added color detector

Color Detection/Model/color_detection.py

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+import cv2
+import pandas as pd
+
+# --------------------------------------------------------------------------
+
+img_path = 'pic1.jpg'
+csv_path = 'colors.csv'
+
+# reading csv file
+index = ['color', 'color_name', 'hex', 'R', 'G', 'B']
+df = pd.read_csv(csv_path, names=index, header=None)
+
+# reading image
+img = cv2.imread(img_path)
+img = cv2.resize(img, (800,600))
+
+#declaring global variables
+clicked = False
+r = g = b = xpos = ypos = 0
+
+#function to calculate minimum distance from all colors and get the most matching color
+def get_color_name(R,G,B):
+	minimum = 1000
+	for i in range(len(df)):
+		d = abs(R - int(df.loc[i,'R'])) + abs(G - int(df.loc[i,'G'])) + abs(B - int(df.loc[i,'B']))
+		if d <= minimum:
+			minimum = d
+			cname = df.loc[i, 'color_name']
+
+	return cname
+
+#function to get x,y coordinates of mouse double click
+def draw_function(event, x, y, flags, params):
+	if event == cv2.EVENT_LBUTTONDBLCLK:
+		global b, g, r, xpos, ypos, clicked
+		clicked = True
+		xpos = x
+		ypos = y
+		b,g,r = img[y,x]
+		b = int(b)
+		g = int(g)
+		r = int(r)
+
+# creating window
+cv2.namedWindow('image')
+cv2.setMouseCallback('image', draw_function)
+
+while True:
+	cv2.imshow('image', img)
+	if clicked:
+		#cv2.rectangle(image, startpoint, endpoint, color, thickness)-1 fills entire rectangle 
+		cv2.rectangle(img, (20,20), (600,60), (b,g,r), -1)
+
+		#Creating text string to display( Color name and RGB values )
+		text = get_color_name(r,g,b) + ' R=' + str(r) + ' G=' + str(g) + ' B=' + str(b)
+		#cv2.putText(img,text,start,font(0-7),fontScale,color,thickness,lineType )
+		cv2.putText(img, text, (50,50), 2,0.8, (255,255,255),2,cv2.LINE_AA)
+
+		#For very light colours we will display text in black colour
+		if r+g+b >=600:
+			cv2.putText(img, text, (50,50), 2,0.8, (0,0,0),2,cv2.LINE_AA)
+
+	if cv2.waitKey(20) & 0xFF == 27:
+		break
+
+cv2.destroyAllWindows()

Color Detection/README.md

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+# Colour Identification using Machine Learning
+
+A very common task in Computer Vision is based on identifying colours.
+More generally, a fascinating aspect of learning is that sometimes we know things but we can’t explain them. 
+For example, if I ask you to describe me the “red colour” your only option is to show me a red object, but you can’t really explain what “red” is.
+For this reason a certain effort is necessary to transmit the definition of a colour to a computer.
+
+**DATASET**
+https://www.kaggle.com/adityabhndari/color-detection-data-set
+
+**WHAT I HAD DONE**
+I have used OpenCV and Pandas library to choose a point on any particular image and calculate the RGB of that point and match it with the nearest colour in the dataset to predict the colour.
+
+**LIBRARIES NEEDED**
+OpenCV and Pandas
+
+Original Pic:
+
+![pic1](https://user-images.githubusercontent.com/58680590/122101277-ba584b00-ce31-11eb-9a98-36e83a1a4e45.jpg)
+
+Result:
+
+![result3](https://user-images.githubusercontent.com/58680590/122101306-c17f5900-ce31-11eb-8fd9-780ee00f764a.png)