def fit(self, train_set):
"""Fit the model to observations.
Parameters
----------
train_set: object of type TrainSet, required
An object contraining the user-item preference in csr scipy sparse format,\
as well as some useful attributes such as mappings to the original user/item ids.\
Please refer to the class TrainSet in the "data" module for details.
"""
Recommender.fit(self, train_set)
X = sp.csc_matrix(self.train_set.matrix)
# recover the striplet sparse format from csc sparse matrix X (needed to feed c++)
(rid, cid, val) = sp.find(X)
val = np.array(val, dtype='float32')
rid = np.array(rid, dtype='int32')
cid = np.array(cid, dtype='int32')
tX = np.concatenate((np.concatenate(([rid], [cid]), axis=0).T, val.reshape((len(val), 1))), axis=1)
del rid, cid, val
if self.trainable:
if self.hierarchical:
res = hpf.hpf(tX, X.shape[0], X.shape[1], self.k, self.max_iter, self.init_params)
else:
res = hpf.pf(tX, X.shape[0], X.shape[1], self.k, self.max_iter, self.init_params)
self.Theta = np.asarray(res['Z'])
self.Beta = np.asarray(res['W'])
elif self.verbose:
print('%s is trained already (trainable = False)' % (self.name))
gt = cv2.cvtColor(cv2.imread(gtlist[i]), cv2.COLOR_BGR2YCrCb)[:, :, 0]
sr = cv2.cvtColor(cv2.imread(srlist[j]), cv2.COLOR_BGR2YCrCb)[:, :, 0]
(height, width) = gt.shape
if gt.shape != sr.shape:
if height % 2 == 1:
height -= 1
if width % 2 == 1:
width -= 1
gt = gt[0:height, 0:width]
if gt.shape != sr.shape:
print('dimensions do not match on image:', gtlist[i], srlist[i])
print(gt.shape, sr.shape)
continue
srh = hpf(sr).astype(float) / 255.
gth = hpf(gt).astype(float) / 255.
'''
cv2.imshow('srh',srh)
cv2.imshow('gth',gth)
print('srh',srh)
print('gth',gth)
cv2.waitKey(0)
cv2.destroyAllWindows()
'''
stackQV(Q, V, count, srh, gth, margin, width, height, imagei, imageN,
patchSize, W, pcaL, svc)
print('count', count)
print('fliping samples...')
sys.stdout.flush()
import cv2
import numpy as np
import os
import pickle
import sys
import argparse
from math import floor, pi
from matplotlib import pyplot as plt
from scipy import interpolate
from skimage import transform
from hpf import hpf
gt = "SR_testing_datasets\\Set14_SR\\Set14\\image_SRF_2\\img_014_SRF_2_HR.png"
gt = (cv2.cvtColor(cv2.imread(gt), cv2.COLOR_BGR2YCrCb)[:, :, 0])
gt = gt.astype(float)
gth = hpf(gt)
gth = gth.astype(float)
gtl = gt - gth
h, w = gt.shape
n = w * h
srlist = []
for parent, dirnames, filenames in os.walk(
"SR_testing_datasets\\Set14_SR\\Set14SR"):
for filename in filenames:
srlist.append(os.path.join(parent, filename))
print(srlist)
for sr_ in srlist:
sr = cv2.cvtColor(cv2.imread(sr_), cv2.COLOR_BGR2YCrCb)[:, :, 0]
sr = sr.astype(float)
if filename.lower().endswith(
('.bmp', '.dib', '.png', '.jpg', '.jpeg', '.pbm', '.pgm',
'.ppm', '.tif', '.tiff')):
imagelist.append(os.path.join(parent, filename))
Vsize = 10000
patchV = np.zeros((Vsize, 4, patchSize, patchSize))
Vid = np.zeros(4).astype(int)
print('Collecting patches...')
imcount = 0
for img in imagelist:
imcount += 1
im = cv2.imread(img)
im = cv2.cvtColor(im, cv2.COLOR_BGR2YCrCb)[:, :, 0]
imh = hpf(im)
w, h = imh.shape
#cv2.imshow('',np.uint8(imh))
#cv2.waitKey(0)
#print(im.shape)
#print(imh.shape)
processi = 0
processma = (h - 2 * margin) * (w - 2 * margin)
#collect all patched with central HF energy>20
for row in range(margin, w - margin):
print('\r', imcount, '/', len(imagelist), ' images', end='|')
print('█' * (processi * 50 // processma), end='')
print(' ' * (50 - processi * 50 // processma), end='|')
sys.stdout.flush()
for col in range(margin, h - margin):
processi += 1
try:
os.mkdir('results/'+ args.output)
except Exception as e:
print("ignoring error:",e)
imagelist=[]
for parent, dirnames, filenames in os.walk(args.input):
for filename in filenames:
if filename.lower().endswith(('.bmp', '.dib', '.png', '.jpg', '.jpeg', '.pbm', '.pgm', '.ppm', '.tif', '.tiff')):
imagelist.append(os.path.join(parent, filename))
imagei=0
imageN=len(imagelist)
for image in imagelist:
imagei+=1
origin=cv2.cvtColor(cv2.imread(image),cv2.COLOR_BGR2YCrCb)
sr=origin[:,:,0]
srh=hpf(sr).astype(float)
srl=sr.astype(float)-srh
ehh=srh
mask=np.zeros(sr.shape)
width,height=sr.shape
enhance(srh,ehh,mask,width,height,margin,pcaL,svc,h,imagei,imageN,patchSize,W)
result=origin
result[:,:,0]=np.uint8(np.clip(srl+ehh*255.,0.,255.))
result=cv2.cvtColor(result,cv2.COLOR_YCrCb2BGR)
cv2.imwrite('results/'+ args.output + '/' +\
os.path.splitext(os.path.basename(image))[0] + '.png',result)
cv2.imwrite('results/'+ args.output + '/' +\
os.path.splitext(os.path.basename(image))[0] + 'mask.png',mask.astype('uint8'))
import cv2
import numpy as np
import os
import pickle
import sys
import argparse
from math import floor, pi
from matplotlib import pyplot as plt
from scipy import interpolate
from skimage import transform
from hpf import hpf
gt = "SR_testing_datasets\\Set14_SR\\Set14\\image_SRF_2\\img_014_SRF_2_HR.png"
gt = cv2.cvtColor(cv2.imread(gt), cv2.COLOR_BGR2YCrCb)[:, :, 0]
gth = hpf(gt)
gtl = gt - gth
h, w = gt.shape
n = w * h
srlist = []
for parent, dirnames, filenames in os.walk(
"SR_testing_datasets\\Set14_SR\\Set14\\image_SRF_2"):
for filename in filenames:
if filename.lower().startswith(('img_014_SRF_2_')):
srlist.append(os.path.join(parent, filename))
print(srlist)
for sr_ in srlist:
sr = cv2.cvtColor(cv2.imread(sr_), cv2.COLOR_BGR2YCrCb)[:, :, 0]
srh = hpf(sr)
srl = srh - srl