# Note: While this should work on any board, the board should have an SDRAM to be of any use.
import sensor, image, time
# Number of frames to pre-allocate and record
N_FRAMES = 500
sensor.reset()
sensor.set_pixformat(sensor.GRAYSCALE)
sensor.set_framesize(sensor.QVGA)
# This frame size must match the image size passed to ImageIO
sensor.set_windowing((120, 120))
sensor.skip_frames(time = 2000)
clock = time.clock()
# Write to memory stream
stream = image.ImageIO((120, 120, sensor.GRAYSCALE), N_FRAMES)
for i in range(0, N_FRAMES):
clock.tick()
stream.write(sensor.snapshot())
print(clock.fps())
while (True):
# Rewind stream and play back
stream.seek(0)
for i in range(0, N_FRAMES):
img = stream.read(copy_to_fb=True, pause=True))
# Do machine vision algorithms on the image here.
import sensor, image, time
# Number of frames to pre-allocate and record
N_FRAMES = 500
sensor.reset()
sensor.set_pixformat(sensor.GRAYSCALE)
sensor.set_framesize(sensor.QVGA)
# This frame size must match the image size passed to ImageIO
sensor.set_windowing((120, 120))
sensor.skip_frames(time=2000)
clock = time.clock()
# Write to memory stream
stream = image.ImageIO((120, 120, 2), N_FRAMES)
for i in range(0, N_FRAMES):
clock.tick()
stream.write(sensor.snapshot())
print(clock.fps())
while (True):
# Rewind stream and play back at 100FPS
stream.seek(0)
for i in range(0, N_FRAMES):
img = stream.read(copy_to_fb=True)
# Do machine vision algorithms on the image here.
time.sleep_ms(10)
# Multi Color Blob Tracking Example
#
# This example shows off multi color blob tracking using the OpenMV Cam.
import sensor, image, time, math
img_src = "cam"
# 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 = [(36, 77, 40, 79, 33, 81)]
# You may pass up to 16 thresholds above. However, it's not really possible to segment any
# scene with 16 thresholds before color thresholds start to overlap heavily.
if (img_src == "stream"):
img_reader = image.ImageIO("/marker_stream.bin", "r")
else:
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. Don't set "merge=True" becuase that will merge blobs which we don't want here.
blobs = []
# Note: While this should work on any board, the board should have an SDRAM to be of any use.
import sensor, image, time
# Number of frames to pre-allocate and record
N_FRAMES = 500
sensor.reset()
sensor.set_pixformat(sensor.RGB565)
sensor.set_framesize(sensor.QVGA)
# This frame size must match the image size passed to ImageIO
sensor.set_windowing((120, 120))
sensor.skip_frames(time = 2000)
clock = time.clock()
# Write to memory stream
stream = image.ImageIO((120, 120, sensor.RGB565), N_FRAMES)
for i in range(0, N_FRAMES):
clock.tick()
stream.write(sensor.snapshot())
print(clock.fps())
while (True):
# Rewind stream and play back
stream.seek(0)
for i in range(0, N_FRAMES):
img = stream.read(copy_to_fb=True, pause=True)
# Do machine vision algorithms on the image here.
import image, pyb, time
clock = time.clock()
img_reader = image.ImageIO("/test_stream.bin", "r")
while (True):
clock.tick()
img = img_reader.read(copy_to_fb=True, loop=True, pause=True)
img.draw_line((0, 0, img.width(), img.height()),
color=(255, 0, 0),
thickness=10)
img.draw_rectangle(104, 79, 119, 96)
time.sleep(1)
img_write.close
#sensor.set_saturation(1)
original_exposure = sensor.get_exposure_us()
sensor.set_auto_exposure(False, int(0.15 * original_exposure))
clock = time.clock()
uart = UART(1, 115200)
color = bytearray(3)
color[0] = 0x15
color[1] = 0xFF
color[2] = 0x00
img_writer = image.ImageIO("/test_stream.bin", "w")
start = pyb.millis()
while pyb.elapsed_millis(start) < record_time:
clock.tick()
img = sensor.snapshot().gamma_corr(gamma=1.4,
contrast=1.2,
brightness=-0.2)
pixie.setColor(color)
time.sleep(0.1)
#lidar_frame = lidar.readLidar()
## Send out our results.
# USE THIS EXAMPLE WITH A USD CARD!
#
# This example shows how to use the Image Reader object to replay snapshots of what your
# OpenMV Cam saw saved by the Image Writer object for testing machine vision algorithms.
# Altered to allow full speed reading from SD card for extraction of sequences to the network etc.
# Set the new pause parameter to false
import sensor, image, time
snapshot_source = False # Set to true once finished to pull data from sensor.
sensor.reset()
sensor.set_pixformat(sensor.RGB565)
sensor.set_framesize(sensor.QQVGA)
sensor.skip_frames(time = 2000)
clock = time.clock()
stream = None
if snapshot_source == False:
stream = image.ImageIO("/stream.bin", "r")
while(True):
clock.tick()
if snapshot_source:
img = sensor.snapshot()
else:
img = stream.read(copy_to_fb=True, loop=True, pause=True)
# Do machine vision algorithms on the image here.
print(clock.fps())