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 collectallmeta():
image_ids = imagesHandler.get_all_img_ids_string()
all_names = []
all_ids = []
names_turp = []
ids_turp = []
for id in image_ids:
json_url = getjsonfile(id)
ids, names = jsonParser.find_values(json_url)
all_names.extend(names)
all_ids.extend(ids)
names_turp.append(names)
ids_turp.append(ids)
return set(all_ids), set(all_names), ids_turp, names_turp
def assignclustertoallimages():
filelocation = "F:/Coryn/Google Drive/Project/Image Collection/Tate/turner-meta.csv"
images_meta_all = util.importCSV(filelocation)
curImagesInDB = imagesHandler.get_all_img_ids_string()
cur_img_meta = []
for image in images_meta_all:
id = image[0]
if id in curImagesInDB:
cur_img_meta.append(image)
print len(cur_img_meta)
ids = []
titles = []
mediums = []
mediums_clustered = []
dates = []
dates_clustered = []
for image in cur_img_meta:
id = image[0]
title = image[1]
medium = image[2]
date = image[3]
ids.append(id)
titles.append(title)
mediums.append(medium)
mediums_clustered.append(checkMediumCluster(medium))
dates.append(date)
dates_clustered.append(checkDateCluster(date))
return dates_clustered, mediums_clustered
