Add some steps

mini_proj/test_nn.py

@@ -3,6 +3,7 @@ from keras.models import Model, load_model
 from keras.utils import to_categorical
 import cv2
 from skimage import color, exposure
+from _cutter import image_cut
 
 def man_result_check():
     pred_y = np.load("predicted_results.npy")
@@ -14,7 +15,7 @@ def man_result_check():
     z = 0
     for i in range(0, len(test_y)):
         print(pred_y[i], test_y[i], file=f)
-        # Calculates correct predictions 
+        # Calculates correct predictions
         if pred_y[i][0] == test_y[i][0]:
             z+=1
 
@@ -25,7 +26,7 @@ def man_result_check():
 Purpose:Loads a trained neural network model (using Keras) to classify an image
 Input:  path/to/trained_model
         image [or] path/to/image [if from_file=True]
-Returns:Boolean variable 
+Returns:Boolean variable
 '''
 def is_Wally(trained_model_path, image, from_file=False):
     if from_file:
@@ -50,5 +51,22 @@ def is_Wally(trained_model_path, image, from_file=False):
 # Mark Wally image somehow (colour the border)
 # Stitch original image back together
 
+if __name__ == '__main__':
+    # Read image
+    image = cv2.imread("10.jpg")
+    # Split image
+    cuts = image_cut(image, 64, 64)
+    for i in len(cuts):
+        # Transform block
+        hsv = color.rgb2hsv(cuts[i])
+        hsv[:, :, 2] = exposure.equalize_hist(hsv[:, :, 2])
+        block = color.hsv2rgb(hsv)
+        block = np.rollaxis(block, -1)
+        if is_Wally("Waldo.h5", block):
+            # Border block
+            cuts[i] = cv2.copyMakeBorder(cuts[i],5,5,5,5,cv2.BORDER_CONSTANT,value=RED)
 
-is_Wally("Waldo.h5", image)
+    # Stitch image TODO!
+    # Show image
+    cv.imshow('Image', image)
+    cv.waitKey(0)