Merge pull request #111 from kwagyeman/master

Update example scripts.
This commit is contained in:
Ibrahim Abd Elkader 2016-04-02 19:08:04 +02:00
commit b8e338138c
32 changed files with 372 additions and 308 deletions

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@ -7,7 +7,7 @@ import sensor, image, time
sensor.reset() # Initialize the camera sensor.
sensor.set_pixformat(sensor.RGB565) # or sensor.GRAYSCALE
sensor.set_framesize(sensor.QVGA) # or sensor.QQVGA (or others)
sensor.skip_frames() # Let new settings take affect.
sensor.skip_frames(10) # Let new settings take affect.
clock = time.clock() # Tracks FPS.
while(True):

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@ -11,11 +11,23 @@ usb = pyb.USB_VCP() # This is a serial port object that allows you to
# communciate with your computer. While it is not open the code below runs.
while(not usb.isconnected()):
led.on()
time.sleep(150)
led.off()
time.sleep(100)
led.on()
time.sleep(150)
led.off()
time.sleep(600)
led.on()
time.sleep(150)
led.off()
time.sleep(100)
led.on()
time.sleep(150)
led.off()
time.sleep(600)
led = pyb.LED(2) # Switch to using the green LED.
while(usb.isconnected()):
led.on()
time.sleep(150)
led.off()
time.sleep(100)
led.on()
time.sleep(150)
led.off()
time.sleep(600)

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@ -1,32 +0,0 @@
import sensor, time
# Reset sensor
sensor.reset()
# Set sensor settings
sensor.set_brightness(0)
sensor.set_saturation(0)
sensor.set_gainceiling(16)
sensor.set_contrast(1)
# Set sensor to QQVGA/RGB565
sensor.set_framesize(sensor.QVGA)
sensor.set_pixformat(sensor.RGB565)
sensor.set_colorbar(True)
# Skip a few frames to allow the sensor settle down
# Note: This takes more time when exec from the IDE.
for i in range(0, 30):
sensor.snapshot()
clock = time.clock()
# Take snapshot
img = sensor.snapshot()
# Compress Image
clock.tick()
img = img.compress(50)
print(clock.avg(),"\n")
with open("/test.jpeg", "w") as f:
f.write(img)

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@ -1,34 +0,0 @@
import sensor, pyb, time
# Reset sensor
sensor.reset()
# Set sensor settings
sensor.set_brightness(0)
sensor.set_saturation(0)
sensor.set_gainceiling(16)
sensor.set_contrast(1)
sensor.set_framesize(sensor.QVGA)
# Enable JPEG and set quality
sensor.set_pixformat(sensor.JPEG)
sensor.set_quality(98)
# Red LED
led = pyb.LED(1)
# Skip a few frames to allow the sensor settle down
# Note: This takes more time when exec from the IDE.
for i in range(0, 30):
sensor.snapshot()
# Turn on red LED and wait for a second
led.on()
time.sleep(1000)
# Write JPEG image to file
with open("/test.jpeg", "w") as f:
f.write(sensor.snapshot())
led.off()
print("Reset the camera to see the saved image.")

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@ -1,33 +0,0 @@
import sensor, pyb, time
# Reset sensor
sensor.reset()
# Set sensor settings
sensor.set_brightness(0)
sensor.set_saturation(0)
sensor.set_gainceiling(16)
sensor.set_contrast(1)
sensor.set_framesize(sensor.QVGA)
# Set sensor to RGB565
sensor.set_pixformat(sensor.RGB565)
# Red LED
led = pyb.LED(1)
# Skip a few frames to allow the sensor settle down
# Note: This takes more time when exec from the IDE.
for i in range(0, 10):
sensor.snapshot()
# Turn on red LED and wait for a second
led.on()
time.sleep(1000)
# Write image to file
img = sensor.snapshot()
img.save("/test.ppm")
led.off()
print("Reset the camera to see the saved image.")

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@ -1,6 +1,6 @@
# Save Image Example
# Snapshot Example
#
# Note: You will need an SD card to run this demo.
# Note: You will need an SD card to run this example.
#
# You can use your OpenMV Cam to save image files.
@ -12,7 +12,7 @@ BLUE_LED_PIN = 3
sensor.reset() # Initialize the camera sensor.
sensor.set_pixformat(sensor.RGB565) # or sensor.GRAYSCALE
sensor.set_framesize(sensor.QVGA) # or sensor.QQVGA (or others)
sensor.skip_frames() # Let new settings take affect.
sensor.skip_frames(10) # Let new settings take affect.
pyb.LED(RED_LED_PIN).on()
sensor.skip_frames(30) # Give the user time to get ready.
@ -21,7 +21,7 @@ pyb.LED(RED_LED_PIN).off()
pyb.LED(BLUE_LED_PIN).on()
print("You're on camera!")
sensor.snapshot().save("demo.jpg") # or "demo.bmp" (or others)
sensor.snapshot().save("example.jpg") # or "example.bmp" (or others)
pyb.LED(BLUE_LED_PIN).off()
print("Done! Reset the camera to see the saved recording.")
print("Done! Reset the camera to see the saved image.")

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@ -0,0 +1,51 @@
# Snapshot on Face Detection Example
#
# Note: You will need an SD card to run this example.
#
# This example demonstrates using face tracking on your OpenMV Cam to take a
# picture.
import sensor, image, pyb
RED_LED_PIN = 1
BLUE_LED_PIN = 3
sensor.reset() # Initialize the camera sensor.
sensor.set_pixformat(sensor.GRAYSCALE)
sensor.set_framesize(sensor.HQVGA) # or sensor.QQVGA (or others)
sensor.skip_frames(10) # Let new settings take affect.
# Load up a face detection HaarCascade. This is object that your OpenMV Cam
# can use to detect faces using the find_features() method below. Your OpenMV
# Cam has fontalface HaarCascade built-in. By default, all the stages of the
# HaarCascade are loaded. However, You can adjust the number of stages to speed
# up processing at the expense of accuracy. The frontalface HaarCascade has 25
# stages.
face_cascade = image.HaarCascade("frontalface", stages=25)
while(True):
pyb.LED(RED_LED_PIN).on()
print("About to start detecting faces...")
sensor.skip_frames(60) # Give the user time to get ready.
pyb.LED(RED_LED_PIN).off()
print("Now detecting faces!")
pyb.LED(BLUE_LED_PIN).on()
diff = 10 # We'll say we detected a face after 10 frames.
while(diff):
img = sensor.snapshot()
# Threshold can be between 0.0 and 1.0. A higher threshold results in a
# higher detection rate with more false positives. The scale value
# controls the matching scale allowing you to detect smaller faces.
faces = img.find_features(face_cascade, threshold=0.5, scale=1.5)
if faces:
diff -= 1
for r in faces:
img.draw_rectangle(r)
pyb.LED(BLUE_LED_PIN).off()
print("Face detected! Saving image...")
sensor.snapshot().save("snapshot-%d.jpg" % pyb.rng()) # Save Pic.

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@ -0,0 +1,43 @@
# Snapshot on Movement Example
#
# Note: You will need an SD card to run this example.
#
# This example demonstrates using frame differencing with your OpenMV Cam to do
# motion detection. After motion is detected your OpenMV Cam will take picture.
import sensor, image, pyb, os
RED_LED_PIN = 1
BLUE_LED_PIN = 3
sensor.reset() # Initialize the camera sensor.
sensor.set_pixformat(sensor.RGB565) # or sensor.GRAYSCALE
sensor.set_framesize(sensor.QVGA) # or sensor.QQVGA (or others)
sensor.skip_frames(10) # Let new settings take affect.
sensor.set_whitebal(False) # Turn off white balance.
if not "temp" in os.listdir(): os.mkdir("temp") # Make a temp directory
while(True):
pyb.LED(RED_LED_PIN).on()
print("About to save background image...")
sensor.skip_frames(60) # Give the user time to get ready.
pyb.LED(RED_LED_PIN).off()
sensor.snapshot().save("temp/bg.bmp")
print("Saved background image - Now detecting motion!")
pyb.LED(BLUE_LED_PIN).on()
diff = 10 # We'll say we detected motion after 10 frames of motion.
while(diff):
img = sensor.snapshot()
img.difference("temp/bg.bmp")
img.binary([(20, 100, -128, 127, -128, 127)])
sum = img.pixels()
if sum > 100: # Over 100 pixels need to change to detect motion.
diff -= 1
pyb.LED(BLUE_LED_PIN).off()
print("Movement detected! Saving image...")
sensor.snapshot().save("temp/snapshot-%d.jpg" % pyb.rng()) # Save Pic.

View File

@ -14,7 +14,7 @@ BLUE_LED_PIN = 3
sensor.reset() # Initialize the camera sensor.
sensor.set_pixformat(sensor.RGB565) # or sensor.GRAYSCALE
sensor.set_framesize(sensor.QQVGA) # or sensor.QVGA (or others)
sensor.skip_frames() # Let new settings take affect.
sensor.skip_frames(10) # Let new settings take affect.
clock = time.clock() # Tracks FPS.
pyb.LED(RED_LED_PIN).on()
@ -23,15 +23,15 @@ sensor.skip_frames(30) # Give the user time to get ready.
pyb.LED(RED_LED_PIN).off()
pyb.LED(BLUE_LED_PIN).on()
gif = gif.Gif("demo.gif", loop=True)
g = gif.Gif("example.gif", loop=True)
print("You're on camera!")
for i in range(100):
clock.tick()
# clock.avg() returns the milliseconds between frames - gif delay is in
gif.add_frame(sensor.snapshot(), delay=int(clock.avg()/10)) # centiseconds.
g.add_frame(sensor.snapshot(), delay=int(clock.avg()/10)) # centiseconds.
print(clock.fps())
gif.close()
g.close()
pyb.LED(BLUE_LED_PIN).off()
print("Done! Reset the camera to see the saved recording.")

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@ -0,0 +1,65 @@
# GIF Video Recording on Face Detection Example
#
# Note: You will need an SD card to run this example.
#
# You can use your OpenMV Cam to record gif files. You can either feed the
# recorder object RGB565 frames or Grayscale frames. Use photo editing software
# like GIMP to compress and optimize the Gif before uploading it to the web.
#
# This example demonstrates using face tracking on your OpenMV Cam to take a
# gif.
import sensor, image, time, gif, pyb
RED_LED_PIN = 1
BLUE_LED_PIN = 3
sensor.reset() # Initialize the camera sensor.
sensor.set_pixformat(sensor.GRAYSCALE) # or sensor.
sensor.set_framesize(sensor.QQVGA) # or sensor.HQVGA (or others)
sensor.skip_frames(10) # Let new settings take affect.
# Load up a face detection HaarCascade. This is object that your OpenMV Cam
# can use to detect faces using the find_features() method below. Your OpenMV
# Cam has fontalface HaarCascade built-in. By default, all the stages of the
# HaarCascade are loaded. However, You can adjust the number of stages to speed
# up processing at the expense of accuracy. The frontalface HaarCascade has 25
# stages.
face_cascade = image.HaarCascade("frontalface", stages=25)
while(True):
pyb.LED(RED_LED_PIN).on()
print("About to start detecting faces...")
sensor.skip_frames(60) # Give the user time to get ready.
pyb.LED(RED_LED_PIN).off()
print("Now detecting faces!")
pyb.LED(BLUE_LED_PIN).on()
diff = 10 # We'll say we detected a face after 10 frames.
while(diff):
img = sensor.snapshot()
# Threshold can be between 0.0 and 1.0. A higher threshold results in a
# higher detection rate with more false positives. The scale value
# controls the matching scale allowing you to detect smaller faces.
faces = img.find_features(face_cascade, threshold=0.5, scale=1.5)
if faces:
diff -= 1
for r in faces:
img.draw_rectangle(r)
g = gif.Gif("example-%d.gif" % pyb.rng(), loop=True)
clock = time.clock() # Tracks FPS.
print("You're on camera!")
for i in range(100):
clock.tick()
# clock.avg() returns the milliseconds between frames - gif delay is in
g.add_frame(sensor.snapshot(), delay=int(clock.avg()/10)) # centiseconds.
print(clock.fps())
g.close()
pyb.LED(BLUE_LED_PIN).off()
print("Restarting...")

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@ -0,0 +1,57 @@
# GIF Video Recording on Movement Example
#
# Note: You will need an SD card to run this example.
#
# You can use your OpenMV Cam to record gif files. You can either feed the
# recorder object RGB565 frames or Grayscale frames. Use photo editing software
# like GIMP to compress and optimize the Gif before uploading it to the web.
#
# This example demonstrates using frame differencing with your OpenMV Cam to do
# motion detection. After motion is detected your OpenMV Cam will take video.
import sensor, image, time, gif, pyb, os
RED_LED_PIN = 1
BLUE_LED_PIN = 3
sensor.reset() # Initialize the camera sensor.
sensor.set_pixformat(sensor.RGB565) # or sensor.GRAYSCALE
sensor.set_framesize(sensor.QQVGA) # or sensor.QVGA (or others)
sensor.skip_frames(10) # Let new settings take affect.
sensor.set_whitebal(False) # Turn off white balance.
if not "temp" in os.listdir(): os.mkdir("temp") # Make a temp directory
while(True):
pyb.LED(RED_LED_PIN).on()
print("About to save background image...")
sensor.skip_frames(60) # Give the user time to get ready.
pyb.LED(RED_LED_PIN).off()
sensor.snapshot().save("temp/bg.bmp")
print("Saved background image - Now detecting motion!")
pyb.LED(BLUE_LED_PIN).on()
diff = 10 # We'll say we detected motion after 10 frames of motion.
while(diff):
img = sensor.snapshot()
img.difference("temp/bg.bmp")
img.binary([(20, 100, -128, 127, -128, 127)])
sum = img.pixels()
if sum > 100: # Over 100 pixels need to change to detect motion.
diff -= 1
g = gif.Gif("example-%d.gif" % pyb.rng(), loop=True)
clock = time.clock() # Tracks FPS.
print("You're on camera!")
for i in range(100):
clock.tick()
# clock.avg() returns the milliseconds between frames - gif delay is in
g.add_frame(sensor.snapshot(), delay=int(clock.avg()/10)) # centiseconds.
print(clock.fps())
g.close()
pyb.LED(BLUE_LED_PIN).off()
print("Restarting...")

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@ -15,7 +15,7 @@ BLUE_LED_PIN = 3
sensor.reset() # Initialize the camera sensor.
sensor.set_pixformat(sensor.RGB565) # or sensor.GRAYSCALE
sensor.set_framesize(sensor.QVGA) # or sensor.QQVGA (or others)
sensor.skip_frames() # Let new settings take affect.
sensor.skip_frames(10) # Let new settings take affect.
clock = time.clock() # Tracks FPS.
pyb.LED(RED_LED_PIN).on()
@ -24,14 +24,14 @@ sensor.skip_frames(30) # Give the user time to get ready.
pyb.LED(RED_LED_PIN).off()
pyb.LED(BLUE_LED_PIN).on()
mjpeg = mjpeg.Mjpeg("demo.mjpeg")
m = mjpeg.Mjpeg("example.mjpeg")
print("You're on camera!")
for i in range(200):
clock.tick()
mjpeg.add_frame(sensor.snapshot())
m.add_frame(sensor.snapshot())
print(clock.fps())
mjpeg.close(clock.fps())
m.close(clock.fps())
pyb.LED(BLUE_LED_PIN).off()
print("Done! Reset the camera to see the saved recording.")

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@ -0,0 +1,65 @@
# MJPEG Video Recording on Face Detection Example
#
# Note: You will need an SD card to run this example.
#
# You can use your OpenMV Cam to record mjpeg files. You can either feed the
# recorder object JPEG frames or RGB565/Grayscale frames. Once you've finished
# recording a Mjpeg file you can use VLC to play it. If you are on Ubuntu then
# the built-in video player will work too.
#
# This example demonstrates using face tracking on your OpenMV Cam to take a
# mjpeg.
import sensor, image, time, mjpeg, pyb
RED_LED_PIN = 1
BLUE_LED_PIN = 3
sensor.reset() # Initialize the camera sensor.
sensor.set_pixformat(sensor.GRAYSCALE) # or sensor.
sensor.set_framesize(sensor.QQVGA) # or sensor.HQVGA (or others)
sensor.skip_frames(10) # Let new settings take affect.
# Load up a face detection HaarCascade. This is object that your OpenMV Cam
# can use to detect faces using the find_features() method below. Your OpenMV
# Cam has fontalface HaarCascade built-in. By default, all the stages of the
# HaarCascade are loaded. However, You can adjust the number of stages to speed
# up processing at the expense of accuracy. The frontalface HaarCascade has 25
# stages.
face_cascade = image.HaarCascade("frontalface", stages=25)
while(True):
pyb.LED(RED_LED_PIN).on()
print("About to start detecting faces...")
sensor.skip_frames(60) # Give the user time to get ready.
pyb.LED(RED_LED_PIN).off()
print("Now detecting faces!")
pyb.LED(BLUE_LED_PIN).on()
diff = 10 # We'll say we detected a face after 10 frames.
while(diff):
img = sensor.snapshot()
# Threshold can be between 0.0 and 1.0. A higher threshold results in a
# higher detection rate with more false positives. The scale value
# controls the matching scale allowing you to detect smaller faces.
faces = img.find_features(face_cascade, threshold=0.5, scale=1.5)
if faces:
diff -= 1
for r in faces:
img.draw_rectangle(r)
m = mjpeg.Mjpeg("example-%d.mjpeg" % pyb.rng())
clock = time.clock() # Tracks FPS.
print("You're on camera!")
for i in range(200):
clock.tick()
m.add_frame(sensor.snapshot())
print(clock.fps())
m.close(clock.fps())
pyb.LED(BLUE_LED_PIN).off()
print("Restarting...")

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@ -0,0 +1,57 @@
# MJPEG Video Recording on Movement Example
#
# Note: You will need an SD card to run this example.
#
# You can use your OpenMV Cam to record mjpeg files. You can either feed the
# recorder object JPEG frames or RGB565/Grayscale frames. Once you've finished
# recording a Mjpeg file you can use VLC to play it. If you are on Ubuntu then
# the built-in video player will work too.
#
# This example demonstrates using frame differencing with your OpenMV Cam to do
# motion detection. After motion is detected your OpenMV Cam will take video.
import sensor, image, time, mjpeg, pyb, os
RED_LED_PIN = 1
BLUE_LED_PIN = 3
sensor.reset() # Initialize the camera sensor.
sensor.set_pixformat(sensor.RGB565) # or sensor.GRAYSCALE
sensor.set_framesize(sensor.QVGA) # or sensor.QQVGA (or others)
sensor.skip_frames(10) # Let new settings take affect.
sensor.set_whitebal(False) # Turn off white balance.
if not "temp" in os.listdir(): os.mkdir("temp") # Make a temp directory
while(True):
pyb.LED(RED_LED_PIN).on()
print("About to save background image...")
sensor.skip_frames(60) # Give the user time to get ready.
pyb.LED(RED_LED_PIN).off()
sensor.snapshot().save("temp/bg.bmp")
print("Saved background image - Now detecting motion!")
pyb.LED(BLUE_LED_PIN).on()
diff = 10 # We'll say we detected motion after 10 frames of motion.
while(diff):
img = sensor.snapshot()
img.difference("temp/bg.bmp")
img.binary([(20, 100, -128, 127, -128, 127)])
sum = img.pixels()
if sum > 100: # Over 100 pixels need to change to detect motion.
diff -= 1
m = mjpeg.Mjpeg("example-%d.mjpeg" % pyb.rng())
clock = time.clock() # Tracks FPS.
print("You're on camera!")
for i in range(200):
clock.tick()
m.add_frame(sensor.snapshot())
print(clock.fps())
m.close(clock.fps())
pyb.LED(BLUE_LED_PIN).off()
print("Restarting...")

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@ -1,146 +0,0 @@
# Copy this module to storage and import it if you want
# to use it in your own scripts. See example usage below.
import ustruct as struct
class AVI:
def __init__(self, path, w, h, codec="MJPG"):
self.w = w
self.h = h
self.codec = codec
self.size = 0
self.frames = 0
self.fp = open(path, "w")
self.fp.seek(224) #skip headers
def avi_hdr(self):
hdr = struct.pack("I", int(1000/self.fps)) # Time delay between frames
hdr += struct.pack("I", 0) # Data rate of AVI data
hdr += struct.pack("I", 1) # Size of single unit of padding
hdr += struct.pack("I", 0) # Flags
hdr += struct.pack("I", self.frames)# Number of video frame stored
hdr += struct.pack("I", 0) # Number of intial frames
hdr += struct.pack("I", 1) # Number of data streams in chunk
hdr += struct.pack("I", 0) # Minimum playback buffer size
hdr += struct.pack("I", self.w) # Width of video frame in pixels
hdr += struct.pack("I", self.h) # Height of video frame in pixels
hdr += struct.pack("I", 1) # Time scale
hdr += struct.pack("I", self.fps) # Data rate of playback
hdr += struct.pack("I", 0) # Starting time of AVI data
hdr += struct.pack("I", 0) # Size of AVI data chunk
return hdr;
def str_hdr(self):
hdr = struct.pack("4s", "vids") # Stream type
hdr += struct.pack("4s", self.codec)# Stream codec
hdr += struct.pack("I", 0) # Flags
hdr += struct.pack("I", 0) # Priority
hdr += struct.pack("I", 0) # Number of first frame
hdr += struct.pack("I", 1) # Time scale
hdr += struct.pack("I", self.fps) # Data rate of playback
hdr += struct.pack("I", 0) # Starting time of AVI data
hdr += struct.pack("I", 0) # Data length
hdr += struct.pack("I", 0) # Buffer size
hdr += struct.pack("I", 0) # Sample quailty factor
hdr += struct.pack("I", 0) # Size of the sample in bytes
hdr += struct.pack("II",0,0) # Rect
return hdr;
def str_fmt(self):
#BITMAPINFOHEADER
hdr = struct.pack("I", 40) # Size in bytes
hdr += struct.pack("I", self.w) # Width
hdr += struct.pack("I", self.h) # Height
hdr += struct.pack("H", 1) # Planes
hdr += struct.pack("H", 16) # Bits per pixel
hdr += struct.pack("4s", self.codec) # This should be BI_JPEG, but ffmpeg writes "MJPG"
hdr += struct.pack("I", 0) # Image size (which one?)
hdr += struct.pack("I", 0) # X pixels-per-meter
hdr += struct.pack("I", 0) # Y pixels-per-meter
hdr += struct.pack("I", 0) # color indexes in the color table
hdr += struct.pack("I", 0) # required color indexes in the color table
return hdr;
def new_chunk(self, c_id, c_data):
return c_id +\
struct.pack("I", len(c_data)) +\
c_data
def new_list(self, l_id, l_4cc, l_size, l_data):
return l_id +\
struct.pack("I", l_size+len(l_data)+4) +\
struct.pack("4s", l_4cc) +\
l_data
def add_frame(self, img):
self.frames +=1
self.size += img.size()
self.fp.write(struct.pack("4sI", "00dc", img.size()))
self.fp.write(img)
def flush(self, fps):
self.fps = fps
self.fp.seek(0)
self.fp.write(
self.new_list(b"RIFF", b"AVI ", self.size,
self.new_list(b"LIST", b"hdrl", 0,
self.new_chunk(b"avih", self.avi_hdr())
+ self.new_list(b"LIST", b"strl", 0,
self.new_chunk(b"strh", self.str_hdr())
+ self.new_chunk(b"strf", self.str_fmt())
)
+ self.new_list(b"LIST", b"movi", self.size, b"")
)
)
)
self.fp.close()
if __name__ == "__main__":
import sensor, time, pyb
#from avi import AVI
# Recording length in seconds
REC_LENGTH = 10
# Reset sensor
sensor.reset()
# Set sensor settings
sensor.set_contrast(2)
sensor.set_framesize(sensor.VGA)
# Enable JPEG and set quality
sensor.set_pixformat(sensor.JPEG)
sensor.set_quality(95)
# Skip a few frames to allow the sensor settle down
# Note: This takes more time when exec from the IDE.
for i in range(0, 30):
sensor.snapshot()
# Create red LED object
led = pyb.LED(1)
# Create video file
video = AVI("%d.mjpeg"%pyb.rng(), 640, 480)
# Recording clocks
led.on()
clock = time.clock()
start = time.ticks()
# Start recording
while ((time.ticks()-start) < (REC_LENGTH*1000)):
clock.tick()
img = sensor.snapshot()
video.add_frame(img)
led.off()
# Flush video file
video.flush(int(clock.fps()))
# Done, flash blue LED
led = pyb.LED(3)
while (True):
led.on()
time.sleep(500)
led.off()
time.sleep(500)

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@ -1,41 +0,0 @@
# Motion Detection Example:
#
# This example demonstrates using frame differencing with your OpenMV Cam to do
# motion detection. After motion is detected your OpenMV Cam will take picture.
import os, pyb, sensor, image, time
if not "temp" in os.listdir(): os.mkdir("temp") # Make a temp directory
sensor.reset()
sensor.set_framesize(sensor.QVGA)
while(True):
sensor.set_pixformat(sensor.GRAYSCALE) # Grayscale is much faster than RGB.
# Warm up the cam
for i in range(10):
sensor.snapshot()
for i in [5, 4, 3, 2, 1]:
print("Saving background in... %d" % i)
time.sleep(1000)
print("Saving background...")
sensor.snapshot().save("temp/bg.bmp")
diff = 30 # wait 30 snapshot before taking picture
while(diff):
img = sensor.snapshot()
img.difference("temp/bg.bmp")
img.binary([(32, 255)])
sum, x, y = img.centroid()
if sum > 100: # 100 pixels detected
img.draw_cross(x, y, color = 127)
diff -= 1
sensor.set_pixformat(sensor.RGB565)
# Warm up the cam
for i in range(10):
sensor.snapshot()
sensor.snapshot().save("temp/movement-%d" % pyb.rng()) # Save movement