working predictive model

This commit is contained in:
glmdev 2019-02-09 01:06:35 -06:00
parent 284bd3315a
commit 8ff4856e4e
2 changed files with 93 additions and 34 deletions

View File

@ -1,7 +1,9 @@
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@ -115,33 +123,18 @@
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View File

@ -1,6 +1,5 @@
from PIL import Image from PIL import Image
def average_color(x, y, n, image): def average_color(x, y, n, image):
r, g, b = 0, 0, 0 r, g, b = 0, 0, 0
count = 0 count = 0
@ -13,6 +12,73 @@ def average_color(x, y, n, image):
count += 1 count += 1
return ((r / count), (g / count), (b / count)) return ((r / count), (g / count), (b / count))
img = Image.open('./lib/test-images/lot-1-car.jpg').load() def differences(rgb_tuple1, rgb_tuple2):
r, g, b = average_color(0, 0, 479, img) r1, g1, b1 = rgb_tuple1
print(r, g, b) r2, g2, b2 = rgb_tuple2
return ((r1-r2), (g1-g2), (b1-b2))
def threshold(diff_tuple, trigger=4):
dr, dg, db = diff_tuple
return (((( dr**4 + dg**4 + db**4 ) / 3)**0.25) > trigger) # Thanks to Thomas Atkins, math whiz
reference_image = Image.open('./lib/test-images/lot-empty.jpg').load()
compare_image = Image.open('./lib/test-images/lot-1-car.jpg').load()
stall_1 = {
'location': (54, 79, 109),
'reference': average_color(54, 79, 109, reference_image),
'compare': average_color(54, 79, 109, compare_image)
}
stall_2 = {
'location': (186, 81, 116),
'reference': average_color(186, 81, 116, reference_image),
'compare': average_color(186, 81, 116, compare_image)
}
stall_3 = {
'location': (317, 71, 127),
'reference': average_color(317, 71, 127, reference_image),
'compare': average_color(317, 71, 127, compare_image)
}
stall_4 = {
'location': (469, 76, 100),
'reference': average_color(469, 76, 100, reference_image),
'compare': average_color(469, 76, 100, compare_image)
}
stall_5 = {
'location': (27, 237, 119),
'reference': average_color(27, 237, 119, reference_image),
'compare': average_color(27, 237, 119, compare_image)
}
stall_6 = {
'location': (172, 240, 130),
'reference': average_color(172, 240, 130, reference_image),
'compare': average_color(172, 240, 130, compare_image)
}
stall_7 = {
'location': (317, 234, 144),
'reference': average_color(317, 234, 144, reference_image),
'compare': average_color(317, 234, 144, compare_image)
}
stall_8 = {
'location': (481, 233, 112),
'reference': average_color(481, 233, 112, reference_image),
'compare': average_color(481, 233, 112, compare_image)
}
print(threshold(differences(stall_1['reference'], stall_1['compare'])))
print(threshold(differences(stall_2['reference'], stall_2['compare'])))
print(threshold(differences(stall_3['reference'], stall_3['compare'])))
print(threshold(differences(stall_4['reference'], stall_4['compare'])))
print(threshold(differences(stall_5['reference'], stall_5['compare'])))
print(threshold(differences(stall_6['reference'], stall_6['compare'])))
print(threshold(differences(stall_7['reference'], stall_7['compare'])))
print(threshold(differences(stall_8['reference'], stall_8['compare'])))