rotate_and_crop.py

import tensorflow as tf
import os, glob, h5py, scipy, random, cv2, time, math
from PIL import Image
import numpy as np
import matplotlib.pyplot as plt
import pickle 
from random import shuffle
# from fc4
def rotate_and_crop(image, angle):
  image_width, image_height = image.shape[:2]
  image_rotated = rotate_image(image, angle)
  image_rotated_cropped = crop_around_center(image_rotated,
                                             *largest_rotated_rect(
                                                 image_width, image_height,
                                                 math.radians(angle)))
  return image_rotated_cropped


def rotate_image(image, angle):
    """
    Rotates an OpenCV 2 / NumPy image about it's centre by the given angle
    (in degrees). The returned image will be large enough to hold the entire
    new image, with a black background
    """

    # Get the image size
    # No that's not an error - NumPy stores image matricies backwards
    image_size = (image.shape[1], image.shape[0])
    image_center = tuple(np.array(image_size) / 2)

    # Convert the OpenCV 3x2 rotation matrix to 3x3, vstack: stack two array along the vertical axis
    rot_mat = np.vstack(
        [cv2.getRotationMatrix2D(image_center, angle, 1.0), [0, 0, 1]])

    rot_mat_notranslate = np.matrix(rot_mat[0:2, 0:2])

    # Shorthand for below calcs
    image_w2 = image_size[0] * 0.5
    image_h2 = image_size[1] * 0.5

    # Obtain the rotated coordinates of the image corners
    rotated_coords = [
        (np.array([-image_w2, image_h2]) * rot_mat_notranslate).A[0],
        (np.array([image_w2, image_h2]) * rot_mat_notranslate).A[0],
        (np.array([-image_w2, -image_h2]) * rot_mat_notranslate).A[0],
        (np.array([image_w2, -image_h2]) * rot_mat_notranslate).A[0]]

    # Find the size of the new image
    x_coords = [pt[0] for pt in rotated_coords]
    x_pos = [x for x in x_coords if x > 0]
    x_neg = [x for x in x_coords if x < 0]

    y_coords = [pt[1] for pt in rotated_coords]
    y_pos = [y for y in y_coords if y > 0]
    y_neg = [y for y in y_coords if y < 0]

    right_bound = max(x_pos)
    left_bound = min(x_neg)
    top_bound = max(y_pos)
    bot_bound = min(y_neg)

    new_w = int(abs(right_bound - left_bound))
    new_h = int(abs(top_bound - bot_bound))

    # We require a translation matrix to keep the image centred
    trans_mat = np.matrix([[1, 0, int(new_w * 0.5 - image_w2)],
                            [0, 1, int(new_h * 0.5 - image_h2)], [0, 0, 1]])

    # Compute the tranform for the combined rotation and translation
    affine_mat = (np.matrix(trans_mat) * np.matrix(rot_mat))[0:2, :]

    # Apply the transform
    result = cv2.warpAffine(
        image, affine_mat, (new_w, new_h), flags=cv2.INTER_LINEAR)

    return result


def crop_around_center(image, width, height):
  """
    Given a NumPy / OpenCV 2 image, crops it to the given width and height,
    around it's centre point
    """

  image_size = (image.shape[1], image.shape[0])
  image_center = (int(image_size[0] * 0.5), int(image_size[1] * 0.5))

  if (width > image_size[0]):
    width = image_size[0]

  if (height > image_size[1]):
    height = image_size[1]

  x1 = int(image_center[0] - width * 0.5)
  x2 = int(image_center[0] + width * 0.5)
  y1 = int(image_center[1] - height * 0.5)
  y2 = int(image_center[1] + height * 0.5)

  return image[y1:y2, x1:x2]
  


def largest_rotated_rect(w, h, angle):
  """
    Given a rectangle of size wxh that has been rotated by 'angle' (in
    radians), computes the width and height of the largest possible
    axis-aligned rectangle within the rotated rectangle.

    Original JS code by 'Andri' and Magnus Hoff from Stack Overflow

    Converted to Python by Aaron Snoswell
    """

  quadrant = int(math.floor(angle / (math.pi / 2))) & 3
  sign_alpha = angle if ((quadrant & 1) == 0) else math.pi - angle
  alpha = (sign_alpha % math.pi + math.pi) % math.pi

  bb_w = w * math.cos(alpha) + h * math.sin(alpha)
  bb_h = w * math.sin(alpha) + h * math.cos(alpha)

  gamma = math.atan2(bb_w, bb_w) if (w < h) else math.atan2(bb_w, bb_w)

  delta = math.pi - alpha - gamma

  length = h if (w < h) else w

  d = length * math.cos(alpha)
  a = d * math.sin(alpha) / math.sin(delta)

  y = a * math.cos(gamma)
  x = y * math.tan(gamma)

  return (bb_w - 2 * x, bb_h - 2 * y)