0))
lcd.display(img)
#time.sleep_ms(5000)
if not squares:
cnt += 1
print(cnt)
else:
roi = findCenter(squares, roi, CENTER_THRESHOLD * math.sqrt(2))
center_roi = list(
map(int, [
roi[0] + roi[2] / 2 - CENTER_THRESHOLD * 2,
roi[1] + roi[3] / 2 - CENTER_THRESHOLD * 2,
CENTER_THRESHOLD * 4, CENTER_THRESHOLD * 4
]))
img = img.draw_rectangle(center_roi)
img = img.draw_rectangle(roi)
lcd.display(img)
sensor.reset()
sensor.set_pixformat(sensor.RGB565)
sensor.set_framesize(sensor.QQVGA)
sensor.set_auto_whitebal(False)
sensor.skip_frames(time=60)
gain = sensor.get_gain_db()
sensor.set_auto_gain(0, gain)
sensor.skip_frames(time=60)
sensor.set_auto_exposure(0, 80000)
sensor.skip_frames(time=60)
return 1
# In Memory Shadow Removal w/ Frame Differencing Example
#
# This example demonstrates using frame differencing with your OpenMV Cam using
# shadow removal to help reduce the affects of cast shadows in your scene.
import sensor, image, pyb, os, time
TRIGGER_THRESHOLD = 5
sensor.reset() # Initialize the camera sensor.
sensor.set_pixformat(sensor.GRAYSCALE) # or sensor.GRAYSCALE
sensor.set_framesize(sensor.QQVGA) # or sensor.QVGA (or others)
if sensor.get_id() == sensor.OV7725: # Reduce sensor PLL from 6x to 4x.
sensor.__write_reg(0x0D, (sensor.__read_reg(0x0D) & 0x3F) | 0x40)
sensor.skip_frames(time = 2000) # Let new settings take affect.
sensor.set_auto_gain(False) # Turn this off too.
clock = time.clock() # Tracks FPS.
# Take from the main frame buffer's RAM to allocate a second frame buffer.
# There's a lot more RAM in the frame buffer than in the MicroPython heap.
# However, after doing this you have a lot less RAM for some algorithms...
# So, be aware that it's a lot easier to get out of RAM issues now. However,
# frame differencing doesn't use a lot of the extra space in the frame buffer.
# But, things like AprilTags do and won't work if you do this...
extra_fb = sensor.alloc_extra_fb(sensor.width(), sensor.height(), sensor.GRAYSCALE)
print("About to save background image...")
sensor.skip_frames(time = 2000) # Give the user time to get ready.
extra_fb.replace(sensor.snapshot())
print("Saved background image - Now frame differencing!")
#
# CIFAR is a convolutional nueral network designed to classify it's field of view into several
# different object types and works on RGB video data.
#
# In this example we slide the LeNet detector window over the image and get a list of activations
# where there might be an object. Note that use a CNN with a sliding window is extremely compute
# expensive so for an exhaustive search do not expect the CNN to be real-time.
import sensor, image, time, os, nn
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((128, 128)) # Set 128x128 window.
sensor.skip_frames(time=750) # Don't let autogain run very long.
sensor.set_auto_gain(False) # Turn off autogain.
sensor.set_auto_exposure(False) # Turn off whitebalance.
# Load cifar10 network (You can get the network from OpenMV IDE).
net = nn.load('/cifar10.network')
# Faster, smaller and less accurate.
# net = nn.load('/cifar10_fast.network')
labels = ['airplane', 'automobile', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']
clock = time.clock()
while(True):
clock.tick()
img = sensor.snapshot()
# net.search() will search an roi in the image for the network (or the whole image if the roi is not
# and then use gain control to make up any remaining ground.
# We can achieve the above by setting a gain ceiling on the automatic
# gain control algorithm. Once this is set the algorithm will have to
# increase the exposure time to meet any gain needs versus using gain
# to do so. However, this comes at the price of the exposure time varying
# more when the lighting changes versus the exposure being constant and
# the gain changing.
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)
# The gain db ceiling maxes out at about 24 db for the OV7725 sensor.
sensor.set_auto_gain(True, gain_db_ceiling = 16.0) # Default gain.
# Note! If you set the gain ceiling to low without adjusting the exposure control
# target value then you'll just get a lot of oscillation from the exposure
# control if it's on.
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("FPS %f, Gain %f dB, Exposure %d us" % \
(clock.fps(), sensor.get_gain_db(), sensor.get_exposure_us()))
# Untitled - By: oillight - 周六 4月 27 2019
import sensor, image, time, math
from pyb import UART, LED
sensor.reset()
sensor.set_auto_exposure(False, 1700)
sensor.set_pixformat(sensor.GRAYSCALE)
sensor.set_framesize(sensor.QQQVGA)
sensor.set_auto_gain(False, 27)
sensor.skip_frames(time=2000)
led_B = LED(3)
led_G = LED(2)
led_R = LED(1)
clock = time.clock()
def get_average(data):
average = sum(data) / len(data)
average = round(average)
return average
def filter(data):
length = len(data)
if length > 2:
sorted(data)
tmps = data[1:length]
else:
tmps = data
average = get_average(tmps)
def cal():
flag=0
zfx=0
yx=0
sjx=0
r=[0,0,0,0]
key = 0
G=0
while(True):
key=uart.readchar()
if key==1:
break
sum_zfx=0
sum_yx=0
sum_sjx=0
dis=0
clock.tick()
img = sensor.snapshot(1.8)
#img1 = img.binary(blue)
for x in templates :
img = sensor.snapshot(1.8)
img = img.to_grayscale()
flag = 0
for t in x:
clock.tick()
img = sensor.snapshot(1.8)
img = img.to_grayscale()
template = image.Image(t)
#ball = image.Image(t)
if x == zfx_tempaltes:
r = img.find_template(template, 0.80, step=4, search=SEARCH_EX) #, roi=(10, 0, 60, 60))
if r:
print(t)
zfx = r
sum_zfx=sum_zfx+1
elif x == yx_tempaltes:
for c in img.find_circles(threshold = 3500, x_margin = 10, y_margin = 10, r_margin = 10,r_min = 2, r_max = 100, r_step = 2):
img.draw_circle(c.x(), c.y(), c.r(), color = (255, 0, 0))
if c.r()>1:
x=c.x()-c.r()
y=c.y()-c.r()
w=c.r()*2
h=c.r()*2
r=[x,y,w,h]
yx = r
sum_yx=20
elif x == sjx_tempaltes:
r = img.find_template(template, 0.80, step=4, search=SEARCH_EX) #, roi=(10, 0, 60, 60))
if r:
print(t)
sjx = r
sum_sjx=sum_sjx+1
if (sum_zfx>sum_yx and sum_zfx>sum_sjx) :
r=zfx
t=8#"zfx"
elif (sum_yx>sum_zfx and sum_yx>sum_sjx) :
r=yx
t=9#"yx"
else:
r=sjx
t=10#"sjx"
if (sum_zfx!=0 or sum_yx!=0 or sum_sjx!=0):
#change[0]=r[0]+0
#change[1]=r[1]+0
#change[2]=r[2]-0
#change[3]=r[3]-0
sum_red=0
sum_green=0
sum_blue=0
x=r[0]
y=r[1]
w=r[2]
h=r[3]
center_x=r[0]+int(r[2]/2)
center_y=r[1]+int(r[3]/2)
sensor.reset()
sensor.set_pixformat(sensor.RGB565)
sensor.set_framesize(sensor.QQVGA)
sensor.skip_frames(time = 300)
sensor.set_auto_gain(False) # must be turned off for color tracking
sensor.set_auto_whitebal(False) # must be turned off for color tracking
sensor.set_vflip(False)
sensor.set_hmirror(False)
img = sensor.snapshot(1.8)
#r=list(r)
i=3
while(i>0):
blobs = img.find_blobs(blue,roi=r,pixel_threshold=200,area_threshold=200)
if blobs:
max_blob = find_max(blobs)
img.draw_rectangle(r) # rect
#img.draw_cross(center_x, center_y) # cx, cy
img.draw_cross(max_blob.cx(), max_blob.cy())
#img.draw_line(x+int(w/2),y,x,y+h)
#img.draw_line(x,y+h,x+w,y+h)
#img.draw_line(x+w,y+h,x+int(w/2),y)#三角形
img.draw_circle(x+int(w/2),y+int(h/2),int(w/2))
sum_blue=sum_blue+1
blobs = img.find_blobs(red,roi=r,pixel_threshold=200,area_threshold=200)
if blobs:
max_blob = find_max(blobs)
img.draw_rectangle(r) # rect
img.draw_cross(center_x, center_y) # cx, cy
img.draw_circle(x+int(w/2),y+int(h/2),int(h/2))
sum_red=sum_red+1
blobs = img.find_blobs(green,roi=r,pixel_threshold=200,area_threshold=200)
if blobs:
max_blob = find_max(blobs)
img.draw_rectangle(r) # rect
img.draw_cross(center_x, center_y) # cx, cy
sum_green=sum_green+1
i=i-1
if (sum_red>sum_green and sum_red>sum_blue) :
flag=5#"red"
elif (sum_green>sum_red and sum_green>sum_blue) :
flag=6#"green"
elif (sum_blue>sum_red and sum_blue>sum_green):
flag=7#"blue"
else :
flag = 0
if(r==0 or flag == 0):
print("没找到")
else:
Lm = int(r[2]/2)
K = 25
G=1
length = K/Lm
#edge =
print("length:",length)
print("color:",flag,"object:",t,"range:",r,"red:",sum_red,
"green:",sum_green,"blue:",sum_blue,"zfx_model:",sum_zfx,"yx_model:",
sum_yx,"sjx_model:",sum_sjx)
uart.writechar(0x55)
uart.writechar(0x53)
uart.writechar(flag)
uart.writechar(t)
uart.writechar(Lm)
uart.writechar(K)
uart.writechar(G)
uart.writechar(1)
G=0
break
sensor_size = sensor.QVGA
sensor.set_pixformat(sensor_format)
sensor.set_framesize(sensor_size)
# Note that there is a -2 compared to QVGA as there looks to be a little misalignement when moving from QVGA to QQQVGA
# As we use the same remap arrays for both definitions, it is important that both are aligned
sensor.set_windowing(
(int((sensor.width() - img_width) / 2) - 2,
int((sensor.height() - img_height) / 2), img_width, img_height))
#get the gains and exposure
gain_db = sensor.get_gain_db()
exposure_us = sensor.get_exposure_us()
rgb_gain_db = sensor.get_rgb_gain_db()
# Set the gain and exposure to fixed values (we dont care about the values)
sensor.set_auto_gain(False, gain_db)
sensor.set_auto_exposure(False, exposure_us)
sensor.set_auto_whitebal(False, rgb_gain_db)
# Setup contrast, brightness and saturation
sensor.set_contrast(0) # range -3 to +3
sensor.set_brightness(0) # range -3 to +3
sensor.set_saturation(0) # range -3 to +3
# Disable night mode (auto frame rate) and black level calibration (BLC)
sensor.__write_reg(0x0E, 0b00000000) # Disable night mode
sensor.__write_reg(0x3E, 0b00000000) # Disable BLC
sensor.__write_reg(0x13, 0b00000000) # disable automated gain
# CIFAR10 Example
import sensor, image, time, os, nn
sensor.reset() # Reset and initialize the sensor.
sensor.set_contrast(3)
sensor.set_pixformat(sensor.RGB565) # Set pixel format to RGB565
sensor.set_framesize(sensor.QVGA) # Set frame size to QVGA (320x240)
sensor.set_windowing((128, 128)) # Set 128x128 window.
sensor.skip_frames(time=100)
sensor.set_auto_gain(True)
sensor.set_auto_exposure(True)
# Load cifar10 network
#cwd = os.getcwd()
#dirs = os.listdir(cwd)
#print(dirs)
#net = nn.load('cifar10.network')
# Faster, smaller and less accurate.
net = nn.load('/cifar10_fast.network')
labels = [
'airplane', 'automobile', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse',
'ship', 'truck'
]
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.
out = net.forward(img)
max_idx = out.index(max(out))
]
# Compute the weight divisor (we're computing this so you don't have to make weights add to 1).
weight_sum = 0
for r in ROIS: weight_sum += r[4] # r[4] is the roi weight.
# Camera setup...
clock = time.clock() # Tracks FPS.
sensor.reset() # Initialize the camera sensor.
sensor.__write_reg(0x6B, 0x22) # switches camera into advanced calibration mode. See this for more: http://forums.openmv.io/viewtopic.php?p=1358#p1358
sensor.set_pixformat(sensor.RGB565)
sensor.set_framesize(sensor.QQVGA) # use QQVGA for speed.
sensor.set_vflip(True)
sensor.set_hmirror(True)
sensor.set_auto_gain(True) # do some calibration at the start
sensor.set_auto_whitebal(True)
sensor.skip_frames(60) # Let new settings take effect.
sensor.set_auto_gain(False) # now turn off autocalibration before we start color tracking
sensor.set_auto_whitebal(False)
while(True):
clock.tick() # Track elapsed milliseconds between snapshots().
img = sensor.snapshot() # Take a picture and return the image.
centroid_sum = 0
for r in ROIS:
blobs = img.find_blobs([thresholds[threshold_index]], roi=r[0:4], merge=True) # r[0:4] is roi tuple.
if blobs:
# Find the index of the blob with the most pixels.
sensor.set_pixformat(sensor.RGB565)
#设置图像色彩格式，有RGB565色彩图和GRAYSCALE灰度图两种
sensor.set_framesize(sensor.QVGA)
#设置图像像素大小
sensor.set_windowing((240, 240)) # VGA中心240x240像素
sensor.set_auto_exposure(False, exposure_us=2000)
sensor.set_auto_gain(False) # 颜色跟踪必须关闭自动增益
sensor.set_auto_whitebal(False, rgb_gain_db = (1,-1,1))
sensor.skip_frames(time = 2000)
clock1 = time.clock()
clock2 = time.clock()
while(True)
x=0
y=0
w=0
h=0
while(1): #1循环——不断找色块的循环
clock1.tick()
img = sensor.snapshot()
blobs = img.find_blobs([thresholds],False,x_stride=2,y_stride=1,area_threshold=10,merge=True)
print(clock1.fps())
if blobs:
# pass the thresholding test... otherwise, you don't get a distance
# benefit.
import sensor, image, time, math, omv
# Set the thresholds to find a white object (i.e. tag border)
thresholds = (150, 255)
sensor.reset()
sensor.set_pixformat(sensor.GRAYSCALE)
if omv.board_type() == "H7": sensor.set_framesize(sensor.VGA)
elif omv.board_type() == "M7": sensor.set_framesize(sensor.QVGA)
else: raise Exception("You need a more powerful OpenMV Cam to run this script")
sensor.skip_frames(
time=200) # increase this to let the auto methods run for longer
sensor.set_auto_gain(False) # must be turned off for color tracking
sensor.set_auto_whitebal(False) # must be turned off for color tracking
clock = time.clock()
# The apriltag code supports up to 6 tag families which can be processed at the same time.
# Returned tag objects will have their tag family and id within the tag family.
tag_families = 0
tag_families |= image.TAG16H5 # comment out to disable this family
tag_families |= image.TAG25H7 # comment out to disable this family
tag_families |= image.TAG25H9 # comment out to disable this family
tag_families |= image.TAG36H10 # comment out to disable this family
tag_families |= image.TAG36H11 # comment out to disable this family (default family)
tag_families |= image.ARTOOLKIT # comment out to disable this family
while (True):
clock.tick()
from pyb import UART
from pyb import Servo
RED_LED_PIN = 1
BLUE_LED_PIN = 3
TS = 3
lcd.init()
threshold_index = 0 # threshold_index的数值对应了列表thresholds[]中的色块LAB
# 口罩的LAB数值写入列表thresholds[]中 支持写入多组
thresholds = [(30, 66, -2, -32, -29, -11), #口罩1
(30, 66, -2, -32, -29, -11), #粉色
(30, 66, -2, -32, -29, -11)] #浅黄
sensor.set_auto_gain(False) # 关闭白平衡
sensor.set_auto_whitebal(False) # 关闭自动增益
clock = time.clock()
uart = UART(3, 115200)
class collect_face:
def collect(self):
sensor.reset() # Initialize the camera sensor.
sensor.set_pixformat(sensor.GRAYSCALE) # or sensor.GRAYSCALE
sensor.set_framesize(sensor.B128X128) # or sensor.QQVGA (or others)
sensor.set_windowing((92,112))
sensor.skip_frames(10) # Let new settings take affect.
sensor.skip_frames(time = 2000)
num = 4 #设置被拍摄者序号，第一个人的图片保存到s1文件夹，第二个人的图片保存到s2文件夹，以此类推。每次更换拍摄者时，修改num值。
e_lab_color_signatures = [] # original enabled threshold indexes
for i in range(len(lab_color_thresholds)):
if lab_color_thresholds[i][7]:
e_lab_color_thresholds.append(lab_color_thresholds[i][0:6])
e_lab_color_code.append(lab_color_thresholds[i][6])
e_lab_color_signatures.append(i + 1)
import image, math, pyb, sensor, struct, time
# Camera Setup
sensor.reset()
sensor.set_pixformat(sensor.GRAYSCALE)
sensor.set_framesize(sensor.QVGA)
sensor.skip_frames(time = 2000)
sensor.set_auto_gain(False)
# LED Setup
red_led = pyb.LED(1)
green_led = pyb.LED(2)
blue_led = pyb.LED(3)
red_led.off()
green_led.off()
blue_led.off()
# DAC Setup
dac = pyb.DAC("P6") if analog_out_enable else None
pid_x = PID(100, 0, 0,
0.7, 0.3, 0,
0, 0, 0)
pid_y = PID(100, 0, 0,
-0.7, -0.3, 0,
0, 0, 0)
sensor.reset() # 初始化摄像头
sensor.set_pixformat(sensor.RGB565) # 格式为 RGB565.
sensor.set_framesize(sensor.QQVGA) # 使用 QQVGA 速度快一些
sensor.set_auto_exposure(False,35000) # 设置曝光时间
sensor.skip_frames(time = 2000) # 跳过2000s，使新设置生效,并自动调节白平衡
sensor.set_auto_gain(False)# 关闭自动增益
sensor.set_auto_whitebal(False)#关闭白平衡。白平衡是默认开启的，在颜色识别中，一定要关闭白平衡。
uart = UART(3, 115200)
control_val_x=1350#云台初始x位置
control_val_y=1100#云台初始y位置
MIN_LIMIT_x=1000#云台x最小值
MAX_LIMIT_x=1700#云台x最大值
MIN_LIMIT_y=950#云台y最小值
MAX_LIMIT_y=1250#云台y最大值
ans_x=70#弹道落点在图片上的x坐标
ans_y=54#弹道落点在图片上的y坐标
#
# This example demonstrates using frame differencing with your OpenMV Cam using
# shadow removal to help reduce the affects of cast shadows in your scene.
import sensor, image, pyb, os, time
TRIGGER_THRESHOLD = 5
sensor.reset() # Initialize the camera sensor.
sensor.set_pixformat(sensor.RGB565) # or sensor.GRAYSCALE
sensor.set_framesize(sensor.QQVGA) # or sensor.QVGA (or others)
if sensor.get_id() == sensor.OV7725: # Reduce sensor PLL from 6x to 4x.
sensor.__write_reg(0x0D, (sensor.__read_reg(0x0D) & 0x3F) | 0x40)
sensor.skip_frames(time = 2000) # Let new settings take affect.
sensor.set_auto_whitebal(False) # Turn off white balance.
sensor.set_auto_gain(False) # Turn this off too.
clock = time.clock() # Tracks FPS.
# Take from the main frame buffer's RAM to allocate a second frame buffer.
# There's a lot more RAM in the frame buffer than in the MicroPython heap.
# However, after doing this you have a lot less RAM for some algorithms...
# So, be aware that it's a lot easier to get out of RAM issues now. However,
# frame differencing doesn't use a lot of the extra space in the frame buffer.
# But, things like AprilTags do and won't work if you do this...
extra_fb = sensor.alloc_extra_fb(sensor.width(), sensor.height(), sensor.RGB565)
print("About to save background image...")
sensor.skip_frames(time = 2000) # Give the user time to get ready.
extra_fb.replace(sensor.snapshot())
print("Saved background image - Now frame differencing!")
clock = time.clock() # Create a clock object to track the FPS.
# You have to turn automatic exposure control and automatic white blance off
# otherwise they will change the image exposure to undo any gain settings
# that you put in place...
sensor.set_auto_exposure(False)
# Need to let the above settings get in...
sensor.skip_frames(time=500)
current_gain_in_decibels = sensor.get_gain_db()
print("Current Gain == %f db" % current_gain_in_decibels)
# Auto gain control (AGC) is enabled by default. Calling the below function
# disables sensor auto gain control. The additionally "gain_db"
# argument then overrides the auto gain value after AGC is disabled.
sensor.set_auto_gain(False, \
gain_db = current_gain_in_decibels * GAIN_SCALE)
print("New gain == %f db" % sensor.get_gain_db())
# sensor.get_gain_db() returns the exact camera sensor gain decibels.
# However, this may be a different number than what was commanded because
# the sensor code converts the gain to a small and large gain value which
# aren't able to accept all possible values...
# If you want to turn auto gain back on do: sensor.set_auto_gain(True)
# Note that the camera sensor will then change the gain as it likes.
# Doing: sensor.set_auto_gain(False)
# Just disables the gain value update but does not change the gain
# value the camera sensor determined was good.
while (True):
# You have to turn automatic exposure control and automatic white blance off
# otherwise they will change the image exposure to undo any gain settings
# that you put in place...
sensor.set_auto_exposure(False)
sensor.set_auto_whitebal(False)
# Need to let the above settings get in...
sensor.skip_frames(time = 500)
current_gain_in_decibels = sensor.get_gain_db()
print("Current Gain == %f db" % current_gain_in_decibels)
# Auto gain control (AGC) is enabled by default. Calling the below function
# disables sensor auto gain control. The additionally "gain_db"
# argument then overrides the auto gain value after AGC is disabled.
sensor.set_auto_gain(False, \
gain_db = current_gain_in_decibels * GAIN_SCALE)
print("New gain == %f db" % sensor.get_gain_db())
# sensor.get_gain_db() returns the exact camera sensor gain decibels.
# However, this may be a different number than what was commanded because
# the sensor code converts the gain to a small and large gain value which
# aren't able to accept all possible values...
# If you want to turn auto gain back on do: sensor.set_auto_gain(True)
# Note that the camera sensor will then change the gain as it likes.
# Doing: sensor.set_auto_gain(False)
# Just disables the gain value update but does not change the gain
# value the camera sensor determined was good.
while(True):
flagB = 1
# 红绿蓝 阈值 要根据实际颜色调试
red_threshold = (93, 24, 98, 29, -54, 90)
green_threshold = (34, 13, -14, -124, -106, 34)
blue_threshold = (48, 0, -68, 87, -128, -18)
# RGB 阈值的组合 对应code 1 2 4
thresholds = [(93, 24, 98, 29, -54, 90), (34, 13, -14, -124, -106, 34),
(48, 0, -68, 87, -128, -18)]
sensor.reset()
sensor.set_pixformat(sensor.RGB565)
sensor.set_framesize(sensor.QVGA)
sensor.skip_frames(time=2000)
sensor.set_auto_gain(True) # 自动增益打开
sensor.set_auto_whitebal(False) # 白平衡关闭
clock = time.clock()
while (True):
clock.tick()
img = sensor.snapshot()
for blob in img.find_blobs(thresholds, pixels_threshold=2000, merge=False):
img.draw_rectangle(blob.rect())
img.draw_cross(blob.cx(), blob.cy())
print("Pixel = ")
print(blob.pixels())
print("\r\n")
print("Area = ")
print(blob.area())
# Find Data Matrices Example
#
# This example shows off how easy it is to detect data matrices using the
# OpenMV Cam M7. Data matrices detection does not work on the M4 Camera.
import sensor, image, time, math
sensor.reset()
sensor.set_pixformat(sensor.RGB565)
sensor.set_framesize(sensor.QVGA)
sensor.skip_frames(time = 2000)
sensor.set_auto_gain(False) # must turn this off to prevent image washout...
sensor.set_auto_whitebal(False) # must turn this off to prevent image washout...
clock = time.clock()
while(True):
clock.tick()
img = sensor.snapshot()
img.lens_corr(1.8) # strength of 1.8 is good for the 2.8mm lens.
matrices = img.find_datamatrices()
for matrix in matrices:
img.draw_rectangle(matrix.rect(), color = (255, 0, 0))
print_args = (matrix.rows(), matrix.columns(), matrix.payload(), (180 * matrix.rotation()) / math.pi, clock.fps())
print("Matrix [%d:%d], Payload \"%s\", rotation %f (degrees), FPS %f" % print_args)
if not matrices:
print("FPS %f" % clock.fps())
#
# NOTE: Please reset the camera after running this script to see the new file.
import sensor, time, image
# Reset sensor
sensor.reset()
# Sensor settings
sensor.set_contrast(3)
sensor.set_gainceiling(16)
sensor.set_framesize(sensor.VGA)
sensor.set_windowing((320, 240))
sensor.set_pixformat(sensor.GRAYSCALE)
sensor.skip_frames(time = 2000)
sensor.set_auto_gain(False, value=100)
FILE_NAME = "desc"
img = sensor.snapshot()
# NOTE: See the docs for other arguments
# NOTE: By default find_keypoints returns multi-scale keypoints extracted from an image pyramid.
kpts = img.find_keypoints(max_keypoints=150, threshold=10, scale_factor=1.2)
if (kpts == None):
raise(Exception("Couldn't find any keypoints!"))
image.save_descriptor(kpts, "/%s.orb"%(FILE_NAME))
img.save("/%s.pgm"%(FILE_NAME))
img.draw_keypoints(kpts)
sensor.snapshot()
# A color code is a blob composed of two or more colors. The example below will
# only track colored objects which have both the colors below in them.
import sensor, image, time, math
# Color Tracking Thresholds (L Min, L Max, A Min, A Max, B Min, B Max)
# The below thresholds track in general red/green things. You may wish to tune them...
thresholds = [(30, 100, 15, 127, 15, 127), # generic_red_thresholds -> index is 0 so code == (1 << 0)
(30, 100, -64, -8, -32, 32)] # generic_green_thresholds -> index is 1 so code == (1 << 1)
# Codes are or'ed together when "merge=True" for "find_blobs".
sensor.reset()
sensor.set_pixformat(sensor.RGB565)
sensor.set_framesize(sensor.QVGA)
sensor.skip_frames(time = 2000)
sensor.set_auto_gain(False) # must be turned off for color tracking
sensor.set_auto_whitebal(False) # must be turned off for color tracking
clock = time.clock()
# Only blobs that with more pixels than "pixel_threshold" and more area than "area_threshold" are
# returned by "find_blobs" below. Change "pixels_threshold" and "area_threshold" if you change the
# camera resolution. "merge=True" must be set to merge overlapping color blobs for color codes.
while(True):
clock.tick()
img = sensor.snapshot()
for blob in img.find_blobs(thresholds, pixels_threshold=100, area_threshold=100, merge=True):
if blob.code() == 3: # r/g code == (1 << 1) | (1 << 0)
# These values depend on the blob not being circular - otherwise they will be shaky.
if blob.elongation() > 0.5:
img.draw_edges(blob.min_corners(), color=(255,0,0))
#
# CIFAR is a convolutional nueral network designed to classify it's field of view into several
# different object types and works on RGB video data.
#
# In this example we slide the LeNet detector window over the image and get a list of activations
# where there might be an object. Note that use a CNN with a sliding window is extremely compute
# expensive so for an exhaustive search do not expect the CNN to be real-time.
import sensor, image, time, os, nn
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((128, 128)) # Set 128x128 window.
sensor.skip_frames(time=750) # Don't let autogain run very long.
sensor.set_auto_gain(False) # Turn off autogain.
sensor.set_auto_exposure(False) # Turn off whitebalance.
# Load cifar10 network (You can get the network from OpenMV IDE).
net = nn.load('/cifar10.network')
# Faster, smaller and less accurate.
# net = nn.load('/cifar10_fast.network')
labels = ['airplane', 'automobile', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']
clock = time.clock()
while(True):
clock.tick()
img = sensor.snapshot()
# net.search() will search an roi in the image for the network (or the whole image if the roi is not
