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basic_image.py
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basic_image.py
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#!/usr/bin/env python
__author__ = 'Aleksandar Gyorev'
__email__ = 'a.gyorev@jacobs-university.de'
import cv2
import numpy as np
class BasicImage(object):
def __init__(self, _image_path):
if isinstance(_image_path, basestring) == True:
self.image = cv2.imread(_image_path)
else:
self.image = _image_path
def get(self):
return self.image
def get_size(self):
return self.image.shape
def show(self):
cv2.imshow('Basic Image', self.image)
cv2.waitKey(0)
def save(self, _image_path):
cv2.imwrite(_image_path, self.image)
def resize(self, _type, _size):
if _type == 'w' or _type == 'W':
ratio = float(_size) / self.image.shape[1]
dim = (_size, int(self.image.shape[0] * ratio))
elif _type == 'h' or _type == 'H':
ratio = float(_size) / self.image.shape[0]
dim = (int(self.image.shape[1] * ratio), _size)
else:
return self.image
result = cv2.resize(self.image, dim, interpolation = cv2.INTER_AREA)
return result
def rotate(self, _angle):
(height, width) = self.image.shape[:2]
center = (width / 2, height / 2)
# make it a 3x3 rotation matrix
M = np.vstack([cv2.getRotationMatrix2D(center, _angle, 1.0), [0, 0, 1]])
R = np.matrix(M[0:2, 0:2])
half_height = height / 2.0
half_width = width / 2.0
# coordinates of the rotated corners
rotated_corners = [
(np.array([-half_width, half_height]) * R).A[0],
(np.array([ half_width, half_height]) * R).A[0],
(np.array([-half_width,-half_height]) * R).A[0],
(np.array([ half_width,-half_height]) * R).A[0]]
# new image size
x_coords = [point[0] for point in rotated_corners]
y_coords = [point[1] for point in rotated_corners]
right_bound = max(x_coords)
left_bound = min(x_coords)
top_bound = max(y_coords)
bot_bound = min(y_coords)
new_height = int(abs(top_bound - bot_bound))
new_width = int(abs(right_bound - left_bound))
# translation matrix to keep it centered
T = np.matrix([
[1, 0, int(new_width / 2.0 - half_width)],
[0, 1, int(new_height / 2.0 - half_height)],
[0, 0, 1]])
# combining rotation and translation
M = (np.matrix(T) * np.matrix(M))[0:2, :]
result = cv2.warpAffine(self.image, M, (new_width, new_height), flags = cv2.INTER_LINEAR)
return result
def crop(self, _top, _bot, _left, _right):
result = self.image[_top:_bot + 1, _left:_right + 1]
return result