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randomize_bg.py
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# -*- coding: utf-8 -*-
"""
Created on Thu Nov 21 12:03:31 2019
@author: Mat Tun
"""
import numpy as np
import matplotlib.pyplot as plt
import glob
import random
from PIL import Image
import cv2
import os
from torchvision import transforms
from mask_green_background_bbox import rgba_from_rgb_green_bg,crop_hand_by_bbox
class BG_randomizer():
def __init__(self,bg_path,max_width,max_hight,randomization_prob = 0.9,photo_bg_prob = 0.9):
# randomization_prob determines the probability that green bg is swapped for different bg per image
# photo_bg_prob determines the probability that green bg is swapped with random photo, othervise bg is swapped for random uniform color
self.paths_images = []
self.randomization_prob = randomization_prob
self.photo_bg_prob = photo_bg_prob
self.slash = os.sep
self.source_folder = bg_path + self.slash
self.max_width = max_width
self.max_hight = max_hight
if bg_path != '':
for filename in glob.iglob(self.source_folder + '**/*.jpg', recursive=True):
self.paths_images.append(os.path.abspath(filename))
# debug code (find out how lowering number of bacground images impact accuracy)
# does not seem to make much difference
# no_background_fraction = 0.01
#
# self.paths_images = random.sample(self.paths_images,int(len(self.paths_images)*no_background_fraction))
# print(len(self.paths_images))
# define torch transforms
self.color_jitter = transforms.ColorJitter(0.8, 0.8, 0.8, 0.2)
self.rnd_color_jitter = transforms.RandomApply([self.color_jitter], p=0.8)
self.rnd_gray = transforms.RandomGrayscale(p=0.2)
def randomize_bg(self,image,bb2d,centroid2d,crop):
# SOURCE 1: https://stackoverflow.com/questions/52179821/python-3-i-am-trying-to-find-find-all-green-pixels-in-an-image-by-traversing-al
# SOURCE 2: https://github.com/kimmobrunfeldt/howto-everything/blob/master/remove-green.md
# Open image and make RGB and HSV versions
RGBim = image.convert('RGB')
HSVim = RGBim.convert('HSV')
# Make numpy versions
RGBna = np.array(RGBim)
HSVna = np.array(HSVim)
if random.uniform(0,1) < self.randomization_prob:
if random.uniform(0,1) < self.photo_bg_prob:
bg_path = random.sample(self.paths_images,1)[0]
bg_im = Image.open(bg_path)
bg_im = bg_im.convert('RGB')
bg_im = bg_im.resize((RGBna.shape[1],RGBna.shape[0]),resample = Image.BILINEAR)
bg_im = np.array(bg_im)
else:
bg_im = np.ones((RGBna.shape[0],RGBna.shape[1],3),dtype = np.uint8)
bg_im[:,:,0] = bg_im[:,:,0]*random.randint(0,255)
bg_im[:,:,1] = bg_im[:,:,1]*random.randint(0,255)
bg_im[:,:,2] = bg_im[:,:,2]*random.randint(0,255)
else:
return RGBim,bb2d,centroid2d
# Extract Hue
H = HSVna[:,:,0]
# Extract Saturation
S = HSVna[:,:,1]
# Extract Value
V = HSVna[:,:,2]
# Find all green pixels, i.e. where 100 < Hue < 140
lo_h,hi_h = 100,140
lo_s,hi_s = 80,255
lo_v,hi_v = 70,255
# Rescale S to 0-255, rather than 0-360 because we are using uint8
lo_h = int((lo_h / 360) * 255)
hi_h = int((hi_h / 360) * 255)
green = np.where((H>=lo_h) & (H<=hi_h) & (S>=lo_s) & (S<=hi_s) & (V>=lo_v) & (V<=hi_v))
RGBna[green] = bg_im[green]
out = Image.fromarray(RGBna)
if crop:
out,bb2d,centroid2d = self.random_crop(out,bb2d,centroid2d)
out,bb2d,centroid2d = self.flip_lr(out,bb2d,centroid2d)
return out,bb2d,centroid2d
def randomize_bg_bbox(self,image,pose,bb2d,centroid2d,crop):
RGBimage = image.convert('RGB')
RGBna = np.array(RGBimage)
if random.uniform(0,1) < self.randomization_prob:
if random.uniform(0,1) < self.photo_bg_prob:
bg_path = random.sample(self.paths_images,1)[0]
bg_im = Image.open(bg_path)
bg_im = bg_im.convert('RGB')
bg_im = bg_im.resize((RGBna.shape[1],RGBna.shape[0]),resample = Image.BILINEAR)
bg_im = np.array(bg_im)
else:
bg_im = np.ones((RGBna.shape[0],RGBna.shape[1],3),dtype = np.uint8)
bg_im[:,:,0] = bg_im[:,:,0]*random.randint(0,255)
bg_im[:,:,1] = bg_im[:,:,1]*random.randint(0,255)
bg_im[:,:,2] = bg_im[:,:,2]*random.randint(0,255)
else:
return RGBimage,bb2d,centroid2d
rgba, mask_green = rgba_from_rgb_green_bg(RGBna)
rgba, mask_bbox = crop_hand_by_bbox(rgba, pose)
mask = mask_green * mask_bbox
# rgb = np.stack([rgba[:, :, 2],rgba[:, :, 1],rgba[:, :, 0]],axis=2).astype(np.uint8)
rgb = np.stack([rgba[:, :, 0],rgba[:, :, 1],rgba[:, :, 2]],axis=2).astype(np.uint8)
mask = np.stack([mask,mask,mask],axis=2)
rgb = mask * rgb + (1. - mask) * bg_im
rgb = rgb.astype(np.uint8)
out = Image.fromarray(rgb)
if crop:
out,bb2d,centroid2d = self.random_crop(out,bb2d,centroid2d)
out,bb2d,centroid2d = self.flip_lr(out,bb2d,centroid2d)
return out,bb2d,centroid2d
def randomize_bg_bbox_from_mask(self,image,mask,pose,bb2d,centroid2d,crop):
RGBimage = image.convert('RGB')
RGBna = np.array(RGBimage)
if random.uniform(0,1) < self.randomization_prob:
if random.uniform(0,1) < self.photo_bg_prob:
bg_path = random.sample(self.paths_images,1)[0]
bg_im = Image.open(bg_path)
bg_im = bg_im.convert('RGB')
bg_im = bg_im.resize((RGBna.shape[1],RGBna.shape[0]),resample = Image.BILINEAR)
bg_im = np.array(bg_im)
else:
bg_im = np.ones((RGBna.shape[0],RGBna.shape[1],3),dtype = np.uint8)
bg_im[:,:,0] = bg_im[:,:,0]*random.randint(0,255)
bg_im[:,:,1] = bg_im[:,:,1]*random.randint(0,255)
bg_im[:,:,2] = bg_im[:,:,2]*random.randint(0,255)
else:
return RGBimage,bb2d,centroid2d
mask = np.array(mask)
# print(mask.shape)
mask = mask[:,:,3]
mask = np.stack([mask,mask,mask],axis=2)/255.
out = mask * RGBna + (1. - mask) * bg_im
out = out.astype(np.uint8)
out = Image.fromarray(out)
if crop:
out,bb2d,centroid2d = self.random_crop(out,bb2d,centroid2d)
out,bb2d,centroid2d = self.flip_lr(out,bb2d,centroid2d)
return out,bb2d,centroid2d
def randomize_bg_bbox_from_mask_empty_scene(self,image,image_empty_scene,mask,pose,bb2d,centroid2d,crop):
RGBimage = image.convert('RGB')
RGBna = np.array(RGBimage)
if random.uniform(0,1) < self.randomization_prob:
if random.uniform(0,1) < self.photo_bg_prob:
bg_path = random.sample(self.paths_images,1)[0]
bg_im = Image.open(bg_path)
bg_im = bg_im.convert('RGB')
bg_im = bg_im.resize((RGBna.shape[1],RGBna.shape[0]),resample = Image.BILINEAR)
bg_im = np.array(bg_im)
else:
# add bacground image of empty table
bg_im = Image.open(image_empty_scene)
bg_im = bg_im.convert('RGB')
bg_im = bg_im.resize((RGBna.shape[1],RGBna.shape[0]),resample = Image.BILINEAR)
bg_im = np.array(bg_im)
else:
return RGBimage,bb2d,centroid2d
mask = np.array(mask)
# print(mask.shape)
mask = mask[:,:,3]
mask = np.stack([mask,mask,mask],axis=2)/255.
out = mask * RGBna + (1. - mask) * bg_im
out = out.astype(np.uint8)
out = Image.fromarray(out)
if crop:
out,bb2d,centroid2d = self.random_crop(out,bb2d,centroid2d)
out,bb2d,centroid2d = self.flip_lr(out,bb2d,centroid2d)
return out,bb2d,centroid2d
# this version is the same as previous version but randomization is NOT applied to bg image only to foreground
def randomize_bg_bbox_from_mask_empty_scene_no_bg_aug(self,image,image_empty_scene,mask,pose,bb2d,centroid2d,crop):
RGBimage = image.convert('RGB')
RGBna = np.array(RGBimage)
if random.uniform(0,1) < self.randomization_prob:
if random.uniform(0,1) < self.photo_bg_prob:
bg_path = random.sample(self.paths_images,1)[0]
bg_im = Image.open(bg_path)
bg_im = bg_im.convert('RGB')
bg_im = bg_im.resize((RGBna.shape[1],RGBna.shape[0]),resample = Image.BILINEAR)
bg_im = np.array(bg_im)
else:
# add bacground image of empty table
bg_im = Image.open(image_empty_scene)
bg_im = bg_im.convert('RGB')
bg_im = bg_im.resize((RGBna.shape[1],RGBna.shape[0]),resample = Image.BILINEAR)
bg_im = np.array(bg_im)
else:
return RGBimage,bb2d,centroid2d
mask = np.array(mask)
# print(mask.shape)
mask = mask[:,:,3]
mask = np.stack([mask,mask,mask],axis=2)
if crop:
RGBna = Image.fromarray(RGBna)
mask = Image.fromarray(mask)
RGBna,mask,bb2d,centroid2d = self.random_crop_w_mask(RGBna,mask,bb2d,centroid2d)
RGBna,mask,bb2d,centroid2d = self.flip_lr_w_mask(RGBna,mask,bb2d,centroid2d)
RGBna = np.array(RGBna)
mask = np.array(mask)/255.
out = mask * RGBna + (1. - mask) * bg_im
out = out.astype(np.uint8)
out = Image.fromarray(out)
return out,bb2d,centroid2d
def randomize_bg_bbox_from_mask_50_50_blend(self,image,image_empty_scene,mask,pose,bb2d,centroid2d,crop):
RGBimage = image.convert('RGB')
RGBna = np.array(RGBimage)
if random.uniform(0,1) < self.randomization_prob:
if random.uniform(0,1) < self.photo_bg_prob:
bg_path = random.sample(self.paths_images,1)[0]
bg_im = Image.open(bg_path)
bg_im = bg_im.convert('RGB')
bg_im = bg_im.resize((RGBna.shape[1],RGBna.shape[0]),resample = Image.BILINEAR)
bg_im = np.array(bg_im)
else:
bg_path = random.sample(self.paths_images,1)[0]
bg_im_random = Image.open(bg_path)
bg_im_random = bg_im_random.convert('RGB')
bg_im_random = bg_im_random.resize((RGBna.shape[1],RGBna.shape[0]),resample = Image.BILINEAR)
bg_im_random = np.array(bg_im_random).astype(np.uint8)
bg_im_empty_bg = Image.open(image_empty_scene)
bg_im_empty_bg = bg_im_empty_bg.convert('RGB')
bg_im_empty_bg = bg_im_empty_bg.resize((RGBna.shape[1],RGBna.shape[0]),resample = Image.BILINEAR)
bg_im_empty_bg = np.array(bg_im_empty_bg).astype(np.uint8)
bg_im_random = Image.fromarray(bg_im_random)
bg_im_empty_bg = Image.fromarray(bg_im_empty_bg)
bg_im = Image.blend(bg_im_empty_bg,bg_im_random,0.5)
bg_im = np.array(bg_im)
else:
return RGBimage,bb2d,centroid2d
mask = np.array(mask)
# print(mask.shape)
mask = mask[:,:,3]
mask = np.stack([mask,mask,mask],axis=2)/255.
out = mask * RGBna + (1. - mask) * bg_im
out = out.astype(np.uint8)
out = Image.fromarray(out)
if crop:
out,bb2d,centroid2d = self.random_crop(out,bb2d,centroid2d)
out,bb2d,centroid2d = self.flip_lr(out,bb2d,centroid2d)
return out,bb2d,centroid2d
def randomize_bg_bbox_from_mask_50_50_random_blend(self,image,image_empty_scene,mask,pose,bb2d,centroid2d,crop):
RGBimage = image.convert('RGB')
RGBna = np.array(RGBimage)
if random.uniform(0,1) < self.randomization_prob:
if random.uniform(0,1) < self.photo_bg_prob:
bg_path = random.sample(self.paths_images,1)[0]
bg_im = Image.open(bg_path)
bg_im = bg_im.convert('RGB')
bg_im = bg_im.resize((RGBna.shape[1],RGBna.shape[0]),resample = Image.BILINEAR)
bg_im = np.array(bg_im)
else:
bg_path = random.sample(self.paths_images,1)[0]
bg_im_random = Image.open(bg_path)
bg_im_random = bg_im_random.convert('RGB')
bg_im_random = bg_im_random.resize((RGBna.shape[1],RGBna.shape[0]),resample = Image.BILINEAR)
bg_im_random = np.array(bg_im_random).astype(np.uint8)
bg_im_empty_bg = Image.open(image_empty_scene)
bg_im_empty_bg = bg_im_empty_bg.convert('RGB')
bg_im_empty_bg = bg_im_empty_bg.resize((RGBna.shape[1],RGBna.shape[0]),resample = Image.BILINEAR)
bg_im_empty_bg = np.array(bg_im_empty_bg).astype(np.uint8)
bg_im_random = Image.fromarray(bg_im_random)
bg_im_empty_bg = Image.fromarray(bg_im_empty_bg)
rand_blend_ratio = round(random.uniform(0,1),2)
bg_im = Image.blend(bg_im_empty_bg,bg_im_random,rand_blend_ratio)
bg_im = np.array(bg_im)
else:
return RGBimage,bb2d,centroid2d
mask = np.array(mask)
# print(mask.shape)
mask = mask[:,:,3]
mask = np.stack([mask,mask,mask],axis=2)/255.
out = mask * RGBna + (1. - mask) * bg_im
out = out.astype(np.uint8)
out = Image.fromarray(out)
if crop:
out,bb2d,centroid2d = self.random_crop(out,bb2d,centroid2d)
out,bb2d,centroid2d = self.flip_lr(out,bb2d,centroid2d)
return out,bb2d,centroid2d
def randomize_crop(self,image,bb2d,centroid2d):
if random.uniform(0,1) < self.randomization_prob:
image,bb2d,centroid2d = self.random_crop(image,bb2d,centroid2d)
image,bb2d,centroid2d = self.flip_lr(image,bb2d,centroid2d)
return image,bb2d,centroid2d
else:
return image,bb2d,centroid2d
def randomize_bg_overlay(self,image,pose,bb2d,centroid2d,crop):
RGBimage = image.convert('RGB')
# RGBimage = image
RGBna = np.array(RGBimage)
if random.uniform(0,1) < self.randomization_prob:
if random.uniform(0,1) < self.photo_bg_prob:
bg_path = random.sample(self.paths_images,1)[0]
bg_im = Image.open(bg_path)
bg_im = bg_im.convert('RGB')
bg_im = bg_im.resize((RGBna.shape[1],RGBna.shape[0]),resample = Image.BILINEAR)
bg_im = np.array(bg_im)
else:
bg_im = np.ones((RGBna.shape[0],RGBna.shape[1],3),dtype = np.uint8)
bg_im[:,:,0] = bg_im[:,:,0]*random.randint(0,255)
bg_im[:,:,1] = bg_im[:,:,1]*random.randint(0,255)
bg_im[:,:,2] = bg_im[:,:,2]*random.randint(0,255)
else:
return RGBimage,bb2d,centroid2d
rgba, mask_green = rgba_from_rgb_green_bg(RGBna)
rgba, mask_bbox = crop_hand_by_bbox(rgba, pose)
mask = mask_green * mask_bbox
# rgb = np.stack([rgba[:, :, 2],rgba[:, :, 1],rgba[:, :, 0]],axis=2).astype(np.uint8)
rgb = np.stack([rgba[:, :, 0],rgba[:, :, 1],rgba[:, :, 2]],axis=2).astype(np.uint8)
mask = np.stack([mask,mask,mask],axis=2)
# experimental: try superimposing background and image onto themselves
background = (1. - mask) * bg_im
# background = mask * rgb + (1. - mask) * bg_im
rgb = rgb.astype(np.uint8)
background = background.astype(np.uint8)
rgb = Image.fromarray(rgb)
background = Image.fromarray(background)
out = Image.blend(background,rgb,0.5)
if crop:
out,bb2d,centroid2d = self.random_crop(out,bb2d,centroid2d)
out,bb2d,centroid2d = self.flip_lr(out,bb2d,centroid2d)
return out,bb2d,centroid2d
def randomize_bg_overlay_from_mask(self,image,mask,pose,bb2d,centroid2d,crop,flip):
RGBimage = image.convert('RGB')
# RGBimage = image
RGBna = np.array(RGBimage)
if random.uniform(0,1) < self.randomization_prob:
if random.uniform(0,1) < self.photo_bg_prob:
bg_path = random.sample(self.paths_images,1)[0]
bg_im = Image.open(bg_path)
bg_im = bg_im.convert('RGB')
bg_im = bg_im.resize((RGBna.shape[1],RGBna.shape[0]),resample = Image.BILINEAR)
bg_im = np.array(bg_im)
else:
bg_im = np.ones((RGBna.shape[0],RGBna.shape[1],3),dtype = np.uint8)
bg_im[:,:,0] = bg_im[:,:,0]*random.randint(0,255)
bg_im[:,:,1] = bg_im[:,:,1]*random.randint(0,255)
bg_im[:,:,2] = bg_im[:,:,2]*random.randint(0,255)
else:
return RGBimage,bb2d,centroid2d
mask = np.array(mask)
mask = mask[:,:,3]
# rgb = np.stack([rgba[:, :, 0],rgba[:, :, 1],rgba[:, :, 2]],axis=2).astype(np.uint8)
# rgb = RGBna.astype(np.uint8)
mask = np.stack([mask,mask,mask],axis=2)/255.
background = mask * RGBna + (1. - mask) * bg_im
RGBna = RGBna.astype(np.uint8)
background = background.astype(np.uint8)
RGBna = Image.fromarray(RGBna)
background = Image.fromarray(background)
out = Image.blend(background,RGBna,0.5)
if crop:
out,bb2d,centroid2d = self.random_crop(out,bb2d,centroid2d)
if flip:
out,bb2d,centroid2d = self.flip_lr(out,bb2d,centroid2d)
# experimental feature
# out = self.color_distort(out)
return out,bb2d,centroid2d
def randomize_bg_overlay_from_mask_noise_bg(self,image,mask,pose,bb2d,centroid2d,crop,flip):
RGBimage = image.convert('RGB')
# RGBimage = image
RGBna = np.array(RGBimage)
if random.uniform(0,1) < self.randomization_prob:
if random.uniform(0,1) < self.photo_bg_prob:
bg_im = np.random.random((RGBna.shape[0],RGBna.shape[1],3))
bg_im = (bg_im*255).astype(np.uint8)
else:
bg_im = np.ones((RGBna.shape[0],RGBna.shape[1],3),dtype = np.uint8)
bg_im[:,:,0] = bg_im[:,:,0]*random.randint(0,255)
bg_im[:,:,1] = bg_im[:,:,1]*random.randint(0,255)
bg_im[:,:,2] = bg_im[:,:,2]*random.randint(0,255)
else:
return RGBimage,bb2d,centroid2d
mask = np.array(mask)
mask = mask[:,:,3]
# rgb = np.stack([rgba[:, :, 0],rgba[:, :, 1],rgba[:, :, 2]],axis=2).astype(np.uint8)
# rgb = RGBna.astype(np.uint8)
mask = np.stack([mask,mask,mask],axis=2)/255.
background = mask * RGBna + (1. - mask) * bg_im
RGBna = RGBna.astype(np.uint8)
background = background.astype(np.uint8)
RGBna = Image.fromarray(RGBna)
background = Image.fromarray(background)
out = Image.blend(background,RGBna,0.5)
if crop:
out,bb2d,centroid2d = self.random_crop(out,bb2d,centroid2d)
if flip:
out,bb2d,centroid2d = self.flip_lr(out,bb2d,centroid2d)
return out,bb2d,centroid2d
def randomize_bg_overlay_from_mask_empty_scene(self,image,image_empty_scene,mask,pose,bb2d,centroid2d,crop):
RGBimage = image.convert('RGB')
# RGBimage = image
RGBna = np.array(RGBimage)
if random.uniform(0,1) < self.randomization_prob:
if random.uniform(0,1) < self.photo_bg_prob:
bg_path = random.sample(self.paths_images,1)[0]
bg_im = Image.open(bg_path)
bg_im = bg_im.convert('RGB')
bg_im = bg_im.resize((RGBna.shape[1],RGBna.shape[0]),resample = Image.BILINEAR)
bg_im = np.array(bg_im)
else:
# add bacground image of empty table
bg_im = Image.open(image_empty_scene)
bg_im = bg_im.convert('RGB')
bg_im = bg_im.resize((RGBna.shape[1],RGBna.shape[0]),resample = Image.BILINEAR)
bg_im = np.array(bg_im)
else:
return RGBimage,bb2d,centroid2d
mask = np.array(mask)
mask = mask[:,:,3]
# rgb = np.stack([rgba[:, :, 0],rgba[:, :, 1],rgba[:, :, 2]],axis=2).astype(np.uint8)
# rgb = RGBna.astype(np.uint8)
mask = np.stack([mask,mask,mask],axis=2)/255.
background = mask * RGBna + (1. - mask) * bg_im
RGBna = RGBna.astype(np.uint8)
background = background.astype(np.uint8)
RGBna = Image.fromarray(RGBna)
background = Image.fromarray(background)
out = Image.blend(background,RGBna,0.5)
if crop:
out,bb2d,centroid2d = self.random_crop(out,bb2d,centroid2d)
out,bb2d,centroid2d = self.flip_lr(out,bb2d,centroid2d)
return out,bb2d,centroid2d
def randomize_bg_overlay_from_mask_50_50_blend(self,image,image_empty_scene,mask,pose,bb2d,centroid2d,crop):
RGBimage = image.convert('RGB')
# RGBimage = image
RGBna = np.array(RGBimage)
if random.uniform(0,1) < self.randomization_prob:
if random.uniform(0,1) < self.photo_bg_prob:
bg_path = random.sample(self.paths_images,1)[0]
bg_im = Image.open(bg_path)
bg_im = bg_im.convert('RGB')
bg_im = bg_im.resize((RGBna.shape[1],RGBna.shape[0]),resample = Image.BILINEAR)
bg_im = np.array(bg_im)
else:
bg_path = random.sample(self.paths_images,1)[0]
bg_im_random = Image.open(bg_path)
bg_im_random = bg_im_random.convert('RGB')
bg_im_random = bg_im_random.resize((RGBna.shape[1],RGBna.shape[0]),resample = Image.BILINEAR)
bg_im_random = np.array(bg_im_random).astype(np.uint8)
bg_im_empty_bg = Image.open(image_empty_scene)
bg_im_empty_bg = bg_im_empty_bg.convert('RGB')
bg_im_empty_bg = bg_im_empty_bg.resize((RGBna.shape[1],RGBna.shape[0]),resample = Image.BILINEAR)
bg_im_empty_bg = np.array(bg_im_empty_bg).astype(np.uint8)
bg_im_random = Image.fromarray(bg_im_random)
bg_im_empty_bg = Image.fromarray(bg_im_empty_bg)
bg_im = Image.blend(bg_im_empty_bg,bg_im_random,0.5)
bg_im = np.array(bg_im)
else:
return RGBimage,bb2d,centroid2d
mask = np.array(mask)
mask = mask[:,:,3]
# rgb = np.stack([rgba[:, :, 0],rgba[:, :, 1],rgba[:, :, 2]],axis=2).astype(np.uint8)
# rgb = RGBna.astype(np.uint8)
mask = np.stack([mask,mask,mask],axis=2)/255.
background = mask * RGBna + (1. - mask) * bg_im
RGBna = RGBna.astype(np.uint8)
background = background.astype(np.uint8)
RGBna = Image.fromarray(RGBna)
background = Image.fromarray(background)
out = Image.blend(background,RGBna,0.5)
if crop:
out,bb2d,centroid2d = self.random_crop(out,bb2d,centroid2d)
out,bb2d,centroid2d = self.flip_lr(out,bb2d,centroid2d)
return out,bb2d,centroid2d
def random_crop(self,image,bb2d,centroid2d):
image_points = np.concatenate((bb2d,centroid2d),axis=0)
border_dist_cutoff = 10
max_iters = 20
if (max(image_points[:,0])>((self.max_width-1)-border_dist_cutoff)) or (max(image_points[:,1])>((self.max_hight-1)-border_dist_cutoff)):
return image,image_points[0:8,:],image_points[np.newaxis,8,:]
p1 = max(image_points[:,0])
p2 = min(image_points[:,1])
p3 = min(image_points[:,0])
p4 = max(image_points[:,1])
success = 0
asp_rat = self.max_width/self.max_hight
for i in range(max_iters):
C1_x = random.uniform(0,p3)
C1_y = random.uniform(0,p2)
y_limit = (self.max_hight-1)-C1_y
x_limit = asp_rat*y_limit
x_limit_y = asp_rat*(p4 - C1_y) + C1_x
if (x_limit_y < x_limit) and (x_limit_y > p1):
C2_x = random.uniform(x_limit_y,x_limit)
C2_y = C1_y + (C2_x-C1_x)/asp_rat
success = 1
break
if success == 0:
return image,image_points[0:8,:],image_points[np.newaxis,8,:]
image = image.crop((C1_x, C1_y, C2_x, C2_y))
w,h = image.size
image = image.resize((self.max_width,self.max_hight), Image.ANTIALIAS)
# RECALCULATE POINT LOCATIONS
# 1. from 640*480 coordinate system to crop coordinate system
new_image_points = np.zeros(image_points.shape,np.float32)
new_image_points[:,0] = (image_points[:,0] - C1_x)
new_image_points[:,1] = (image_points[:,1] - C1_y)
# 2. from crop coordinate system to 640*480 coordinate system
new_image_points[:,0] = (new_image_points[:,0]/w)*self.max_width
new_image_points[:,1] = (new_image_points[:,1]/h)*self.max_hight
return image,new_image_points[0:8,:],new_image_points[np.newaxis,8,:]
def flip_lr(self,image,bb2d,centroid2d):
image_points = np.concatenate((bb2d,centroid2d),axis=0)
if random.uniform(0,1) > 0.5:
w,h = image.size
image = image.transpose(Image.FLIP_LEFT_RIGHT)
image_points[:,0] = (w/2) - image_points[:,0] + (w/2)
return image,image_points[0:8,:],image_points[np.newaxis,8,:]
def random_crop_w_mask(self,image,mask,bb2d,centroid2d):
image_points = np.concatenate((bb2d,centroid2d),axis=0)
border_dist_cutoff = 10
max_iters = 20
if (max(image_points[:,0])>((self.max_width-1)-border_dist_cutoff)) or (max(image_points[:,1])>((self.max_hight-1)-border_dist_cutoff)):
return image,mask,image_points[0:8,:],image_points[np.newaxis,8,:]
p1 = max(image_points[:,0])
p2 = min(image_points[:,1])
p3 = min(image_points[:,0])
p4 = max(image_points[:,1])
success = 0
asp_rat = self.max_width/self.max_hight
for i in range(max_iters):
C1_x = random.uniform(0,p3)
C1_y = random.uniform(0,p2)
y_limit = (self.max_hight-1)-C1_y
x_limit = asp_rat*y_limit
x_limit_y = asp_rat*(p4 - C1_y) + C1_x
if (x_limit_y < x_limit) and (x_limit_y > p1):
C2_x = random.uniform(x_limit_y,x_limit)
C2_y = C1_y + (C2_x-C1_x)/asp_rat
success = 1
break
if success == 0:
return image,mask,image_points[0:8,:],image_points[np.newaxis,8,:]
image = image.crop((C1_x, C1_y, C2_x, C2_y))
mask = mask.crop((C1_x, C1_y, C2_x, C2_y))
w,h = image.size
image = image.resize((self.max_width,self.max_hight), Image.ANTIALIAS)
mask = mask.resize((self.max_width,self.max_hight), Image.ANTIALIAS)
# RECALCULATE POINT LOCATIONS
# 1. from 640*480 coordinate system to crop coordinate system
new_image_points = np.zeros(image_points.shape,np.float32)
new_image_points[:,0] = (image_points[:,0] - C1_x)
new_image_points[:,1] = (image_points[:,1] - C1_y)
# 2. from crop coordinate system to 640*480 coordinate system
new_image_points[:,0] = (new_image_points[:,0]/w)*self.max_width
new_image_points[:,1] = (new_image_points[:,1]/h)*self.max_hight
return image,mask,new_image_points[0:8,:],new_image_points[np.newaxis,8,:]
def flip_lr_w_mask(self,image,mask,bb2d,centroid2d):
image_points = np.concatenate((bb2d,centroid2d),axis=0)
if random.uniform(0,1) > 0.5:
w,h = image.size
image = image.transpose(Image.FLIP_LEFT_RIGHT)
mask = mask.transpose(Image.FLIP_LEFT_RIGHT)
image_points[:,0] = (w/2) - image_points[:,0] + (w/2)
return image,mask,image_points[0:8,:],image_points[np.newaxis,8,:]
def color_distort(self,image):
image = self.color_jitter(image)
image = self.rnd_gray(image)
return image
#TEST
#slash = os.sep
#
#source = ''
#
#randomizer = BG_randomizer(''+ slash,1.)
#ep_sample_dir = ''
#
#paths_images = []
#
#for filename in glob.iglob(source + slash + '**/*.png', recursive=True):
#
# if 'Depth' not in filename:
#
# paths_images.append(filename)
#
#
#for i in range(50):
#
# img_path = random.sample(paths_images,1)[0]
#
# img = Image.open(img_path)
#
# out_im = randomizer.randomize_bg_bbox(img)
# plt.imshow(out_im)
# plt.show()
# cv2.imwrite(os.path.join(ep_sample_dir,'sample_' + str(i) + '.jpg'),cv2.cvtColor(np.array(out_im), cv2.COLOR_RGB2BGR))