def unittest(data_path, temp_path):
    import sensor
    sensor.reset()
    sensor.set_framesize(sensor.QVGA)
    sensor.set_pixformat(sensor.GRAYSCALE)
    img = sensor.snapshot().clear()
    img.set_pixel(img.width()//2+50, 120, 255)
    img.set_pixel(img.width()//2-50, 120, 255)
    img.draw_line([img.width()//2-50, 50, img.width()//2+50, 50])
    img.draw_rectangle([img.width()//2-25, img.height()//2-25, 50, 50])
    img.draw_circle(img.width()//2, img.height()//2, 40)
    img.draw_string(11, 10, "HelloWorld!")
    img.draw_cross(img.width()//2, img.height()//2)
    sensor.flush()
    img.difference(data_path+"/drawing.pgm")
    stats = img.get_statistics()
    return (stats.max() == 0) and (stats.min() == 0)

def test_color_bars():

    sensor.reset()
    # Set sensor settings
    sensor.set_brightness(0)
    sensor.set_saturation(0)
    sensor.set_gainceiling(8)
    sensor.set_contrast(2)

    # Set sensor pixel format
    sensor.set_framesize(sensor.QVGA)
    sensor.set_pixformat(sensor.RGB565)

    # Enable colorbar test mode
    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, 100):
        image = sensor.snapshot()

    # Color bars thresholds
    t = [lambda r, g, b: r < 50  and g < 50  and b < 50,   # Black
         lambda r, g, b: r < 50  and g < 50  and b > 200,  # Blue
         lambda r, g, b: r > 200 and g < 50  and b < 50,   # Red
         lambda r, g, b: r > 200 and g < 50  and b > 200,  # Purple
         lambda r, g, b: r < 50  and g > 200 and b < 50,   # Green
         lambda r, g, b: r < 50  and g > 200 and b > 200,  # Aqua
         lambda r, g, b: r > 200 and g > 200 and b < 50,   # Yellow
         lambda r, g, b: r > 200 and g > 200 and b > 200]  # White

    # 320x240 image with 8 color bars each one is approx 40 pixels.
    # we start from the center of the frame buffer, and average the
    # values of 10 sample pixels from the center of each color bar.
    for i in range(0, 8):
        avg = (0, 0, 0)
        idx = 40*i+20 # center of colorbars
        for off in range(0, 10): # avg 10 pixels
            rgb = image.get_pixel(idx+off, 120)
            avg = tuple(map(sum, zip(avg, rgb)))

        if not t[i](avg[0]/10, avg[1]/10, avg[2]/10):
            raise Exception("COLOR BARS TEST FAILED. "
            "BAR#(%d): RGB(%d,%d,%d)"%(i+1, avg[0]/10, avg[1]/10, avg[2]/10))

    print("COLOR BARS TEST PASSED...")

# Find Rects Example
#
# This example shows off how to find rectangles in the image using the quad threshold
# detection code from our April Tags code. The quad threshold detection algorithm
# detects rectangles in an extremely robust way and is much better than Hough
# Transform based methods. For example, it can still detect rectangles even when lens
# distortion causes those rectangles to look bent. Rounded rectangles are no problem!
# (But, given this the code will also detect small radius circles too)...

import sensor, image, time

sensor.reset()
sensor.set_pixformat(sensor.RGB565) # grayscale is faster (160x120 max on OpenMV-M7)
sensor.set_framesize(sensor.QQVGA)
sensor.skip_frames(time = 2000)
clock = time.clock()

while(True):
    clock.tick()
    img = sensor.snapshot()

    # `threshold` below should be set to a high enough value to filter out noise
    # rectangles detected in the image which have low edge magnitudes. Rectangles
    # have larger edge magnitudes the larger and more contrasty they are...

    for r in img.find_rects(threshold = 10000):
        img.draw_rectangle(r.rect(), color = (255, 0, 0))
        for p in r.corners(): img.draw_circle(p[0], p[1], 5, color = (0, 255, 0))
        print(r)

    print("FPS %f" % clock.fps())

# Cartoon Filter
#
# This example shows off a simple cartoon filter on images. The cartoon
# filter works by joining similar pixel areas of an image and replacing
# the pixels in those areas with the area mean.

import sensor, image, time

sensor.reset()
sensor.set_pixformat(sensor.RGB565) # or GRAYSCALE...
sensor.set_framesize(sensor.QVGA) # or QQVGA...
sensor.skip_frames(time = 2000)
clock = time.clock()

while(True):
    clock.tick()

    # seed_threshold controls the maximum area growth of a colored
    # region. Making this larger will merge more pixels.

    # floating_threshold controls the maximum pixel-to-pixel difference
    # when growing a region. Settings this very high will quickly combine
    # all pixels in the image. You should keep this small.

    # cartoon() will grow regions while both thresholds are statisfied...

    img = sensor.snapshot().cartoon(seed_threshold=0.05, floating_thresholds=0.05)

    print(clock.fps())

# can then classify as a marker.

import sensor, image, time

# For color tracking to work really well you should ideally be in a very, very,
# very, controlled enviroment where the lighting is constant. Additionally, if
# you want to track more than 2 colors you need to set the boundaries for them
# very narrowly. If you try to track... generally red, green, and blue then
# you will end up just tracking everything which you don't want.
red_threshold   = (  40,   60,   60,   90,   50,   70)
blue_threshold  = (   0,   20,  -10,   30,  -60,   10)
# You may need to tweak the above settings for tracking red and blue things...
# Select an area in the Framebuffer to copy the color settings.

sensor.reset() # Initialize the camera sensor.
sensor.set_pixformat(sensor.RGB565) # use RGB565.
sensor.set_framesize(sensor.QQVGA) # use QQVGA for speed.
sensor.skip_frames(10) # Let new settings take affect.
sensor.set_whitebal(False) # turn this off.
clock = time.clock() # Tracks FPS.

while(True):
    clock.tick() # Track elapsed milliseconds between snapshots().
    img = sensor.snapshot() # Take a picture and return the image.

    blobs = img.find_blobs([red_threshold, blue_threshold])
    merged_blobs = img.find_markers(blobs)
    if merged_blobs:
        for b in merged_blobs:
            # Draw a rect around the blob.
            img.draw_rectangle(b[0:4]) # rect

# Optical Flow Example
#
# Your OpenMV Cam can use optical flow to determine the displacement between
# two images. This allows your OpenMV Cam to track movement like how your laser
# mouse tracks movement. By tacking the difference between successive images
# you can determine instaneous displacement with your OpenMV Cam too!

import sensor, image, time

sensor.reset() # Initialize the camera sensor.
sensor.set_pixformat(sensor.GRAYSCALE) # or sensor.GRAYSCALE
sensor.set_framesize(sensor.B64x32) # or B40x30 or B64x64
clock = time.clock() # Tracks FPS.

# NOTE: The find_displacement function works by taking the 2D FFTs of the old
# and new images and compares them using phase correlation. Your OpenMV Cam
# only has enough memory to work on two 64x64 FFTs (or 128x32, 32x128, or etc).
old = sensor.snapshot()

while(True):
    clock.tick() # Track elapsed milliseconds between snapshots().
    img = sensor.snapshot() # Take a picture and return the image.

    [delta_x, delta_y, response] = old.find_displacement(img)

    old = img.copy()

    print("%0.1f X\t%0.1f Y\t%0.2f QoR\t%0.2f FPS" % \
        (delta_x, delta_y, response, clock.fps()))

# IR Beacon Grayscale Tracking Example
#
# This example shows off IR beacon Grayscale tracking using the OpenMV Cam.

import sensor, image, time

thresholds = (255, 255) # thresholds for bright white light from IR.

sensor.reset()
sensor.set_pixformat(sensor.GRAYSCALE)
sensor.set_framesize(sensor.VGA)
sensor.set_windowing((240, 240)) # 240x240 center pixels of VGA
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" merges all overlapping blobs in the image.

while(True):
    clock.tick()
    img = sensor.snapshot()
    for blob in img.find_blobs([thresholds], pixels_threshold=200, area_threshold=200, merge=True):
        ratio = blob.w() / blob.h()
        if (ratio >= 0.5) and (ratio <= 1.5): # filter out non-squarish blobs
            img.draw_rectangle(blob.rect())
            img.draw_cross(blob.cx(), blob.cy())
    print(clock.fps())

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())

import pyb, sensor, image, os, time
sensor.reset()
sensor.set_framesize(sensor.QVGA)
if not "test" in os.listdir(): os.mkdir("test")
while(True):
    sensor.set_pixformat(sensor.GRAYSCALE)
    for i in range(2):
        img = sensor.snapshot()
        num = pyb.rng()
        print("Saving %d" % num)
        img.save("test/image-%d" % num)
        #
        img = sensor.snapshot()
        num = pyb.rng()
        print("Saving %d" % num)
        img.save("test/image-%d.bmp" % num)
        #
        img = sensor.snapshot()
        num = pyb.rng()
        print("Saving %d" % num)
        img.save("test/image-%d.pgm" % num)
        #
        img = sensor.snapshot()
        num = pyb.rng()
        print("Saving %d" % num)
        img.save("/test/image-%d" % num)
        #
        img = sensor.snapshot()
        num = pyb.rng()
        print("Saving %d" % num)
        img.save("/test/image-%d.bmp" % num)

# High FPS Example
#
# This example shows off how to make the frame rate of the global shutter camera extremely
# high. To do so you need to set the resolution to a low value such that pixel binning is
# activated on the camera and then reduce the maximum exposure time.
#
# When the resolution is 320x240 or less the camera reads out pixels 2x faster. When the
# resolution is 160x120 or less the camera reads out pixels 4x faster. This happens due
# to pixel binning which is automatically activated for you to increase the readout speed.
#
# While the readout speed may increase the camera must still expose the image for the request
# time so you will not get the maximum readout speed unless you reduce the exposure time too.
# This results in a dark image however so YOU NEED A LOT of lighting for high FPS.

import sensor, image, time

sensor.reset()                      # Reset and initialize the sensor.
sensor.set_pixformat(sensor.GRAYSCALE) # Set pixel format to GRAYSCALE
sensor.set_framesize(sensor.QQVGA)  # Set frame size to QQVGA (160x120) - make smaller to go faster
sensor.skip_frames(time = 2000)     # Wait for settings take effect.
clock = time.clock()                # Create a clock object to track the FPS.

sensor.set_auto_exposure(True, exposure_us=5000) # make smaller to go faster

while(True):
    clock.tick()                    # Update the FPS clock.
    img = sensor.snapshot()         # Take a picture and return the image.
    print(clock.fps())              # Note: OpenMV Cam runs about half as fast when connected
                                    # to the IDE. The FPS should increase once disconnected.

# Find Circles Example
#
# This example shows off how to find circles in the image using the Hough
# Transform. https://en.wikipedia.org/wiki/Circle_Hough_Transform
#
# Note that the find_circles() method will only find circles which are completely
# inside of the image. Circles which go outside of the image/roi are ignored...

import sensor, image, time

sensor.reset()
sensor.set_pixformat(sensor.RGB565) # grayscale is faster
sensor.set_framesize(sensor.QQVGA)
sensor.skip_frames(time = 2000)
clock = time.clock()

while(True):
    clock.tick()
    img = sensor.snapshot().lens_corr(1.8)

    # Circle objects have four values: x, y, r (radius), and magnitude. The
    # magnitude is the strength of the detection of the circle. Higher is
    # better...

    # `threshold` controls how many circles are found. Increase its value
    # to decrease the number of circles detected...

    # `x_margin`, `y_margin`, and `r_margin` control the merging of similar
    # circles in the x, y, and r (radius) directions.

    for c in img.find_circles(threshold = 2000, x_margin = 10, y_margin = 10, r_margin = 10):

#
# Note: You will need an SD card to run this example.
#
# This example demonstrates using frame differencing with your OpenMV Cam. This
# example is advanced because it preforms a background update to deal with the
# backgound image changing overtime.

import sensor, image, pyb, os, time

TRIGGER_THRESHOLD = 5

BG_UPDATE_FRAMES = 50 # How many frames before blending.
BG_UPDATE_BLEND = 128 # How much to blend by... ([0-256]==[0.0-1.0]).

sensor.reset() # Initialize the camera sensor.
sensor.set_pixformat(sensor.RGB565) # or sensor.RGB565
sensor.set_framesize(sensor.QVGA) # or sensor.QQVGA (or others)
sensor.skip_frames(time = 2000) # Let new settings take affect.
sensor.set_auto_whitebal(False) # Turn off white balance.
clock = time.clock() # Tracks FPS.

if not "temp" in os.listdir(): os.mkdir("temp") # Make a temp directory

print("About to save background image...")
sensor.skip_frames(time = 2000) # Give the user time to get ready.
sensor.snapshot().save("temp/bg.bmp")
print("Saved background image - Now frame differencing!")

triggered = False

frame_count = 0

# Image Histogram Info Example
#
# This script computes the histogram of the image and prints it out.

import sensor, image, time

sensor.reset()
sensor.set_pixformat(sensor.GRAYSCALE) # or 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()

while(True):
    clock.tick()
    img = sensor.snapshot()
    # Gets the grayscale histogram for the image into 8 bins.
    # Bins defaults to 256 and may be between 2 and 256.
    print(img.get_histogram(bins=8))
    print(clock.fps())

# You can also pass get_histogram() an "roi=" to get just the histogram of that area.
# get_histogram() allows you to quickly determine the color channel information of
# any any area in the image.

# Basic Frame Differencing Example
#
# Note: You will need an SD card to run this example.
#
# This example demonstrates using frame differencing with your OpenMV Cam. It's
# called basic frame differencing because there's no background image update.
# So, as time passes the background image may change resulting in issues.

import sensor, image, pyb, os, 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(time = 2000) # Let new settings take affect.
sensor.set_auto_whitebal(False) # Turn off white balance.
clock = time.clock() # Tracks FPS.

if not "temp" in os.listdir(): os.mkdir("temp") # Make a temp directory

print("About to save background image...")
sensor.skip_frames(time = 2000) # Give the user time to get ready.
sensor.snapshot().save("temp/bg.bmp")
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.difference("temp/bg.bmp")

rst.high()
time.sleep(100)

write_command(0x11) # Sleep Exit
time.sleep(120)

# Memory Data Access Control
write_command(0x36, 0xC0)

# Interface Pixel Format
write_command(0x3A, 0x05)

# Display On
write_command(0x29)

sensor.reset() # Initialize the camera sensor.
sensor.set_pixformat(sensor.RGB565) # must be this
sensor.set_framesize(sensor.QQVGA2) # must be this
sensor.skip_frames(time = 2000) # Let new settings take affect.
clock = time.clock() # Tracks FPS.

while(True):
    clock.tick() # Track elapsed milliseconds between snapshots().
    img = sensor.snapshot() # Take a picture and return the image.

    write_command(0x2C) # Write image command...
    write_image(img)

    print(clock.fps()) # Note: Your OpenMV Cam runs about half as fast while
    # connected to your computer. The FPS should increase once disconnected.

# CIFAR-10 Search Just Center Example
#
# 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()

# centroid of the largest blob in each roi. The x position of the centroids
# will then be averaged with different weights where the most weight is assigned
# to the roi near the bottom of the image and less to the next roi and so on.
ROIS = [ # [ROI, weight]
        (0, 100, 160, 20, 0.7), # You'll need to tweak the weights for your app
        (0,  50, 160, 20, 0.3), # depending on how your robot is setup.
        (0,   0, 160, 20, 0.1)
       ]

# 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...
sensor.reset() # Initialize the camera sensor.
sensor.set_pixformat(sensor.GRAYSCALE) # use grayscale.
sensor.set_framesize(sensor.QQVGA) # use QQVGA for speed.
sensor.skip_frames(time = 2000) # Let new settings take affect.
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() # Tracks FPS.

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(GRAYSCALE_THRESHOLD, roi=r[0:4], merge=True) # r[0:4] is roi tuple.

import sensor, image, time

# NOTE!!! You have to use a small power of 2 resolution when using
# find_displacement(). This is because the algorithm is powered by
# something called phase correlation which does the image comparison
# using FFTs. A non-power of 2 resolution requires padding to a power
# of 2 which reduces the usefulness of the algorithm results. Please
# use a resolution like B128X128 or B128X64 (2x faster).

# 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.

# Mjpeg recording 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.

import sensor, image, time, mjpeg

sensor.reset()
sensor.set_framesize(sensor.QVGA)
sensor.set_pixformat(sensor.RGB565) # you can also use grayscale

# Warm up the cam
for i in range(10):
    sensor.snapshot()

# FPS clock
clock = time.clock()
mjpeg = mjpeg.Mjpeg("/test.mjpeg") # video setup to use current resolution

for i in range(100):
    clock.tick()
    img = sensor.snapshot()
    mjpeg.add_frame(img)
    # Print FPS.
    # Note: Actual FPS is higher, the IDE slows down streaming.
    print(clock.fps())

mjpeg.close(clock.fps())
print("done")

#
# This example shows off single color code tracking using the OpenMV Cam.
#
# 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)