Implemented basic focus detection, closes #5

src/focusdetection/focusdetection.py

@@ -0,0 +1,49 @@
+import argparse
+import os
+from os.path import abspath
+import cv2
+import pandas
+import numpy
+import matplotlib.pyplot as plt
+
+#parser = argparse.ArgumentParser()
+#parser.add_argument('images', type=os.path.abspath, metavar='image-location', nargs='+',
+#                    help='path(s) to input image(s)')
+#args = vars(parser.parse_args())
+#
+#imagePaths = args["images"]
+
+def laplacian_variance(image):
+    return cv2.Laplacian(image, cv2.CV_64F).var()
+
+def fast_fourier(image, size=60, thresh=10):
+    (h, w) = image.shape
+    (xCenter, yCenter) = (int(w/ 2), int(h/2))
+    fft = numpy.fft.fft2(image)
+    fftShift = numpy.fft.fftshift(fft)
+    fftShift[yCenter -size:yCenter + size, xCenter - size:xCenter + size] = 0
+    fftShift = numpy.fft.ifftshift(fftShift)
+    recon = numpy.fft.ifft2(fftShift)
+    magnitude = 20 * numpy.log(numpy.abs(recon))
+    mean = numpy.mean(magnitude)
+    return (mean, mean <= thresh)
+
+def laplacian_variance_method():
+    column_labels =  ['Path', 'Laplacian Variance']
+    df = pandas.DataFrame(columns = column_labels)
+    index = 0
+
+    for imagePath in imagePaths:
+        index += 1
+        if index % 10 == 0:
+            print("Processing image " + str(index) + "/" + str(len(imagePaths)))
+        image = cv2.imread(imagePath)
+        image_grey = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
+        var = laplacian_variance(image_grey)
+        df.loc[len(df.index)] = [imagePath, var]
+
+    mean_lap_var = df["Laplacian Variance"].mean()
+    df_not_blurred = df[df['Laplacian Variance']>mean_lap_var]
+    df_blurred = df[df['Laplacian Variance']<=mean_lap_var]
+
+#def fast_fourier_transform_method():