software that can name colors

detect_colors.py

# Use the tool
# python detect_color.py --image source.png
#
# Install the opencv and numpy packages before 
# python -m pip install numpy
# python -m pip install opencv-python
# python -m pip install scikit-learn

# import the necessary packages
import numpy as np
import cv2
from sklearn.cluster import KMeans


threshold_RGB = 120
threshold_area = 2000


def image_resize(image, width = None, height = None, inter = cv2.INTER_AREA):
  # initialize the dimensions of the image to be resized and
  # grab the image size
  dim = None
  (h, w) = image.shape[:2]

  # if both the width and height are None, then return the
  # original image
  if width is None and height is None:
      return image

  # check to see if the width is None
  if width is None:
      # calculate the ratio of the height and construct the
      # dimensions
      r = height / float(h)
      dim = (int(w * r), height)

  # otherwise, the height is None
  else:
      # calculate the ratio of the width and construct the
      # dimensions
      r = width / float(w)
      dim = (width, int(h * r))

  # resize the image
  resized = cv2.resize(image, dim, interpolation = inter)

  # return the resized image
  return resized


def name_color(masked_image, output, rgb): 
  r, g , b, color_name = rgb
  font = cv2.FONT_HERSHEY_SIMPLEX
  font_scale = 0.55
  font_margin = 5
  font_thickness = 1
  font_color = (255, 255, 255)
  
  gray = cv2.cvtColor(masked_image, cv2.COLOR_RGB2GRAY)
  ret, thresh = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY)
  #cv2.imshow("thresh",thresh)
  
  # find contours and get the external one
  contours = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
  if contours is not None:
    # contours = np.array(contours, dtype=np.float32)
    for cnt in contours[0]:
     
      area = cv2.contourArea(cnt)         
      if area > threshold_area:  
        # compute the center of the contour
        M = cv2.moments(cnt)
        if  M["m00"] != 0:
          cx = int(M["m10"] / M["m00"])
          cy = int(M["m01"] / M["m00"])
          cv2.circle(output, (cx, cy), 3, (255, 255, 255), -1)
          text_size = cv2.getTextSize(color_name, font, font_scale, font_thickness)
          text_width = text_size[0][0]
          text_height = text_size[0][1]

          cv2.putText(output, color_name, (cx - int(text_width/2), cy), font, 0.5, (0,0,0), 4)
          cv2.putText(output, color_name, (cx - int(text_width/2), cy), font, 0.5, font_color, 2)

        x,y,w,h = cv2.boundingRect(cnt)
        cv2.rectangle(output, (x,y),(x + w, y + h), (b, g, r), 2)

  return output

def main(args):
  
  # load the image
  original = cv2.imread(args["image"])
  original_resized =image_resize(original, height = 400)

  n_colors = 6
  arr = original_resized.reshape((-1, 3))
  kmeans = KMeans(n_clusters=n_colors, random_state=42).fit(arr)
  labels = kmeans.labels_
  centers = kmeans.cluster_centers_
  less_colors = centers[labels].reshape(original_resized.shape).astype('uint8')

  rgbs = [
    (255, 255, 0, 'yellow'),
    (255, 0, 0, 'red'), 
    (0, 255, 0, 'green'), 
    (0,  0, 255, 'blue'),
  ]

  output = original_resized.copy()

  # loop over the boundaries
  for rgb in rgbs:
    
    r, g , b, color_name = rgb

    # create NumPy arrays from the boundaries
    lowerBGR = np.array([b - threshold_RGB, g - threshold_RGB, r - threshold_RGB])
    upperBGR = np.array([b + threshold_RGB, g + threshold_RGB, r + threshold_RGB])
    
    # find the colors within the specified boundaries and apply
    # the mask
    mask = cv2.inRange(less_colors, lowerBGR, upperBGR)
    masked_image = cv2.bitwise_and(less_colors, less_colors, mask = mask)

    #cv2.imshow("masked_image:"+color_name, masked_image)
    #cv2.waitKey(0)
    output = name_color(masked_image, output, rgb)

  cv2.imshow("images", np.hstack([original_resized, output]))
  cv2.waitKey(0)


if __name__ == '__main__':
  import argparse
  # construct the argument parse and parse the arguments
  ap = argparse.ArgumentParser()
  ap.add_argument("-i", "--image", help = "path to the image")
  args = vars(ap.parse_args())
  main(args)