def resize_all_images(size):
imageids = imagesHandler.get_all_img_ids_string()
for img_id in imageids:
img = imagesHandler.get_image(img_id)
url = imagesHandler.get_full_url(img_id)
filename = url.split(dirm.rootDirectory)[1]
imgr = cv2.resize(img, (size, size))
cv2.imwrite(dirm.rootDirectory + "/small/" + filename, imgr)
def resize_all_images(size):
imageids = imagesHandler.get_all_img_ids_string()
for img_id in imageids:
img = imagesHandler.get_image(img_id)
url = imagesHandler.get_full_url(img_id)
filename = url.split(dirm.rootDirectory)[1]
imgr = cv2.resize(img, (size, size))
cv2.imwrite(dirm.rootDirectory+"/small/"+ filename, imgr )
def plot_raw_TSNE(tsneValues, imageIDs, filename):
print "Plotting " + filename
filename = filename + "_raw"
locations = [
] #loc keeps track of where the images are being placed in the visualisation
#Load Tsne Values
x = np.array(tsneValues)
#subtract the min of each column
x = x - x.min(axis=0)
#devide by the max of each column
x = x / x.max(axis=0)
#get filename
fs = imageIDs
N = len(fs) #N = length(fs);
# size of every individual image
s = 250
# size of final image
S = 10004
# the final images intitialised
G = np.ones((S, S, 3)) # ,'uint8'
G = G * 255
for i in range(0, N):
a = math.ceil(x[i, 0] * (S - s) + 1)
b = math.ceil(x[i, 1] * (S - s) + 1)
a = a - ((a - 1) % s) + 1
b = b - ((b - 1) % s) + 1
#print meta,b
imgid = fs[i].rstrip('\n')
if i % 100 == 0:
print i, "/", len(fs)
if i == N:
print i, "/", len(fs)
img = imagesHandler.get_image(imgid)
#CROPING
#img_crop = util.cropImage(img,100,100)
imgr = cv2.resize(img, (s, s))
c = a + s
#print c
d = b + s
G[a:c, b:d, :] = imgr
loc = [imgid, a, c, b, d]
locations.append(loc)
print "Saving Image:" + dirm.outputDirectory + filename + ".jpg"
cv2.imwrite(dirm.outputDirectory + filename + ".jpg", G)
print "Image Saved"
tsneHandler.storeLoc(filename, locations)
return G
def plot_raw_TSNE(tsneValues, imageIDs, filename):
print "Plotting " + filename
filename = filename + "_raw"
locations = [] # loc keeps track of where the images are being placed in the visualisation
# Load Tsne Values
x = np.array(tsneValues)
# subtract the min of each column
x = x - x.min(axis=0)
# devide by the max of each column
x = x / x.max(axis=0)
# get filename
fs = imageIDs
N = len(fs) # N = length(fs);
# size of every individual image
s = 250
# size of final image
S = 10004
# the final images intitialised
G = np.ones((S, S, 3)) # ,'uint8'
G = G * 255
for i in range(0, N):
a = math.ceil(x[i, 0] * (S - s) + 1)
b = math.ceil(x[i, 1] * (S - s) + 1)
a = a - ((a - 1) % s) + 1
b = b - ((b - 1) % s) + 1
# print meta,b
imgid = fs[i].rstrip("\n")
if i % 100 == 0:
print i, "/", len(fs)
if i == N:
print i, "/", len(fs)
img = imagesHandler.get_image(imgid)
# CROPING
# img_crop = util.cropImage(img,100,100)
imgr = cv2.resize(img, (s, s))
c = a + s
# print c
d = b + s
G[a:c, b:d, :] = imgr
loc = [imgid, a, c, b, d]
locations.append(loc)
print "Saving Image:" + dirm.outputDirectory + filename + ".jpg"
cv2.imwrite(dirm.outputDirectory + filename + ".jpg", G)
print "Image Saved"
tsneHandler.storeLoc(filename, locations)
return G