Deleted old files

src/index.py

@@ -1,32 +0,0 @@
-# # import the necessary packages
-# from colordescriptor import ColorDescriptor
-# import glob
-# import cv2
-
-
-# class Indexer:
-#         def __init__(self, indexPath):
-#                 # store our index path
-#                 self.indexPath = indexPath
-
-#         def index(self):
-#                 # initialize the color descriptor
-#                 cd = ColorDescriptor((8, 12, 3))
-
-#                 # open the output index file for writing
-#                 output = open(self.indexPath, "w")
-
-#                 # use glob to grab the image paths and loop over them
-#                 for imagePath in glob.glob("../static/images/" + "/*.png"):
-#                         # extract the image ID (i.e. the unique filename) from the image
-#                         # path and load the image itself
-#                         imageID = imagePath[imagePath.rfind("/") + 1:]
-#                         image = cv2.imread(imagePath)
-#                         # describe the image
-#                         features = cd.describe(image)
-#                         # write the features to file
-#                         features = [str(f) for f in features]
-#                         output.write("%s,%s\n" % (imageID, ",".join(features)))
-
-#                 # close the index file
-#                 output.close()

src/search.py

@@ -1,31 +0,0 @@
-# from colordescriptor import ColorDescriptor
-# from searcher import Searcher
-# import argparse
-# import cv2
-
-# # construct the argument parser and parse the arguments
-# ap = argparse.ArgumentParser()
-# ap.add_argument("-i", "--index", required = True,
-#         help = "Path to where the computed index will be stored")
-# ap.add_argument("-q", "--query", required = True,
-#         help = "Path to the query image")
-# ap.add_argument("-r", "--result-path", required = True,
-#         help = "Path to the result path")
-# args = vars(ap.parse_args())
-
-# # initialize the image descriptor
-# cd = ColorDescriptor((8, 12, 3))
-# # load the query image and describe it
-# query = cv2.imread(args["query"])
-# features = cd.describe(query)
-# # perform the search
-# searcher = Searcher(args["index"])
-# results = searcher.search(features)
-# # display the query
-# cv2.imshow("Query", query)
-# # loop over the results
-# for (score, resultID) in results:
-#         # load the result image and display it
-#         result = cv2.imread(args["result_path"] + "/" + resultID)
-#         cv2.imshow("Result", result)
-#         cv2.waitKey(0)

src/searcher.py

@@ -1,47 +0,0 @@
-# import numpy as np
-# import csv
-
-# class Searcher:
-#     def __init__(self, indexPath):
-#         # store our index path
-#         self.indexPath = indexPath
-
-
-#     def chi2_distance(self, histA, histB, eps = 1e-10):
-#         # compute the chi-squared distance
-#         d = 0.5 * np.sum([((a - b) ** 2) / (a + b + eps)
-#             for (a, b) in zip(histA, histB)])
-#         # return the chi-squared distance
-#         return d
-
-#     def search(self, queryFeatures, limit = 3):
-#         # initialize our dictionary of results
-#         results = {}
-#         # open the index file for reading
-#         with open(self.indexPath) as f:
-#             # initialize the CSV reader
-#             reader = csv.reader(f)
-#             # loop over the rows in the index
-#             for row in reader:
-#                 # parse out the image ID and features, then compute the
-#                 # chi-squared distance between the features in our index
-#                 # and our query features
-#                 features = [float(x) for x in row[1:]]
-#                 d = self.chi2_distance(features, queryFeatures)
-#                 # now that we have the distance between the two feature
-#                 # vectors, we can udpate the results dictionary -- the
-#                 # key is the current image ID in the index and the
-#                 # value is the distance we just computed, representing
-#                 # how 'similar' the image in the index is to our query
-#                 results[row[0]] = d
-
-#             # close the reader
-#             f.close()
-
-#         # sort our results, so that the smaller distances (i.e. the
-#         # more relevant images are at the front of the list)
-#         path = "home/user/app/static/images/"
-#         results = sorted([(v, f"{path}{k}") for (k, v) in results.items()])
-
-#         # return our (limited) results
-#         return results[:limit]