def rescale(root_new, root_old, img_path, ann_path, out_shape):
try:
img = io.imread(root_old + "/" + img_path)
except Exception as E:
print E
h, w, _ = img.shape
f_h, f_w = float(out_shape) / h, float(out_shape) / w
trans_img = transform.rescale(img, (f_h, f_w))
num_objs = 0
with open(root_old + "/" + ann_path, 'r') as f:
ann = f.readline()
ann = ann.rstrip()
ann = ann.split(' ')
ann = [float(i) for i in ann]
num_objs = len(ann) / 5
for idx in xrange(num_objs):
ann[idx * 5 + 0] = int(f_w * ann[idx * 5 + 0])
ann[idx * 5 + 1] = int(f_h * ann[idx * 5 + 1])
ann[idx * 5 + 2] = int(f_w * ann[idx * 5 + 2])
ann[idx * 5 + 3] = int(f_h * ann[idx * 5 + 3])
# Write the new annotations to file
with open(root_new + "/" + ann_path, 'w') as f_new:
for val in ann:
f_new.write(str(val) + ' ')
# Save the new image
io.imwrite(root_new + "/" + img_path, trans_img)
def rescale(root_new, root_old, img_path, ann_path, out_shape):
try:
img = io.imread(root_old+"/"+img_path)
except Exception as E:
print E
h, w, _ = img.shape
f_h, f_w = float(out_shape)/h, float(out_shape)/w
trans_img = transform.rescale(img, (f_h, f_w))
num_objs = 0
with open(root_old+"/"+ann_path, 'r') as f:
ann = f.readline()
ann = ann.rstrip()
ann = ann.split(' ')
ann = [float(i) for i in ann]
num_objs = len(ann) / 5
for idx in xrange(num_objs):
ann[idx * 5 + 0] = int(f_w * ann[idx * 5 + 0])
ann[idx * 5 + 1] = int(f_h * ann[idx * 5 + 1])
ann[idx * 5 + 2] = int(f_w * ann[idx * 5 + 2])
ann[idx * 5 + 3] = int(f_h * ann[idx * 5 + 3])
# Write the new annotations to file
with open(root_new+"/"+ann_path, 'w') as f_new:
for val in ann:
f_new.write(str(val)+' ')
# Save the new image
io.imwrite(root_new+"/"+img_path, trans_img)
def imwrite(path, im):
try:
import cv2
return cv2.imwrite(path, im)
except ImportError:
pass
from skimage import io
return io.imwrite(path, im)
recon[:] = obj.tv_loop(recon, dPOCS, ng)
dg = np.linalg.norm(recon - temp_recon)
if (dg > dp * r_max and dd_vec[i] > eps):
dPOCS *= alpha_red
tv_vec[i] = tv(recon)
rmse_vec[i] = np.sqrt(((recon - img0)**2).mean())
if ((i + 1) % 300 == 0):
if save: #save image
recon[recon < 0] = 0
io.imwrite('eps_' + str(round(eps, 2)) + '_' + file_name,
np.uint16(recon))
# add new epsilon parameters
eps_vec[eps_ind, :] = (eps, i)
eps_ind += 1
eps -= 5
beta = 0.3
recalc_l2 = True
if save:
file_name = file_name.replace('.tif', '')
np.save('Results/dyn_eps.npy', eps_vec)
np.save('Results/dyn_tv.npy', tv_vec)
np.save('Results/dyn_dd.npy', dd_vec)
np.save('Results/dyn_rmse.npy', rmse_vec)
# In[14]:
kernel = load_kernel_from_file(ctx, '../datas/cl/rgb2gray.cl')
# In[ ]:
imageFormat = cl.ImageFormat(cl.channel_order.RGBA,
cl.channel_type.UNSIGNED_INT8)
img_in_dev = copy_image_to_device_readonly(ctx, src, imageFormat)
# In[10]:
img_out_dev = create_device_write_only_image(ctx, imageFormat,
(src.shape[0], src.shape[1]))
# In[14]:
local_work_size = (8, 8)
global_work_size = (cal_work_size(local_work_size[0], src.shape[0]),
cal_work_size(local_work_size[1], src.shape[1]))
print(local_work_size, global_work_size)
kernel.gray_avg_filter(queue, global_work_size, local_work_size, img_in_dev,
img_out_dev)
# In[16]:
output = copy_image_to_host(queue, img_out_dev, (src.shape[0], src.shape[1]),
np.uint8)
skio.imwrite('../temp/cl_output.jpg')
import os
import sys
from skimage import io
from modules.histogramNormalizing import matchHistograms
reference = sys.argv[1]
target = sys.argv[2]
prefix = os.path.splitext(reference)[0]
matched = matchHistograms(reference, target)
io.imwrite(f"{prefix}_matched.tif", matched)
def write_image(datapath, timestamp, imraw):
filename = os.path.join(datapath,
timestamp.strftime('D%Y%m%dT%H%M%S.%f.bmp'))
imwrite(filename, np.uint8(imraw))
logger.info('Image written')