# Your OpenMV Cam supports power of 2 resolutions of 64x32, 64x64,
# 128x64, and 128x128. If you want a resolution of 32x32 you can create
# it by doing "img.pool(2, 2)" on a 64x64 image.
sensor.reset() # Reset and initialize the sensor.
sensor.set_pixformat(sensor.RGB565) # Set pixel format to RGB565 (or GRAYSCALE)
sensor.set_framesize(sensor.B64X64) # Set frame size to 64x64... (or 64x32)...
sensor.skip_frames(time = 2000) # Wait for settings take effect.
clock = time.clock() # Create a clock object to track the 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.
extra_fb = sensor.alloc_extra_fb(sensor.width(), sensor.height(), sensor.RGB565)
extra_fb.replace(sensor.snapshot())
while(True):
clock.tick() # Track elapsed milliseconds between snapshots().
img = sensor.snapshot() # Take a picture and return the image.
# This algorithm is hard to test without a perfect jig... So, here's a cheat to see it works.
# Put in a z_rotation value below and you should see the r output be equal to that.
if(0):
expected_rotation = 20.0
extra_fb.rotation_corr(z_rotation=(-expected_rotation))
# This algorithm is hard to test without a perfect jig... So, here's a cheat to see it works.
# Put in a zoom value below and you should see the z output be equal to that.
if(0):
# Your OpenMV Cam supports power of 2 resolutions of 64x32, 64x64,
# 128x64, and 128x128. If you want a resolution of 32x32 you can create
# it by doing "img.pool(2, 2)" on a 64x64 image.
sensor.reset() # Reset and initialize the sensor.
sensor.set_pixformat(sensor.GRAYSCALE) # Set pixel format to GRAYSCALE (or RGB565)
sensor.set_framesize(sensor.B128X128) # Set frame size to 128x128... (or 128x64)...
sensor.skip_frames(time = 2000) # Wait for settings take effect.
clock = time.clock() # Create a clock object to track the 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.
extra_fb = sensor.alloc_extra_fb(sensor.width(), sensor.height(), sensor.GRAYSCALE)
extra_fb.replace(sensor.snapshot())
while(True):
clock.tick() # Track elapsed milliseconds between snapshots().
img = sensor.snapshot() # Take a picture and return the image.
for y in range(0, sensor.height(), BLOCK_H):
for x in range(0, sensor.width(), BLOCK_W):
displacement = extra_fb.find_displacement(img, \
roi = (x, y, BLOCK_W, BLOCK_H), template_roi = (x, y, BLOCK_W, BLOCK_H))
# Below 0.1 or so (YMMV) and the results are just noise.
if(displacement.response() > 0.1):
pixel_x = x + (BLOCK_W//2) + int(displacement.x_translation())
pixel_y = y + (BLOCK_H//2) + int(displacement.y_translation())
packet_sequence += 1
uart.write(temp)
sensor.reset() # Reset and initialize the sensor.
sensor.set_pixformat(
sensor.RGB565) # Set pixel format to RGB565 (or GRAYSCALE)
sensor.set_framesize(sensor.B64X32) # Set frame size to 64x32... (or 64x64)...
sensor.skip_frames(time=2000) # Wait for settings take effect.
clock = time.clock() # Create a clock object to track the 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.
extra_fb = sensor.alloc_extra_fb(sensor.width(), sensor.height(),
sensor.RGB565)
extra_fb.replace(sensor.snapshot())
while (True):
clock.tick() # Track elapsed milliseconds between snapshots().
img = sensor.snapshot() # Take a picture and return the image.
displacement = extra_fb.find_displacement(img)
extra_fb.replace(img)
# Offset results are noisy without filtering so we drop some accuracy.
sub_pixel_x = int(displacement.x_translation() * 5) / 5.0
sub_pixel_y = int(displacement.y_translation() * 5) / 5.0
if (displacement.response() > MAV_OPTICAL_FLOW_confidence_threshold):
send_optical_flow_packet(sub_pixel_x, sub_pixel_y,
checksum(temp, MAV_OPTICAL_FLOW_extra_crc))
packet_sequence += 1
uart.write(temp)
update_led()
sensor.reset() # Reset and initialize the sensor.
sensor.set_pixformat(sensor.GRAYSCALE) # Set pixel format to RGB565 (or GRAYSCALE)
sensor.set_framesize(sensor.B64X32) # Set frame size to 64x32... (or 64x64)...
sensor.skip_frames(time = 2000) # Wait for settings take effect.
clock = time.clock() # Create a clock object to track the 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.
extra_fb = sensor.alloc_extra_fb(sensor.width(), sensor.height(), sensor.GRAYSCALE)
extra_fb.replace(sensor.snapshot())
while(True):
clock.tick() # Track elapsed milliseconds between snapshots().
img = sensor.snapshot() # Take a picture and return the image.
displacement = extra_fb.find_displacement(img)
extra_fb.replace(img)
# Offset results are noisy without filtering so we drop some accuracy.
sub_pixel_x = int(-displacement.x_translation() * 35)
sub_pixel_y = int(displacement.y_translation() * 53)
send_optical_flow_packet(sub_pixel_x, sub_pixel_y, displacement.response())
global flag
flag = 1
tim = Timer(4, freq=20) # create a timer object using timer 4 - trigger at 1Hz
tim.callback(tick) # set the callback to our tick function
#--------------------------------------while循环开始-----------------------------------------#
while (True):
#led.on()
if (flag == 1):
img = sensor.snapshot()
img_old = img.copy()
img.lens_corr(1.5) # for 2.8mm lens...摄像头畸变纠正
#--------------------------------------光流定点-----------------------------------------#
old = sensor.alloc_extra_fb(16, 16, sensor.GRAYSCALE)
old.replace(sensor.snapshot().mean_pooled(4, 4))
new_img = sensor.snapshot().mean_pooled(4, 4)
displacement = old.find_displacement(new_img)
old_img.replace(new_img)
delta_x0 = int(displacement.x_translation() * 5) / 5.0
delta_y0 = int(displacement.y_translation() * 5) / 5.0
delta_x = 10 * delta_x0
delta_y = 10 * delta_y0
#--------------------------------------检测直线交点的位置---------------------------------------#
lines = img_old.find_lines(threshold=1000,
theta_margin=50,
rho_margin=50)
for i in range(0, len(lines) - 1):
for j in range(i + 1, len(lines)):
l0x1 = lines[i].x1()
1] != remote_g_gain_db or rgb_gain_db[2] != remote_b_gain_db:
print("rgb_gain_db is " + str(rgb_gain_db) + " but we got [" +
str(remote_r_gain_db) + "," + str(remote_g_gain_db) + "," +
str(remote_b_gain_db) + "]")
exit(1)
else:
print("Could not configure the remote cam!")
exit(1)
# skip frames as this is exactly what the remote is doing on its side
sensor.skip_frames(time=2000)
# save the ref image used for the diff
print("About to save background image...")
ref_img = sensor.alloc_extra_fb(img_width, img_height, sensor_format)
data_fb = sensor.alloc_extra_fb(img_width, img_height, sensor.RGB565)
ref_img.replace(sensor.snapshot().remap(data_fb, right=False,
upside_down=True))
sensor.dealloc_extra_fb()
print("Saved background image - Now frame differencing!")
# now add an additional part that will convey the mask info
sensor.set_windowing((int((sensor.width() - img_width) / 2) - 2,
int((sensor.height() - img_height) / 2), img_width,
img_height + mask_height))
time.sleep(500)
clock = time.clock()
idx = 0
data_fb = sensor.alloc_extra_fb(img_width, img_height, sensor.RGB565)
green_led = LED(2)
green_led.off()
blue_led = LED(3)
blue_led.off()
class DataTX():
img_width = 320
img_height = 240
sent = False
buff = None
dataTX = DataTX()
dataRX = DataTX()
dataTX.buff = sensor.alloc_extra_fb(DataTX.img_width, DataTX.img_height,
sensor.RGB565)
dataRX.buff = sensor.alloc_extra_fb(DataTX.img_width, DataTX.img_height,
sensor.RGB565)
usb = USB_VCP()
blue_led.on()
spi_error = False
debug_image = False
control = CameraSlaveControl()
control.column_offset = 10
control.row_offset = 10
control.column_zoom_numerator = 22
control.column_zoom_denominator = 20
if col == begin:
continue
img.draw_line(begin, col, end, col, color=120, thickness=1)
return img
sensor.reset()
sensor.set_pixformat(sensor.GRAYSCALE)
sensor.set_framesize(sensor.B64X64)
sensor.set_auto_whitebal(False) # Turn off white balance.
sensor.set_auto_gain(False, gain_db=8)
sensor.skip_frames(time=2000)
clock = time.clock()
buffer1 = sensor.alloc_extra_fb(sensor.width(), sensor.height(),
sensor.GRAYSCALE)
buffer2 = sensor.alloc_extra_fb(sensor.width(), sensor.height(),
sensor.GRAYSCALE)
sensor.skip_frames(time=500)
buffer1.replace(sensor.snapshot())
oddframe = True # Tracks if the frame number is odd or not.
pyb.LED(BLUE_LED_PIN).on() # indicator on
for i in range(1000):
clock.tick() # Track elapsed milliseconds between snapshots().
img = sensor.snapshot() # Take a picture and return the image.
if (oddframe):
oddframe = False
buffer2.replace(img)
face_cascade = image.HaarCascade("frontalface", stages=25)
num_faces = 0
while (True):
# Capture snapshot
img = sensor.snapshot()
# Find objects.
# 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.
objects = img.find_features(face_cascade, threshold=0.60, scale_factor=1.15)
if num_faces == len(objects):
continue
else:
num_faces = len(objects)
# Send full image over USB
for r in objects:
led.on()
tmp_fb = sensor.alloc_extra_fb(r[2], r[3], sensor.RGB565)
send_img = img.copy(r,copy_to_fb=tmp_fb).compress()
usb.send(b"IMGS")
usb.send(ustruct.pack("<L", send_img.size()))
usb.send(send_img)
usb.send(b"IMGE")
sensor.dealloc_extra_fb()
img.draw_rectangle(r)
led.off()
clock = time.clock() # Create a clock object to track the FPS.
# windowing
window_width = 320 # max 320
window_height = 150 # max 240
roi = ((sensor.width() / 2) - (window_width / 2),
(sensor.height() / 2) - (window_height / 2), window_width,
window_height)
sensor.set_windowing((int(roi[0]), int(roi[1]), int(roi[2]), int(roi[3])))
# positional settings
sensor.set_hmirror(True)
sensor.set_vflip(False)
# variables
extra_fb = sensor.alloc_extra_fb(window_width, window_height, sensor.GRAYSCALE)
uart = pyb.UART(3, 9600, timeout_char=1000)
red_led = pyb.LED(1)
green_led = pyb.LED(2)
blue_led = pyb.LED(3)
ir_leds = pyb.LED(4)
trigger_threshold = 5
# debug
show_debug = True
debug_color = (0, 0, 0)
min_mo_life_to_show = 5
debug_detected_duration = 50
debug_detected_counter = debug_detected_duration
def face_recog(pic_name, vi_ip):
print("~~~~~~~~~~~~~~~~FACE_RECOG~~~~~~~~~~~~~~~~~~~~~~")
gc.collect()
snap_img = image.Image(pic_name, copy_to_fb=True).mask_ellipse()
d0 = snap_img.find_lbp((0, 0, snap_img.width(), snap_img.height()))
pyb.LED(2).on()
pyb.LED(3).on()
name_lbp_list = []
uos.chdir("/CamFaces")
for filename in uos.listdir("/CamFaces"):
if filename.endswith(".pgm"):
try:
img = None
img = image.Image(filename, copy_to_fb=True).mask_ellipse()
sensor.alloc_extra_fb(img.width(), img.height(),
sensor.GRAYSCALE)
d1 = img.find_lbp((0, 0, img.width(), img.height()))
dist = image.match_descriptor(d0, d1, 50)
sensor.dealloc_extra_fb()
pname = filename
und_loc = pname.index('_')
pname = pname[0:(und_loc)]
name_lbp_list.append(pname)
name_lbp_list.append(dist)
continue
except Exception as e:
print(e)
print("error producing LBP value")
else:
print("file found that is not of type pgm")
print(name_lbp_list)
gc.collect()
end = 0
name_avg = []
i = 0
start = 0
while i < len(name_lbp_list):
if ((i + 2) < len(name_lbp_list)) and (name_lbp_list[i] !=
name_lbp_list[i + 2]):
end = i + 2
face = []
face = name_lbp_list[start:end]
print(face)
j = 1
sum_lbp = 0
while j < len(face):
sum_lbp += face[j]
j += 2
name_avg.append(face[0])
name_avg.append(sum_lbp / (len(face) / 2))
start = i + 2
i += 2
face = []
face = name_lbp_list[(end):(len(name_lbp_list))]
print(face)
gc.collect()
j = 1
sum_lbp = 0
while j < len(face):
sum_lbp += face[j]
j += 2
name_avg.append(face[0])
name_avg.append(sum_lbp / (len(face) / 2))
print(name_avg)
lbps = []
k = 1
while k < len(name_avg):
lbps.append(name_avg[k])
k += 2
print(lbps)
gc.collect()
min_lbp = min(lbps)
print(min_lbp)
ind = lbps.index(min(lbps))
ind += 1
found_person = name_avg[2 * ind - 2]
id_name = "The person you are looking at is: " + found_person
print(id_name)
uos.remove("/snapshot-person.pgm")
pyb.LED(2).off()
pyb.LED(3).off()
chost = vi_ip
cport = 8080
client = usocket.socket(usocket.AF_INET, usocket.SOCK_STREAM)
client.connect((chost, cport))
print("connected to visually impaired user's smartphone")
to_send = id_name + "\n"
client.send(to_send.encode())
print("sent name to phone")
client.close()
gc.collect()
return
# Lens Correction
import sensor, image, time
sensor.reset()
sensor.set_framesize(sensor.VGA)
sensor.set_pixformat(sensor.RGB565)
sensor.set_windowing((160, 120, 320, 240))
sensor.skip_frames(time=2000)
data_fb = sensor.alloc_extra_fb(320, 240, sensor.RGB565)
clock = time.clock()
count = 0
remap = False
while (True):
clock.tick()
# test without remap, with remap QVGA and with remap QQVGA
img = sensor.snapshot()
if remap:
img.remap(data_fb, right=False, upside_down=False)
count += 1
if count == 100:
print("remap QVGA GRAYSCALE")
remap = True
sensor.set_framesize(sensor.VGA)
sensor.set_pixformat(sensor.GRAYSCALE)
sensor.set_windowing((160, 120, 320, 240))
elif count == 200:
sensor.snapshot()
#get the gains and exposure
gain_db = sensor.get_gain_db()
exposure_us = sensor.get_exposure_us()
print("exposure is " + str(exposure_us))
rgb_gain_db = sensor.get_rgb_gain_db()
# deactivate the auto settings, use the given gains and exposure and wait for a bit
sensor.set_auto_gain(False, gain_db)
sensor.set_auto_exposure(False, exposure_us)
sensor.set_auto_whitebal(False, rgb_gain_db)
sensor.skip_frames(time=2000)
# allocate extra buffers to store the ref image and to
data_fb = sensor.alloc_extra_fb(img_width, img_height, sensor.RGB565)
ref_image = sensor.alloc_extra_fb(img_width, img_height, sensor.RGB565)
# save the ref image used for the diff function
print("About to save background image...")
img = sensor.snapshot()
img.remap(data_fb, right=True)
ref_image.replace(img)
print("Saved background image - Now frame differencing!")
# now add an additional part that will convey the mask info
sensor.set_windowing((int(
(sensor.width() - img_width) / 2), int((sensor.height() - img_height) / 2),
img_width, img_height + mask_height))
time.sleep(500)
clock = time.clock()
sensor.reset() # Initialize the camera sensor.
sensor.set_pixformat(sensor.GRAYSCALE) # or sensor.GRAYSCALE RGB565
sensor.set_framesize(sensor.QVGA) # or sensor.QQVGA (or others)
sensor.set_windowing(
(width_frame,
height_frame)) # look at center 240x240 pixels of the VGA resolution.
sensor.skip_frames(time=200) # Let new settings take affect.
sensor.set_auto_whitebal(False) # Turn off white balance.
sensor.set_auto_gain(False)
clock = time.clock() # Tracks FPS.
#extra_fb = sensor.alloc_extra_fb(sensor.width(), sensor.height(), sensor.GRAYSCALE)
extra_fb = sensor.alloc_extra_fb(width_frame, height_frame, sensor.GRAYSCALE)
print("About to save background image...")
sensor.skip_frames(time=200) # Give the user time to get ready.
extra_fb.replace(sensor.snapshot())
print("Saved background image - Now frame differencing!")
while (True):
clock.tick() # Track elapsed milliseconds between snapshots().
img = sensor.snapshot() # Take a picture and return the image.
# Replace the image with the "abs(NEW-OLD)" frame difference.
img = img.difference(extra_fb)
edges = img.find_edges(image.EDGE_CANNY, threshold=(10, 80))
blobs = img.find_blobs([(30, 255)],
def face_recog(pic_name, vi_ip):
print("~~~~~~~~~~~~~~~~FACE_RECOG~~~~~~~~~~~~~~~~~~~~~~")
gc.collect() #garbage collection
#find LBP value for snapshot saved in face_detect
snap_img = image.Image(pic_name, copy_to_fb=True).mask_ellipse()
d0 = snap_img.find_lbp((0, 0, snap_img.width(), snap_img.height()))
# turn on lights signaling facial recognition calculations starting
pyb.LED(2).on()
pyb.LED(3).on()
#find LBP values for each image received in server_recv
name_lbp_list = []
uos.chdir(
"/CamFaces"
) # change directory to where all the images from server_recv are stored
for filename in uos.listdir("/CamFaces"):
if filename.endswith(".pgm"):
try:
img = None
img = image.Image(filename, copy_to_fb=True).mask_ellipse()
sensor.alloc_extra_fb(
img.width(), img.height(),
sensor.GRAYSCALE) #allocate more space for images
d1 = img.find_lbp((0, 0, img.width(), img.height()))
dist = image.match_descriptor(
d0, d1,
50) #set threshold lower than 70 to tighten matching algo
sensor.dealloc_extra_fb()
# extracting the person's name from the file name
pname = filename
und_loc = pname.index('_')
pname = pname[0:(und_loc)]
# add the person's name and LBP value for the image to the list
name_lbp_list.append(word)
name_lbp_list.append(dist)
continue
except Exception as e:
print(e)
print("error producing LBP value")
else:
print("file found that is not of type pgm")
print(name_lbp_list)
gc.collect() #garbage collection
# finding average LBP values for each name
end = 0
name_avg = []
i = 0
start = 0
while i < len(name_lbp_list): # for names 1 thru n-1
if ((i + 2) < len(name_lbp_list)) and (name_lbp_list[i] !=
name_lbp_list[i + 2]):
end = i + 2
face = []
face = name_lbp_list[start:end]
print(face)
j = 1
sum_lbp = 0
while j < len(face):
sum_lbp += face[j]
j += 2
name_avg.append(face[0])
name_avg.append(sum_lbp / (len(face) / 2))
start = i + 2
i += 2
face = []
face = name_lbp_list[(end):(len(name_lbp_list))]
print(face)
gc.collect() #garbage collection
# special case: find average LBP value for last name in list (name n)
j = 1
sum_lbp = 0
while j < len(face):
sum_lbp += face[j]
j += 2
name_avg.append(face[0])
name_avg.append(sum_lbp / (len(face) / 2))
print(name_avg)
lbps = []
k = 1
while k < len(name_avg):
lbps.append(name_avg[k])
k += 2
print(lbps)
gc.collect() #garbage collection
# find minimum average LBP and associated person name
min_lbp = min(lbps)
print(min_lbp)
ind = lbps.index(min(lbps))
ind += 1
found_person = name_avg[2 * ind - 2]
id_name = "The person you are looking at is: " + found_person
print(id_name)
#delete snapshot of person
uos.remove("/snapshot-person.pgm")
# turn off lights signaling facial recognition calculations done
pyb.LED(2).off()
pyb.LED(3).off()
#TCP client socket to send name of the person recognized to the visually impaired user's smartphone
chost = vi_ip
cport = 8080
client = usocket.socket(
usocket.AF_INET,
usocket.SOCK_STREAM) #TCP client socket with IPv4 addressing
client.connect((chost, cport))
print("connected to visually impaired user's smartphone")
to_send = id_name + "\n"
client.send(to_send.encode())
print("sent name to phone")
client.close() #client closed
gc.collect() #garbage collection
return
