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test.py
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test.py
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import math
import cv2 as cv
import numpy as np
import torch
from torchvision import transforms
from tqdm import tqdm
from config import device, fg_path_test, a_path_test, bg_path_test
from data_gen import data_transforms, fg_test_files, bg_test_files
from utils import compute_mse, compute_sad, AverageMeter, get_logger
def gen_test_names():
num_fgs = 50
num_bgs = 1000
num_bgs_per_fg = 20
names = []
bcount = 0
for fcount in range(num_fgs):
for i in range(num_bgs_per_fg):
names.append(str(fcount) + '_' + str(bcount) + '.png')
bcount += 1
return names
def process_test(im_name, bg_name, trimap):
# print(bg_path_test + bg_name)
im = cv.imread(fg_path_test + im_name)
a = cv.imread(a_path_test + im_name, 0)
h, w = im.shape[:2]
bg = cv.imread(bg_path_test + bg_name)
bh, bw = bg.shape[:2]
wratio = w / bw
hratio = h / bh
ratio = wratio if wratio > hratio else hratio
if ratio > 1:
bg = cv.resize(src=bg, dsize=(math.ceil(bw * ratio), math.ceil(bh * ratio)), interpolation=cv.INTER_CUBIC)
return composite4_test(im, bg, a, w, h, trimap)
# def composite4_test(fg, bg, a, w, h):
# fg = np.array(fg, np.float32)
# bg_h, bg_w = bg.shape[:2]
# x = max(0, int((bg_w - w)/2))
# y = max(0, int((bg_h - h)/2))
# bg = np.array(bg[y:y + h, x:x + w], np.float32)
# alpha = np.zeros((h, w, 1), np.float32)
# alpha[:, :, 0] = a / 255.
# im = alpha * fg + (1 - alpha) * bg
# im = im.astype(np.uint8)
# print('im.shape: ' + str(im.shape))
# print('a.shape: ' + str(a.shape))
# print('fg.shape: ' + str(fg.shape))
# print('bg.shape: ' + str(bg.shape))
# return im, a, fg, bg
def composite4_test(fg, bg, a, w, h, trimap):
fg = np.array(fg, np.float32)
bg_h, bg_w = bg.shape[:2]
x = max(0, int((bg_w - w) / 2))
y = max(0, int((bg_h - h) / 2))
crop = np.array(bg[y:y + h, x:x + w], np.float32)
alpha = np.zeros((h, w, 1), np.float32)
alpha[:, :, 0] = a / 255.
# trimaps = np.zeros((h, w, 1), np.float32)
# trimaps[:,:,0]=trimap/255.
im = alpha * fg + (1 - alpha) * crop
im = im.astype(np.uint8)
new_a = np.zeros((bg_h, bg_w), np.uint8)
new_a[y:y + h, x:x + w] = a
new_trimap = np.zeros((bg_h, bg_w), np.uint8)
new_trimap[y:y + h, x:x + w] = trimap
cv.imwrite('images/test/new/' + trimap_name, new_trimap)
new_im = bg.copy()
new_im[y:y + h, x:x + w] = im
# cv.imwrite('images/test/new_im/'+trimap_name,new_im)
return new_im, new_a, fg, bg, new_trimap
if __name__ == '__main__':
checkpoint = 'BEST_checkpoint.tar'
checkpoint = torch.load(checkpoint)
model = checkpoint['model'].module
model = model.to(device)
model.eval()
transformer = data_transforms['valid']
names = gen_test_names()
mse_losses = AverageMeter()
sad_losses = AverageMeter()
logger = get_logger()
i = 0
for name in tqdm(names):
fcount = int(name.split('.')[0].split('_')[0])
bcount = int(name.split('.')[0].split('_')[1])
im_name = fg_test_files[fcount]
# print(im_name)
bg_name = bg_test_files[bcount]
trimap_name = im_name.split('.')[0] + '_' + str(i) + '.png'
# print('trimap_name: ' + str(trimap_name))
trimap = cv.imread('data/Combined_Dataset/Test_set/Adobe-licensed images/trimaps/' + trimap_name, 0)
# print('trimap: ' + str(trimap))
i += 1
if i == 20:
i = 0
img, alpha, fg, bg, new_trimap = process_test(im_name, bg_name, trimap)
h, w = img.shape[:2]
# mytrimap = gen_trimap(alpha)
# cv.imwrite('images/test/new_im/'+trimap_name,mytrimap)
x = torch.zeros((1, 4, h, w), dtype=torch.float)
img = img[..., ::-1] # RGB
img = transforms.ToPILImage()(img) # [3, 320, 320]
img = transformer(img) # [3, 320, 320]
x[0:, 0:3, :, :] = img
x[0:, 3, :, :] = torch.from_numpy(new_trimap.copy() / 255.)
# Move to GPU, if available
x = x.type(torch.FloatTensor).to(device) # [1, 4, 320, 320]
alpha = alpha / 255.
with torch.no_grad():
pred = model(x) # [1, 4, 320, 320]
pred = pred.cpu().numpy()
pred = pred.reshape((h, w)) # [320, 320]
pred[new_trimap == 0] = 0.0
pred[new_trimap == 255] = 1.0
cv.imwrite('images/test/out/' + trimap_name, pred * 255)
# Calculate loss
# loss = criterion(alpha_out, alpha_label)
mse_loss = compute_mse(pred, alpha, trimap)
sad_loss = compute_sad(pred, alpha)
# Keep track of metrics
mse_losses.update(mse_loss.item())
sad_losses.update(sad_loss.item())
print("sad:{} mse:{}".format(sad_loss.item(), mse_loss.item()))
print("sad:{} mse:{}".format(sad_losses.avg, mse_losses.avg))
print("sad:{} mse:{}".format(sad_losses.avg, mse_losses.avg))