zamber · April 3, 2017 17:14 · Apr 3, 2017 · Apr 3, 2017 · Apr 3, 2017
diff --git a/ness.py b/ness.py
@@ -0,0 +1,76 @@
+#!/usr/bin/env python2.7
+
+'''
+Based on:
+http://www.pyimagesearch.com/2015/09/07/blur-detection-with-opencv/
+
+opencv is available for python2.7 on Ubuntu only (in default repos)
+hence pip2 and above hashbang
+
+# Installation
+    sudo apt install python-opencv
+    sudo pip2 install numpy
+    sudo pip2 install imutils
+
+It's Dangerous to Go Alone! Take This
+(to drop to the interpreter in-line):
+    import code
+    code.interact(local=dict(globals(), **locals()))
+'''
+
+import sys
+import argparse
+from imutils import paths
+import cv2
+import numpy
+
+
+class MyParser(argparse.ArgumentParser):
+    def error(self, message):
+        sys.stderr.write('error: %s\n' % message)
+        self.print_help()
+        sys.exit(2)
+
+
+def create_parser():
+    parser = MyParser(description='Uber (sharp/bright)-ness CSVer')
+    parser.add_argument(
+        'image_dirs',
+        metavar='IMAGE_DIRS',
+        type=str,
+        nargs="+",
+        help='Directories containing images to parse and spew out'
+    )
+    return parser
+
+
+def analize_image(image_path):
+    # load the image, convert it to grayscale, and compute the
+    # focus measure of the image using the Variance of Laplacian
+    # method
+    image = cv2.imread(image_path.replace('\\', ''))
+    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
+    # compute the Laplacian of the image and then return the
+    # focus measure, which is the variance of the Laplacian
+    fm = cv2.Laplacian(gray, cv2.CV_64F).var()
+
+    # convert image to HSV, split out V, calculate a histogram
+    # for V (which is the value for brightness), then get a
+    # median for it
+    hsv = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)
+    v = cv2.split(hsv)
+    br_hist = cv2.calcHist(v, [2], None, [256], [0,256])
+    br_median = numpy.median(br_hist)
+
+    return image_path, fm, br_median
+
+
+if (__name__ == '__main__'):
+    parser = create_parser()
+    args = parser.parse_args()
+
+    print '"%s", "%s", "%s"' % ("image", "sharpness", "brightness")
+    for directory in args.image_dirs:
+        for image_path in paths.list_images(directory):
+            # Professional Output Generator (TM)
+            print '"%s", "%s", "%s"' % (analize_image(image_path))
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