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