An even stupider way to detect Red Bulls... in an empty fridge. It's like, extremely stupid.

DetectRedbull2.py

import numpy as np
import matplotlib.pyplot as plt
from skimage import io
import skimage

img =  io.imread('./test1.png')
img_gray =  io.imread('./test1.png', as_gray=True)

window = 0.15
max = img_gray.max()
min = max - window
if min < 0:
  min = 0

print(max)
print(min)

w = img.shape[1]
h = img.shape[0]

minx = w
miny = h

maxx = 0
maxy = 0

print(maxx)
print(maxy)

plt.imshow(img)
plt.colorbar()
plt.show()

for x in range(0, w):
  for y in range(0, h):
    #img[y, x, 0] = 0
    #img[y, x, 1] = 0
    if img_gray[y, x] >= min and img_gray[y, x] <= max:
      if x < minx:
        minx = x
      if x > maxx:
        maxx = x
      if y < miny:
        miny = y
      if y > maxy:
        maxy = y


cropped = img[miny:maxy, minx:maxx]

plt.imshow(cropped)
plt.colorbar()
plt.show()

!pip install colormath
import colormath.color_objects
import colormath.color_conversions
import colormath.color_diff
from colormath.color_objects import sRGBColor, LabColor
from colormath.color_conversions import convert_color
from colormath.color_diff import delta_e_cie2000

# Can Color
color1_rgb = sRGBColor(20/255, 36/255, 89/255);
tr = 20
tg = 36
tb = 100

canc = np.array([tr/255,tg/255,tb/255])
can = skimage.color.rgb2hsv(canc)

ww = cropped.shape[1]
hh = cropped.shape[0]

blueness = np.zeros((hh, ww, 3))
blueness.shape
import math

min = 1000
max = 0

for x in range(0, ww):
  for y in range(0, hh):
    # Img Color
    r = cropped[y, x, 0]#/255
    g = cropped[y, x, 1]#/255
    b = cropped[y, x, 2]#/255
    #color2_rgb = sRGBColor(r, g, b);

    # Convert from RGB to Lab Color Space
    #color1_lab = convert_color(color1_rgb, LabColor);
    #color2_lab = convert_color(color2_rgb, LabColor);

    # Find the color difference
    #delta_e = delta_e_cie2000(color1_lab, color2_lab);
    c1rgb = np.array([r/255,g/255,b/255])
    c1 = skimage.color.rgb2hsv(c1rgb)
    d = np.linalg.norm(c1 - can)
    # d = math.sqrt((tr-r)**2 + (tg-g)**2 + (tb-b)**2)
    if d < min:
      min = d
    if d > max:
      max = d
    blueness[y, x, 0] = 1 - d
    blueness[y, x, 1] = 1 - d
    blueness[y, x, 2] = 1 - d

print(min)
print(max)

plt.imshow(blueness)
plt.colorbar()
plt.show()


cansq =  np.array([tr/255,tg/255,tb/255]) * np.ones([50,50,3])

plt.subplot(131)
plt.title('can')
plt.imshow(cansq)

cx = 0
cy = 0
wsum = 0

maxblue = blueness.max()
print(maxblue)
minblue = maxblue - 0.02

for x in range(0, ww):
  for y in range(0, hh):
    if (blueness[y, x, 0] > minblue):
      wsum = wsum + blueness[y, x, 0]
      cx = cx + blueness[y, x, 0] * x
      cy = cy + blueness[y, x, 0] * y

cx = cx/wsum
cy = cy/wsum

icx = int(cx)
icy = int(cy)

blueness[icy-2:icy+2, icx-2:icx+2, 0] = 1
blueness[icy-2:icy+2, icx-2:icx+2, 1] = 0
blueness[icy-2:icy+2, icx-2:icx+2, 2] = 0

plt.imshow(blueness)
plt.colorbar()
plt.show()