I would like to load an image into OpenMV, create a rectangular region, then get the CLOSEST color NAME of the pixels in that region. I would like to use color names from the following page:
For example, the name of the closest color in the region shown in the image is “Royal Blue”. Of course the region may have small parts of other colors (black,red, brown, etc.), but I need the color name representing most pixels in the picture.
import math
list_of_color_names = ["AliceBlue"]
list_of_color_rgb_tuples = [(240, 248, 255)]
def closest_color(r, g, b):
closest_color_diff = 1000000
closest_color_name = "Uknown"
for i in range(len(list_of_color_rgb_tuples)):
red_diff = r - list_of_color_rgb_tuples[i][0]
green_diff = g - list_of_color_rgb_tuples[i][1]
blue_diff = b - list_of_color_rgb_tuples[i][2]
diff = math.sqrt((red_diff*red_diff)+(green_diff*green_diff)+(blue_diff*blue_diff))
if diff < closest_color_diff:
closest_color_diff = diff
closest_color_name = list_of_color_names[i]
return closest_color_name
Then just use the color conversion methods to take the results of get_statistics which are in the LAB color space and convert those to RGB and then feed the RGB value into the function above.
I am looking at the Color tracking example as I am still learning Python. I can see the statement:
print(img.get_statistics())
Can you please show me how to
"
…use the color conversion methods to take the results of get_statistics which are in the LAB color space and convert those to RGB and then feed the RGB value into the function
Please accommodate one last question. I made the change in the code below but there is a compile error. May I ask you for one last request to check it out? I am sure it is a simple Python language error.
Thanks in advance.
# Image Statistics Info Example
#
# This script computes the statistics of the image and prints it out.
import sensor, image, time
import math
sensor.reset()
sensor.set_pixformat(sensor.RGB565) # or RGB565.
sensor.set_framesize(sensor.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()
while(True):
clock.tick()
img = sensor.snapshot()
print(img.get_statistics())
#-----------------------------------------------------------------------------------
stats = img.get_statistics()
r, g, b = image.lab_to_rgb((stats.l_mean(), stats.a_mean(), stats.b_mean()))
colorName = closest_color(r, g, b)
print(colorName)
#-----------------------------------------------------------------------------------
print(clock.fps())
def closest_color(r, g, b):
closest_color_diff = 1000000
closest_color_name = "Uknown"
for i in range(len(list_of_color_rgb_tuples)):
red_diff = r - list_of_color_rgb_tuples[i][0]
green_diff = g - list_of_color_rgb_tuples[i][1]
blue_diff = b - list_of_color_rgb_tuples[i][2]
diff = math.sqrt((red_diff*red_diff)+(green_diff*green_diff)+(blue_diff*blue_diff))
if diff < closest_color_diff:
closest_color_diff = diff
closest_color_name = list_of_color_names[i]
return closest_color_name
Hi Jimme, you need to debug these types of errors yourself. I’ve available for high level help support. You are asking about basic python coding issues.
Anyway, the variable:
list_of_color_rgb_tuples is a list of the words you need to define:
But then what happens if the rgb values obtained from the function are not identical to those? For example (235, 245, 255)? How will the closest color be found?
So, it seems like you are attempting something quite a bit out of your skill level. So, I’ll do this. Here’s the code:
import sensor, image, time, math, gc
sensor.reset()
sensor.set_pixformat(sensor.RGB565) # 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()
list_of_color_names = []
list_of_color_rgb_tuples = []
with open("rgb.txt") as f:
for l in f:
tokens = l.strip().split()
print("Loading", tokens)
list_of_color_names.append(tokens[3])
list_of_color_rgb_tuples.append(tuple(int(x) for x in tokens[0:3]))
gc.collect() # prevent heap fragmentation
def closest_color(r, g, b):
closest_color_diff = 1000000
closest_color_name = "Uknown"
for i in range(len(list_of_color_rgb_tuples)):
red_diff = r - list_of_color_rgb_tuples[i][0]
green_diff = g - list_of_color_rgb_tuples[i][1]
blue_diff = b - list_of_color_rgb_tuples[i][2]
diff = math.sqrt((red_diff*red_diff)+(green_diff*green_diff)+(blue_diff*blue_diff))
if diff < closest_color_diff:
closest_color_diff = diff
closest_color_name = list_of_color_names[i]
return closest_color_name
while(True):
clock.tick()
img = sensor.snapshot()
stats = img.get_statistics()
r, g, b = image.lab_to_rgb((stats.l_mean(), stats.a_mean(), stats.b_mean()))
colorName = closest_color(r, g, b)
print(clock.fps(), colorName)
And finally, you need to copy the attached rgb.txt file to the OpenMV Cam disk to get the color names.
…
This meets your initial question. However, moving forwards… please try to understand exactly what is happening in every line in that script and understand what’s going on. rgb.txt (31 KB)
Thank you again, the code you sent works like a charm.
Where did the color names in the rgb.txt come from? I noticed that most of them are rather strange names . For example, 39, 63, 65 gives from rgb.txt a “daintree” color.
. For example, 39, 63, 65 gives from rgb.txt a “daintree” color.