Changed image-manip to ImageWrapper class

scripts/imageWrapper.py

@@ -0,0 +1,69 @@
+import cv2
+import math
+import random
+
+import numpy as np
+
+
+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]
+
+    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]
+        return new_name
+
+    def crop_image(self):
+        print("Cropping image to 480x480...")
+        cropped_image = self.image[0:480, 120:600]
+        return cropped_image
+
+    def should_activate(self):
+        PROB_THRESHHOLD = 0.5
+        random_number = random.uniform(0.1,1.0)
+        print("Probability is %f..." % random_number)
+        if(random_number > PROB_THRESHHOLD):
+            print("Activating...")
+            return True
+        print("Did not activate...")
+        return False
+
+    def flip_image(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)
+
+    def rotate_image(self):
+        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)
+
+    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
+
+        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)
+
+
+