import cv2
import numpy as np
import copy


def _rand_scale(scale):
    scale = np.random.uniform(1, scale)
    return scale if np.random.randint(2) == 0 else 1./scale


def _constrain(min_v, max_v, value):

    if value < min_v:
        return min_v

    if value > max_v:
        return max_v

    return value 


def random_flip(image, flip):
    if flip == 1:
        return cv2.flip(image, 1)
    return image


def correct_bounding_boxes(boxes, new_w, new_h, net_w, net_h, dx, dy, flip, image_w, image_h):
    boxes = copy.deepcopy(boxes)

    # randomize boxes' order
    np.random.shuffle(boxes)

    # correct sizes and positions
    sx, sy = float(new_w)/image_w, float(new_h)/image_h
    zero_boxes = []

    for i in range(len(boxes)):
        boxes[i]['xmin'] = int(_constrain(0, net_w, boxes[i]['xmin']*sx + dx))
        boxes[i]['xmax'] = int(_constrain(0, net_w, boxes[i]['xmax']*sx + dx))
        boxes[i]['ymin'] = int(_constrain(0, net_h, boxes[i]['ymin']*sy + dy))
        boxes[i]['ymax'] = int(_constrain(0, net_h, boxes[i]['ymax']*sy + dy))

        if boxes[i]['xmax'] <= boxes[i]['xmin'] or boxes[i]['ymax'] <= boxes[i]['ymin']:
            zero_boxes += [i]
            continue

        if flip == 1:
            swap = boxes[i]['xmin']
            boxes[i]['xmin'] = net_w - boxes[i]['xmax']
            boxes[i]['xmax'] = net_w - swap

    boxes = [boxes[i] for i in range(len(boxes)) if i not in zero_boxes]

    return boxes


def random_distort_image(image, hue=18, saturation=1.5, exposure=1.5):
    # determine scale factors
    dhue = np.random.uniform(-hue, hue)
    dsat = _rand_scale(saturation)
    dexp = _rand_scale(exposure)

    # convert RGB space to HSV space
    image = cv2.cvtColor(image, cv2.COLOR_RGB2HSV).astype('float')
    
    # change satuation and exposure
    image[:, :, 1] *= dsat
    image[:, :, 2] *= dexp
    
    # change hue
    image[:, :, 0] += dhue
    image[:, :, 0] -= (image[:, :, 0] > 180) * 180
    image[:, :, 0] += (image[:, :, 0] < 0)   * 180
    
    # convert back to RGB from HSV
    return cv2.cvtColor(image.astype('uint8'), cv2.COLOR_HSV2RGB)


def apply_random_scale_and_crop(image, new_w, new_h, net_w, net_h, dx, dy):

    im_sized = cv2.resize(image, (new_w, new_h))
    
    if dx > 0: 
        im_sized = np.pad(im_sized, ((0, 0), (dx, 0), (0, 0)), mode='constant', constant_values=127)
    else:
        im_sized = im_sized[:, -dx:, :]
    if (new_w + dx) < net_w:
        im_sized = np.pad(im_sized, ((0, 0), (0, net_w - (new_w+dx)), (0, 0)), mode='constant', constant_values=127)
               
    if dy > 0: 
        im_sized = np.pad(im_sized, ((dy, 0), (0, 0), (0, 0)), mode='constant', constant_values=127)
    else:
        im_sized = im_sized[-dy:, :, :]
        
    if (new_h + dy) < net_h:
        im_sized = np.pad(im_sized, ((0, net_h - (new_h+dy)), (0, 0), (0, 0)), mode='constant', constant_values=127)
        
    return im_sized[:net_h, :net_w, :]
