Detect color in superpixel regions

colored_regions.py

Author: yashk2000

README.md

@@ -22,6 +22,7 @@
 20. [Detecting the centre of a geometric shape](https://github.com/yashk2000/Image-Processing#20-detecting-the-centre-of-a-geometric-shape)
 21. [Long Exposure](https://github.com/yashk2000/Image-Processing#21-long-exposure)
 22. [Segmentation into Superpixels](https://github.com/yashk2000/Image-Processing#22-segmentation-into-superpixels)
+23. [Colors in superpixels](https://github.com/yashk2000/Image-Processing#23-colors-in-superpixels)
 
 # 1) Opening an image using openCV
 
@@ -668,3 +669,11 @@ Image segmented into 200 superpixels
 Image segmented into 300 superpixels
 
 ![Screenshot from 2020-04-24 23-49-39](https://user-images.githubusercontent.com/41234408/80244625-e2617980-8686-11ea-9830-8693839bfb5f.png)
+
+# 23) Colors in superpixels
+
+The code for this can be found [here](https://github.com/yashk2000/Image-Processing/blob/master/colored_regions.py).
+
+This helps in detecting the saturation of colors in different parts of an image that have been segmented into superpixels. 
+
+![Screenshot from 2020-04-26 13-27-49](https://user-images.githubusercontent.com/41234408/80301559-7ab04900-87c2-11ea-9a65-3528779f2be8.png)

colored_regions.py

@@ -0,0 +1,53 @@
+from skimage.exposure import rescale_intensity
+from skimage.segmentation import slic
+from skimage.util import img_as_float
+from skimage import io
+import numpy as np
+import argparse
+import cv2
+
+def segment_colorfulness(image, mask):
+	(B, G, R) = cv2.split(image.astype("float"))
+	R = np.ma.masked_array(R, mask=mask)
+	G = np.ma.masked_array(B, mask=mask)
+	B = np.ma.masked_array(B, mask=mask)
+
+	rg = np.absolute(R - G)
+
+	yb = np.absolute(0.5 * (R + G) - B)
+
+	stdRoot = np.sqrt((rg.std() ** 2) + (yb.std() ** 2))
+	meanRoot = np.sqrt((rg.mean() ** 2) + (yb.mean() ** 2))
+
+	return stdRoot + (0.3 * meanRoot)
+
+ap = argparse.ArgumentParser()
+ap.add_argument("-i", "--image", required=True)
+ap.add_argument("-s", "--segments", type=int, default=100)
+args = vars(ap.parse_args())
+
+orig = cv2.imread(args["image"])
+vis = np.zeros(orig.shape[:2], dtype="float")
+
+image = io.imread(args["image"])
+segments = slic(img_as_float(image), n_segments=args["segments"],
+	slic_zero=True)
+
+for v in np.unique(segments):
+	mask = np.ones(image.shape[:2])
+	mask[segments == v] = 0
+
+	C = segment_colorfulness(orig, mask)
+	vis[segments == v] = C
+
+vis = rescale_intensity(vis, out_range=(0, 255)).astype("uint8")
+
+alpha = 0.6
+overlay = np.dstack([vis] * 3)
+output = orig.copy()
+cv2.addWeighted(overlay, alpha, output, 1 - alpha, 0, output)
+
+cv2.imshow("Input", orig)
+cv2.imshow("Visualization", vis)
+cv2.imshow("Output", output)
+cv2.waitKey(0)