# Get all the images
for file in files:
img = cv2.imread(os.sep.join([r, file]))
if img is not None:
images.append(img)
height, width = img.shape[:2]
else:
print("%s not found." % (file))
images = np.array(images)
images.shape
##
# 2. Inital Watermark Detection
##
gx, gy = estimate_watermark(images)
est = poisson_reconstruct(gx, gy)
gx_manual_crop = gx[(height-constants.wmY):height, (width-constants.wmX):width]
gy_manual_crop = gy[(height-constants.wmY):height, (width-constants.wmX):width]
est_manual_crop = poisson_reconstruct(gx_manual_crop, gy_manual_crop)
cropped_gx, cropped_gy = crop_watermark(gx_manual_crop, gy_manual_crop)
est_auto_crop = poisson_reconstruct(cropped_gx, cropped_gy)
with open('cropped.npz', 'wb') as f:
np.savez(f, cropped_gx=cropped_gx, cropped_gy=cropped_gy)
img = cv2.imread('images/fotolia_processed/25_3_018.jpg')
start, rect = watermark_detector(img, cropped_gx, cropped_gy)
# basics
import numpy as np
np.set_printoptions(threshold=np.nan)
import matplotlib.pyplot as plt
# customs
from src.estimate_watermark import *
from src.preprocess import *
from src.image_crawler import *
from src.watermark_reconstruct import *
gx, gy, gxlist, gylist = estimate_watermark('ref_inputs')
# est = poisson_reconstruct(gx, gy, np.zeros(gx.shape)[:,:,0])
cropped_gx, cropped_gy = crop_watermark(gx, gy)
W_m = poisson_reconstruct(cropped_gx, cropped_gy)
# random photo
img = cv2.imread('ref_inputs/fotolia_168668150.jpg')
im, start, end = watermark_detector(img, cropped_gx, cropped_gy)
plt.imshow(cv2.cvtColor(im, cv2.COLOR_BGR2RGB))
plt.show()
# we don't need the others...
exit()
# We are done with watermark estimation
# W_m is the cropped watermark
num_images = len(gxlist)
return img[:, :, [2, 1, 0]]
'''
Ground Truth values
alpha -> coco_dataset/alpha.png
copyright -> coco_dataset/copyright.png
c = .45
Experiments: Threshold for estimating initial alpha -> 153, and then subtract 1 from alpha
'''
if __name__ == "__main__":
# watermark = cv2.imread('coco_dataset/watermark.png')
# alpha = get_alpha_matte(watermark)
foldername = os.path.join(IMAGE_FOLDER, IMG_PROCESSED_LOC)
gx, gy, gxlist, gylist = estimate_watermark(foldername)
# est = poisson_reconstruct(gx, gy, np.zeros(gx.shape)[:,:,0])
cropped_gx, cropped_gy = crop_watermark(gx, gy)
W_m = poisson_reconstruct(cropped_gx, cropped_gy, num_iters=5000)
# random photo
img = cv2.imread(os.path.join(foldername, '000000051008.jpg'))
im, start, end = watermark_detector(img, cropped_gx, cropped_gy)
num_images = len(gxlist)
J, img_paths = get_cropped_images(foldername, num_images, start, end,
cropped_gx.shape)
# get a random subset of J
idx = [
389, 144, 147, 468, 423, 92, 3, 354, 196, 53, 470, 445, 314, 349, 105,
# ----------------------------------------------------------------------------------------------------------------------
# INITIAL WATERMARK ESTIMATE & DETECTION -------------------------------------------------------------------------------
# Thresholds -----------------------------------------------------------------------------------------------------------
wm_crop_trh = 0.03
wm_detector_wm_thr = 0.03
wm_detector_img_thr = 0.3
wm_detector_tr_thr = 0.01
# ----------------------------------------------------------------------------------------------------------------------
J = read_images(dir_images)
for i in range(1):
images = preprocess(J, image_size)
# images.update(preprocess(J, image_size, 'constant'))
Wm_x, Wm_y, num_images = estimate_watermark(images)
# crop watermark area and get cropped gradient
cropped_Wm_x, cropped_Wm_y = crop_watermark_ignore_borders(
Wm_x, Wm_y, threshold=wm_crop_trh)
# reconstruct watermark image with poisson
W_m = poisson_reconstruct2(cropped_Wm_x, cropped_Wm_y)
cv2.imwrite(
(os.sep.join([os.path.abspath(res_dir), 'watermark_whole.jpg'])),
poisson_reconstruct2(Wm_x, Wm_y))
# # detect watermark on random photo
# img_sample = rnd.choice(images)
# # img_sample = cv2.imread(os.path.join(dir_images, img_name))
# img_marked, wm_start, wm_end = watermark_detector(img_sample, cropped_Wm_x, cropped_Wm_y)
# basics
import numpy as np
np.set_printoptions(threshold=np.nan)
import matplotlib.pyplot as plt
# customs
from src.estimate_watermark import *
from src.preprocess import *
from src.image_crawler import *
from src.watermark_reconstruct import *
gx, gy, gxlist, gylist = estimate_watermark(
'sources/paired_train-wmark_cifar10_wmark_32x32_tile_cifar10_transpair_noise_ResNet_l1_binary-cross-entropy_cross-entropy_64_30_0.01_0.001_0.001_0_0_2.0_1.0_1.0_10.0_1.0'
)
# est = poisson_reconstruct(gx, gy, np.zeros(gx.shape)[:,:,0])
cropped_gx, cropped_gy = crop_watermark(gx, gy)
W_m = poisson_reconstruct(cropped_gx, cropped_gy)
# random photo
img = cv2.imread(
('sources/paired_train-wmark_cifar10_wmark_32x32_tile_cifar10_'
'transpair_noise_ResNet_l1_binary-cross-entropy_cross-entropy_'
'64_30_0.01_0.001_0.001_0_0_2.0_1.0_1.0_10.0_1.0/0_4.png'))
im, start, end = watermark_detector(img, cropped_gx, cropped_gy)
# plot the watermarked area
plt.imshow(cv2.cvtColor(im, cv2.COLOR_BGR2RGB))
plt.show()
# we don't need the others
import cv2
import os
from src.estimate_watermark import *
from src.preprocess import *
from src.image_crawler import *
from src.watermark_reconstruct import *
gx, gy, gxlist, gylist = estimate_watermark('./images/fotolia_processed/8023974513')
# est = poisson_reconstruct(gx, gy, np.zeros(gx.shape)[:,:,0])
cropped_gx, cropped_gy = crop_watermark(gx, gy)
W_m = poisson_reconstruct(cropped_gx, cropped_gy)
# random photo
# img = cv2.imread('images/fotolia_processed/fotolia_137840668.jpg')
img = cv2.imread('images/fotolia_processed/8023974513/ahdx_8024936287_14.jpg')
im, start, end = watermark_detector(img, cropped_gx, cropped_gy)
plt.imshow(im)
plt.show()
# We are done with watermark estimation
# W_m is the cropped watermark
num_images = len(gxlist)
J, img_paths = get_cropped_images(
'images/fotolia_processed/8023974513', num_images, start, end, cropped_gx.shape)
# get a random subset of J
idx = [389, 144, 147, 468, 423, 92, 3, 354, 196, 53, 470, 445, 314, 349, 105, 366, 56, 168, 351, 15, 465, 368, 90, 96, 202, 54, 295, 137, 17, 79, 214, 413, 454, 305, 187, 4, 458, 330, 290, 73, 220, 118, 125, 180, 247, 243, 257, 194, 117, 320, 104, 252, 87, 95, 228, 324, 271, 398, 334, 148, 425, 190, 78, 151, 34, 310, 122, 376, 102, 260]
idx = idx[:25]
# Wm = (255*PlotImage(W_m))
Wm = W_m - W_m.min()
import cv2
from src.estimate_watermark import *
from src.preprocess import *
from src.image_crawler import *
from src.watermark_reconstruct import *
gx, gy, gxlist, gylist = estimate_watermark('./images/fotolia_processed')
# est = poisson_reconstruct(gx, gy, np.zeros(gx.shape)[:,:,0])
cropped_gx, cropped_gy = crop_watermark(gx, gy)
W_m = poisson_reconstruct(cropped_gx, cropped_gy)
# random photo
img = cv2.imread('images/fotolia_processed/fotolia_137840668.jpg')
im, start, end = watermark_detector(img, cropped_gx, cropped_gy)
# plt.imshow(im)
# plt.show()
# We are done with watermark estimation
# W_m is the cropped watermark
num_images = len(gxlist)
J, img_paths = get_cropped_images('images/fotolia_processed', num_images,
start, end, cropped_gx.shape)
# get a random subset of J
idx = [
389, 144, 147, 468, 423, 92, 3, 354, 196, 53, 470, 445, 314, 349, 105, 366,
56, 168, 351, 15, 465, 368, 90, 96, 202, 54, 295, 137, 17, 79, 214, 413,
454, 305, 187, 4, 458, 330, 290, 73, 220, 118, 125, 180, 247, 243, 257,
194, 117, 320, 104, 252, 87, 95, 228, 324, 271, 398, 334, 148, 425, 190,
78, 151, 34, 310, 122, 376, 102, 260