[Steph] Updated imagewrapper to take parameters for crop

data/imageWrapper.py

@@ -1,5 +1,6 @@
 import cv2
 import math
+import os
 import random
 
 import numpy as np
@@ -11,18 +12,41 @@ class ImageWrapper:
     def __init__(self, image, root_directory):
         self.image = cv2.imread(image,1)
         self.root_directory = root_directory
-        self.height, self.width, self.channels = image.shape
-        self.name = self.root_directory.split('/')[-1]
+        self.height, self.width, self.channels = self.image.shape
+        self.name = image.split('/')[-1]
 
     def get_name_path(self, process):
         name_list = self.name.split('.')
         name_list.insert(1, process)
-        new_name = self.root_directory + '_'.join(name_list[:2]) + '.' + name_list[2]
+        process_directory = self.root_directory + "/" + process
+        new_name =  process_directory + "/" + '_'.join(name_list[:2]) + '.' + name_list[2]
+        if not os.path.exists(process_directory):
+            os.makedirs(process_directory)
         return new_name
 
-    def crop(self):
-        print("Cropping image to 480x480...")
-        cropped_image = self.image[0:480, 120:600]
+    def get_crop_measurements(self, height, width):
+        if self.width > width:
+            width_difference = self.width - width
+            width_adjustment = width_difference/2
+        else:
+            width_adjustment = 0
+
+        if self.height > height:
+            height_difference = self.height - height
+            height_adjustment = height_difference/2
+        else:
+            height_adjustment = 0
+
+        return int(width_adjustment), int(height_adjustment)
+
+    def crop(self, height, width):
+        print("Cropping image to %dx%d..." % (height, width))
+        width_adjustment, height_adjustment = self.get_crop_measurements(height, width)
+        width_left = 0 + width_adjustment
+        width_right = self.width - width_adjustment
+        height_top = 0 + height_adjustment
+        height_bottom = self.height - height_adjustment
+        cropped_image = self.image[height_top:height_bottom, width_left:width_right]
         return cropped_image
 
     def should_activate(self):
@@ -37,42 +61,40 @@ def should_activate(self):
 
     def flip(self):
         print("Flipping...")
-        if self.should_activate():
-            flipped_image = cv2.flip(self.image, 1)
-            image_name = self.get_name_path('flip')
-            cv2.imwrite(image_name, flipped_image)
+        flipped_image = cv2.flip(self.image, 1)
+        image_name = self.get_name_path('flip')
+        cv2.imwrite(image_name, flipped_image)
 
-    def rotate(self):
+    def rotate(self, degrees_rotation=90):
         print("Rotating...")
         rows, cols, channels = self.image.shape
-        if self.should_activate():
-            rotation_matrix = cv2.getRotationMatrix2D((cols/2, rows/2), 90, 1)
-            rotated_image = cv2.warpAffine(self.image, rotation_matrix, (cols, rows))
-            image_name = self.get_name_path('rotate')
-            cv2.imwrite(image_name, rotated_image)
+        rotation_matrix = cv2.getRotationMatrix2D((cols/2, rows/2), degrees_rotation, 1)
+        rotated_image = cv2.warpAffine(self.image, rotation_matrix, (cols, rows))
+        image_name = self.get_name_path('rotate')
+        print(image_name)
+        cv2.imwrite(image_name, rotated_image)
 
     def add_noise(self):
         print("Adding noise...")
         rgb_img = cv2.cvtColor(self.image, cv2.COLOR_BGR2RGB)
         height, width, channels = rgb_img.shape
-        print("H: %d\tW: %d\tC:%d" % (height, width, channels))
         noise = np.random.randint(0,50, (height, width))
         jitter = np.zeros_like(rgb_img)
         jitter[:,:,1] = noise
+        image_name = self.get_name_path('noisy')
+        noisy_image = cv2.add(self.image, jitter)
+        combined = np.vstack((self.image[:math.ceil(height/2),:,:], noisy_image[math.ceil(height/2):,:,:]))
+        print(image_name)
+        cv2.imwrite(image_name,combined)
 
-        if self.should_activate(self):
-            image_name = self.get_name_path('noisy')
-            noisy_image = cv2.add(self.image, jitter)
-            combined = np.vstack((self.image[:math.ceil(height/2),:,:], noisy_image[math.ceil(height/2):,:,:]))
-            cv2.imwrite(image_name,combined)
-
-    def brighten(self):
+    def brighten(self, max_brighten=13):
         print("Brightening...")
         image_for_brightening = Image.fromarray(self.image)
         enhancer = ImageEnhance.Brightness(image_for_brightening)
-        factor = random.randint(0, 13)/4.0
+        factor = random.randint(0, max_brighten)/4.0
         brightened = enhancer.enhance(factor)
-        image_name = self.get_name_path(self, 'brighten')
+        image_name = self.get_name_path('brighten')
+        print(type(brightened))
         cv2.imwrite(image_name,brightened)