Add low precision color detection (#17)
* Add low precision color detection * Add blank line at end of file * Remove tab * Remove tab * Remove useless comment Co-authored-by: Erick <74315338+erickahmed@users.noreply.github.com> * Update vision-env/src/vision-test.py Co-authored-by: Erick <74315338+erickahmed@users.noreply.github.com> * Update vision-env/src/vision-test.py Co-authored-by: Erick <74315338+erickahmed@users.noreply.github.com> * Update vision-env/src/vision-test.py Co-authored-by: Erick <74315338+erickahmed@users.noreply.github.com> Co-authored-by: Erick <74315338+erickahmed@users.noreply.github.com>
This commit is contained in:
Davide Tonelli
GitHub
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from scipy.spatial import KDTree
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from webcolors import (
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hex_to_rgb,
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)
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COLORS = {
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"aqua": ["#00ffff", (0,255,255)],
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"black": ["#000000", (0,0,0)],
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"blue": ["#0000ff", (0,0,255)],
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"fuchsia": ["#ff00ff", (255,0,255)],
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"green": ["#008000", (0,128,0)],
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"gray": ["#808080", (128,128,128)],
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"lime": ["#00ff00", (0,255,0)],
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"olive": ["#808000", (128,128,0)],
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"purple": ["#800080", (128,0,128)],
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"red": ["#ff0000", (255,0,0)],
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"silver": ["#c0c0c0", (192,192,192)],
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"teal": ["#008080", (0,128,128)],
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"white": ["#ffffff", (255,255,255)],
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"yellow": ["#ffff00", (255,255,0)],
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"beige": ["#f5f5dc", (245,245,220)],
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"brown": ["#a52a2a", (165,42,42)],
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"gold": ["#ffd700", (255,215,0)],
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"pink": ["#ffc0cb", (255,192,203)],
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"lavender": ["#e6e6fa", (230,230,250)],
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"turquoise": ["#40e0d0", (64,224,208)],
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"violet": ["#ee82ee", (238,130,238)],
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"orange": ["#ffa500", (255,165,0)],
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"lightblue": ["#add8e6", (173,216,230)]
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}
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COLORS_HEX = {
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"aqua": "#00ffff",
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"black": "#000000",
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"blue": "#0000ff",
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"fuchsia": "#ff00ff",
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"green": "#008000",
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"gray": "#808080",
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"lime": "#00ff00",
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"olive": "#808000",
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"purple": "#800080",
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"red": "#ff0000",
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"silver": "#c0c0c0",
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"teal": "#008080",
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"white": "#ffffff",
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"yellow": "#ffff00",
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"beige": "#f5f5dc",
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"brown": "#a52a2a",
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"gold": "#ffd700",
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"pink": "#ffc0cb",
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"lavender": "#e6e6fa",
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"turquoise": "#40e0d0",
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"violet": "#ee82ee",
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"orange": "#ffa500",
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"lightblue": "#add8e6",
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}
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def convert_rgb_to_names(rgb_tuple):
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# A dictionary of all the color names and their respective hex in color_db
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color_db = COLORS_HEX
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names = []
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rgb_values = []
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for color_name, color_hex in color_db.items():
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names.append(color_name)
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rgb_values.append(hex_to_rgb(color_hex))
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kdt_db = KDTree(rgb_values)
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distance, index = kdt_db.query(rgb_tuple)
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return str(names[index])
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@@ -1,16 +1,22 @@
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from genericpath import isfile
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import io
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import os
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import sys
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import numpy as np
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import cv2
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import math
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import colors_detection
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import json
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from genericpath import isfile
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from pyautogui import size
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from google.cloud import vision
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# Minimum score required for labels detection
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MIN_SCORE_REQUIRED = 0.80
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# Minimum pixel_fraction required for color detection.
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# Actually unused but useful for filtering colors.
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MIN_PFRACTION_REQUIRED = 0.02
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def shape_selection(event, x, y, flags, param):
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# grab references to the global variables
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global ref_point, crop
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@@ -138,15 +144,45 @@ client = vision.ImageAnnotatorClient()
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image = vision.Image(content=content)
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# Performs label detection on the image file
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response = client.label_detection(image=image)
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labels = response.label_annotations
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label_response = client.label_detection(image=image)
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labels = label_response.label_annotations
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# Performs image_properties detection on the image file
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colors_response = client.image_properties(image=image)
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colors = colors_response.image_properties_annotation.dominant_colors.colors
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# Labels sorted by attribute score
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sorted_labels = sorted(labels, key=lambda x:x.score, reverse=True)
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# Labels filtered by attribute score
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filtered_labels = filter(lambda x:x.score > MIN_SCORE_REQUIRED, sorted_labels)
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print('Labels:')
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# Labels sorted by attribute score
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sorted_colors = sorted(colors, key=lambda x:x.pixel_fraction, reverse=True)
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# Colors filtered by attribute pixel_fraction
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#filtered_colors = filter(lambda x:x.pixel_fraction > MIN_PFRACTION_REQUIRED, sorted_colors)
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# Actually no filtering policy
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filtered_colors = sorted_colors
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print('\nLabels:')
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for label in filtered_labels:
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print(label.description + ' ---> ' + str(label.score))
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print('--- Label: ' + label.description + ' ---> ' + str(math.trunc(label.score*100)) + '%' +
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'\n')
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print('\nColors:')
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for color_info in filtered_colors:
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color = color_info.color
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red = math.trunc(color.red)
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green = math.trunc(color.green)
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blue = math.trunc(color.blue)
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rgb_triplet = (red, green, blue)
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rgb_triplet_str = (str(red) + '%, ', str(green) + '%, ', str(blue) + '%')
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color_name = colors_detection.convert_rgb_to_names(rgb_triplet)
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# uncomment for verbose output
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#print(str(color_info.pixel_fraction) + ' == ' + str(math.trunc(color_info.pixel_fraction*100)) + '%' + ' score: ' + str(color_info.score) + '---> ' + color_name)
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print('--- Pixel Fraction: ' + str(round(color_info.pixel_fraction*100, 3)) + '%' +
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'\tScore: ' + str(round(color_info.score, 5)) +
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'\tColor Name: ' + color_name +
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'\n')
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