Uses pandas/numpy/numexpr for operations on pictures - very fast
Project description
$pip install a-pandas-ex-image-tools
import cv2
import pandas as pd
from a_cv2_imshow_thread import add_imshow_thread_to_cv2
add_imshow_thread_to_cv2()
from a_pandas_ex_image_tools import pd_add_image_tools, debugger, get_list_of_all_colors_in_range
pd_add_image_tools()
#you can ignore all exceptions
debugger.stop_on_exceptions = True
#Useful if you want to ignore every exception, but want to see what's going on
debugger.print_all_exceptions = True
pythonlogo = 'https://github.com/hansalemaos/screenshots/raw/main/colorfind1.png'
df = pd.Q_image2df(pythonlogo)
df
Out[5]:
x y red green blue
0 0 0 0 0 0
1 1 0 0 0 0
2 2 0 0 0 0
3 3 0 0 0 0
4 4 0 0 0 0
... ... ... ... ...
39995 195 199 0 0 0
39996 196 199 0 0 0
39997 197 199 0 0 0
39998 198 199 0 0 0
39999 199 199 0 0 0
[40000 rows x 5 columns]
df1 = df.im_multicolor_lookup([(255, 232, 115)])
df1
Out[11]:
x y red green blue
13337 137 66 255 232 115
13338 138 66 255 232 115
13339 139 66 255 232 115
13340 140 66 255 232 115
13341 141 66 255 232 115
... ... ... ... ...
25269 69 126 255 232 115
25270 70 126 255 232 115
25467 67 127 255 232 115
25468 68 127 255 232 115
25667 67 128 255 232 115
[608 rows x 5 columns]
df1.im_xy_to_convex_hull() # get the convex hull
Out[12]:
array([[[154, 67]],
[[153, 68]],
[[ 73, 124]],
[[ 67, 128]],
[[ 67, 114]],
[[ 70, 108]],
[[ 74, 104]],
[[137, 66]],
[[150, 66]]], dtype=int32)
df.im_show_df_image() # cv2.imshow
# converts the pandas DataFrame back to a numpy array
df.im_df_to_np_image()
df.im_print_all_colors() # prints all colors sorted by qty
df1.im_xy_to_shapely()
Out[13]: <shapely.geometry.polygon.Polygon at 0x21302f70>
# Gets all x,y coordinates as a numpy array
df1.im_xy_to_np()
Out[14]:
array([[137, 66],
[138, 66],
[139, 66],
...,
[ 67, 127],
[ 68, 127],
[ 67, 128]], dtype=uint16)
# you can even edit images
df.loc[df.im_multicolor_lookup([(0,0,0)]).index, ['red', 'green', 'blue']] = [255,255,0]
# Replace a color range with one color
df.loc[df.im_multicolor_lookup(get_list_of_all_colors_in_range((0,0,0), (10,10,10))).index, ['red', 'green', 'blue']] = [255,255,0]
# cluster colors in areas
# More about the parameters: https://scikit-learn.org/stable/modules/generated/sklearn.cluster.DBSCAN.html
multilookupdf = df.im_multicolor_lookup(
colorlist=[(255, 232, 115), (255, 0, 0), (48, 105, 152)], ).im_xy_to_color_clusters(eps=2, min_samples=4, n_jobs=-1,
poly_color=(255, 255, 0), poly_outline_thickness=3, rectanglecolor=(0, 255, 0), )
multilookupdf
Out[24]:
aa_repr_point aa_bounds ... aa_draw_poly aa_draw_rectangle
0 (124, 82) (106, 66, 132, 96) ... () ()
1 (142, 71) (137, 66, 154, 79) ... () ()
2 (73, 119) (67, 102, 104, 128) ... () ()
3 (158, 123) (144, 117, 164, 131) ... () ()
4 (119, 155) (93, 139, 132, 167) ... () ()
[5 rows x 9 columns]
# Cluster multiple colors
multilookupdf = df.im_multicolor_lookup(
colorlist=[(255, 232, 115), (255, 0, 0), (48, 105, 152)], ).im_xy_to_color_clusters(eps=2, min_samples=4, n_jobs=-1,
poly_color=(255, 0, 120), poly_outline_thickness=3, rectanglecolor=(0, 255, 0), )
# show the clustered results
npimage = df.im_df_to_np_image().copy()
multilookupdf.aa_draw_rectangle.apply(lambda x: x(npimage))
multilookupdf.aa_draw_poly.apply(lambda x: x(npimage))
cv2.imshow_thread(npimage)
bab=df.im_multicolor_lookup(colorlist=[(255, 232, 115), (48, 105, 152)], ).im_xy_to_merged_color_clusters(
image=df.im_df_to_np_image().copy(), show_results=True, max_merge_distance=2, eps=2, min_samples=4, n_jobs=-1,
poly_color=(255, 255, 0), poly_outline_thickness=3, rectanglecolor=(0, 255, 0), mergedcolor=(0, 0, 255), )
bab
Out[6]:
aa_repr_point ... bb_pic
0 (124, 82) ... [[[0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], ...
1 (142, 71) ... [[[0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], ...
2 (73, 119) ... [[[0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], ...
# Do you prefer working with dicts instead of DataFrames?
df.im_get_color_lookup_dict()
{(0,
0,
0): array([[ 0, 0],
[ 1, 0],
[ 2, 0],
...,
[196, 199],
[197, 199],
[198, 199]], dtype=uint16),
(15, 33, 48): array([[199, 199]], dtype=uint16),
(17, 35, 49): array([[131, 88]], dtype=uint16),
(17, 35, 50): array([[ 63, 114]], dtype=uint16),
(17, 36, 53): array([[75, 99]], dtype=uint16),
(17,
37,
53): array([[119, 97],
[129, 91]], dtype=uint16),
(18, 36, 50): array([[126, 94]], dtype=uint16),
(18, 37, 52): array([[125, 35]], dtype=uint16),
(19, 38, 53): array([[ 68, 104]], dtype=uint16),
(19,
39,
55): array([[ 36, 124],
[ 73, 100]], dtype=uint16),
...
df.im_get_image_size()
# Out[7]: [(0, 0, 200, 200)]
# Count colors in certain regions
df.im_get_colors_in_regions_and_count(regions=[(10, 10, 100, 100), (150, 150, 160, 170)])
Out[11]:
red green blue ... size_of_area percentage_of_area region
0 0 0 0 ... 8100 61.925926 (10, 10, 100, 100)
1 69 132 182 ... 8100 1.790123 (10, 10, 100, 100)
2 255 255 255 ... 8100 1.160494 (10, 10, 100, 100)
3 67 129 179 ... 8100 1.148148 (10, 10, 100, 100)
4 62 123 172 ... 8100 1.135802 (10, 10, 100, 100)
.. ... ... ... ... ... ... ...
218 59 110 151 ... 8100 0.012346 (10, 10, 100, 100)
219 59 111 153 ... 8100 0.012346 (10, 10, 100, 100)
220 60 112 154 ... 8100 0.012346 (10, 10, 100, 100)
221 62 111 147 ... 8100 0.012346 (10, 10, 100, 100)
222 0 0 0 ... 200 115.500000 (150, 150, 160, 170)
[223 rows x 7 columns]
# You can limit the search region
df.im_limit_search_areas(areas=[(0, 0, 50, 50), (50, 50, 150, 170)]).im_multicolor_lookup(
colorlist=[(255, 232, 115), (255, 0, 0), (48, 105, 152)])
Out[12]:
x y red green blue
13332 132 66 48 105 152
13337 137 66 255 232 115
13338 138 66 255 232 115
13339 139 66 255 232 115
13340 140 66 255 232 115
... ... ... ... ...
33502 102 167 255 0 0
33503 103 167 255 0 0
33504 104 167 255 0 0
33505 105 167 255 0 0
33506 106 167 255 0 0
[1352 rows x 5 columns]
# define regions for every individual color
colordict = {(255, 0, 0): [(0, 0, 50, 50), (0, 0, 1000, 1000)],
(255, 232, 115): [(0, 0, 1000, 1000), (500, 500, 502, 502), (900, 900, 1000, 950), ],
(255, 255, 255): [(0, 0, 1000, 1000), (400, 200, 800, 400)], (0, 176, 111): [(0, 0, 1000, 1000)],
(57, 59, 61): [(0, 0, 1000, 1000)], (0, 0, 0): [(0, 0, 1000, 1000)], }
regi = df.im_get_coords_of_colors_in_regions(colordict)
x y red green blue
0 0 0 0 0 0
1 1 0 0 0 0
2 2 0 0 0 0
3 3 0 0 0 0
4 4 0 0 0 0
... ... ... ... ...
39995 195 199 0 0 0
39996 196 199 0 0 0
39997 197 199 0 0 0
39998 198 199 0 0 0
39999 199 199 0 0 0
[28789 rows x 5 columns]
# You can use a multi index to check individual pixels
dfxy = df.im_xy_as_index()
red green blue
x y
0 0 0 0 0
1 0 0 0
2 0 0 0
3 0 0 0
4 0 0 0
... ... ...
199 195 0 0 0
196 0 0 0
197 0 0 0
198 0 0 0
199 0 0 0
[40000 rows x 3 columns]
# Let's get the RGB values using the DataFrame we have just created
dfxy.loc[[(0,100), (100,100), (44,40)]]
Out[17]:
red green blue
x y
0 100 0 0 0
100 100 0 0 0
44 40 0 0 0
# can also be done like that:
coordlist = [(10, 10), (100, 100), (50, 80)]
colors_of_coords = df.im_get_colors_of_coords(coordlist)
colors_of_coords
Out[19]:
red green blue
x y
10 10 0 0 0
100 100 0 0 0
50 80 70 134 184
Compare 2 images:
pythonlogo = 'https://github.com/hansalemaos/screenshots/raw/main/colorfind1.png'
df = pd.Q_image2df(pythonlogo)
df.loc[df.im_multicolor_lookup(get_list_of_all_colors_in_range((0,0,0), (10,10,10))).index, ['red', 'green', 'blue']] = [255,255,0]
pythonlogo = 'https://github.com/hansalemaos/screenshots/raw/main/colorfind1.png'
df2 = pd.Q_image2df(pythonlogo)
df2.im_compare_2_images(df)
Out[23]:
pic_index x y r1 g1 b1 r2 g2 b2
0 0 0 0 0 0 0 255 255 0
1 1 1 0 0 0 0 255 255 0
2 2 2 0 0 0 0 255 255 0
3 3 3 0 0 0 0 255 255 0
4 4 4 0 0 0 0 255 255 0
... ... ... .. .. .. ... ... ..
27510 39995 195 199 0 0 0 255 255 0
27511 39996 196 199 0 0 0 255 255 0
27512 39997 197 199 0 0 0 255 255 0
27513 39998 198 199 0 0 0 255 255 0
27514 39999 199 199 0 0 0 255 255 0
[27515 rows x 9 columns]
# If you want to do additional stuff with OpenCV:
df.im_xy_to_cv2_coords()
Out[28]:
array([[[ 0, 0],
[ 1, 0],
[ 2, 0],
...,
[197, 199],
[198, 199],
[199, 199]]])
df.im_singlecolor_lookup((255,255,0))
Out[45]:
x y red green blue
0 0 0 255 255 0
1 1 0 255 255 0
2 2 0 255 255 0
3 3 0 255 255 0
4 4 0 255 255 0
... ... ... ... ...
39995 195 199 255 255 0
39996 196 199 255 255 0
39997 197 199 255 255 0
39998 198 199 255 255 0
39999 199 199 255 255 0
[27515 rows x 5 columns]
df.im_get_closest_colors([(10,3,4)])
Out[55]:
r g b rating rgb
0 0 0 0 11.179688 (10, 3, 4)
1 15 33 48 53.500000 (10, 3, 4)
2 17 35 49 55.656250 (10, 3, 4)
3 17 35 50 56.468750 (10, 3, 4)
4 18 36 50 57.187500 (10, 3, 4)
.. ... ... ... ... ...
712 250 242 218 400.750000 (10, 3, 4)
713 252 247 230 411.250000 (10, 3, 4)
714 241 244 246 412.250000 (10, 3, 4)
715 253 251 243 421.500000 (10, 3, 4)
716 255 255 255 432.000000 (10, 3, 4)
[717 rows x 5 columns]
get_list_of_all_colors_in_range((0,0,0), (3,4,3))
Out[59]:
[(0, 0, 0),
(0, 0, 1),
(0, 0, 2),
(0, 0, 3),
(0, 1, 0),
(0, 1, 1),
(0, 1, 2),
(0, 1, 3),
(0, 2, 0),
(0, 2, 1),
(0, 2, 2),
(0, 2, 3),
(0, 3, 0),
dfr = df.im_rgb_as_index()
Out[65]:
x y
red green blue
0 0 0 0 0
0 1 0
0 2 0
0 3 0
0 4 0
... ...
255 255 255 120 155
255 121 155
255 117 156
255 118 156
255 119 156
[40000 rows x 2 columns]
#Now you can use dfr.loc to get the coordinates
dfr.loc[(17,37,53)]
dfr.loc[ (39,83,119)]
# finds all colors that only exist in the region and nowhere else in the picture
df.im_get_unique_colors_in_region([(0,0,100,100)])
Out[72]:
x y red green blue
18432 32 92 73 137 188
18433 33 92 73 137 187
18434 34 92 72 136 187
18435 35 92 72 136 186
18436 36 92 72 135 186
.. .. ... ... ...
14255 55 71 71 135 186
14256 56 71 71 135 185
14257 57 71 71 134 185
12266 66 61 53 102 140
18431 31 92 59 111 153
[845 rows x 5 columns]
# finds all colors that are in the region and somewhere else in the picture
df.im_get_not_unique_colors_in_region([(0,0,100,100)])
Out[73]:
x y red green blue
0 0 0 0 0 0
1 1 0 0 0 0
2 2 0 0 0 0
3 3 0 0 0 0
4 4 0 0 0 0
... ... ... ... ...
20096 96 100 0 0 0
20097 97 100 0 0 0
20098 98 100 0 0 0
20099 99 100 0 0 0
20100 100 100 0 0 0
[9356 rows x 5 columns]
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