Merge branch 'master' of https://github.com/Dekker1/ResearchMethods

mini_proj/Load_Images.py

@@ -6,6 +6,21 @@ import numpy as np
 from matplotlib import pyplot as plt
 import math
 import cv2
+from skimage import color, exposure, transform
+
+'''
+Defines some basic preprocessing for the images
+'''
+def preprocess(img):
+    # Histogram normalization in v channel
+    hsv = color.rgb2hsv(img)
+    hsv[:, :, 2] = exposure.equalize_hist(hsv[:, :, 2])
+    img = color.hsv2rgb(hsv)
+
+    img = img/255                           # Scaling images down to values of 0-255
+    img = np.rollaxis(img, -1)              # rolls colour axis to 0 
+
+    return img
 
 def gen_data(w_path, n_w_path):
     waldo_file_list = os.listdir(os.path.join(w_path))
@@ -15,12 +30,12 @@ def gen_data(w_path, n_w_path):
     imgs_raw = []           # Images
     imgs_lbl = []           # Image labels
 
-    #imgs_raw = np.array([np.array(imread(wdir + "waldo/"+fname)) for fname in os.listdir(wdir + "waldo")])
     i = 0
     for image_name in waldo_file_list:
         pic = cv2.imread(os.path.join(w_path, image_name))              # NOTE: cv2.imread() returns a numpy array in BGR not RGB
-        pic = pic/255                                                   # Scaling images down to values of 0-255
-        imgs_raw.append(np.rollaxis(pic, -1))                           # rolls colour axis to 0 
+        pic = preprocess(pic)
+        
+        imgs_raw.append(pic)
         imgs_lbl.append(1)                                              # Value of 1 as Waldo is present in the image
 
         print('Completed: {0}/{1} Waldo images'.format(i+1, total_w))
@@ -29,8 +44,9 @@ def gen_data(w_path, n_w_path):
     i = 0
     for image_name in not_waldo_file_list:
         pic = cv2.imread(os.path.join(n_w_path, image_name))
-        pic = pic/255                                                   # Scaling images down to values of 0-255        
-        imgs_raw.append(np.rollaxis(pic, -1))
+        pic = preprocess(pic)
+        
+        imgs_raw.append(pic)
         imgs_lbl.append(0)
         
         print('Completed: {0}/{1} non-Waldo images'.format(i+1, total_nw))

mini_proj/waldo_model.py

@@ -8,6 +8,7 @@ os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
 from keras.layers import Dense, Dropout, Activation, Flatten, Input
 from keras.layers import Conv2D, MaxPooling2D, ZeroPadding2D
 from keras.models import Model
+from keras import metrics
 
 from sklearn import svm, tree, naive_bayes, ensemble
 from sklearn.metrics import accuracy_score