search_center_region.py

# Usage: >> python search_center_region.py --index index.csv --query queries/sample_query.jpg --result-path datacars
# or >> py search_center_region.py -i <your_index_filename> -q <your_query_image> -r <your_dataset>

# import the necessary packages
from module.colordescriptor import ColorDescriptor
from module.searcher import Searcher
import argparse
import cv2
import time

# 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())

# Estimated timing
start_time = time.time()

# initialize the image descriptor
# using 8 Hue bins, 12 Saturation bins, and 3 Value bins
cd = ColorDescriptor((8, 12, 3))

# load the query image and describe it
query = cv2.imread(args["query"])
features = cd.describe_center_region(query)

# perform the search
searcher = Searcher(args["index"])
results = searcher.search(features)
print "\nResults: ",results

# display the query
cv2.imshow("Query: %s"% (args["query"]), query)

# Result estimated time
print("\n--- Estimated time execution: %s seconds ---" % round(time.time() - start_time, 4))

# loop over the results
i = 0
print '\n 10 Best Matching Result for Query: ', args["query"], '\n', "len(features-Q): ", len(features)
for (score, resultID) in results:
	# load the result image and display it
	result = cv2.imread(args["result_path"] + "/" + resultID)
	
	i = i + 1
	cv2.imshow('Result #%s - %s'% (i, resultID), result)
	print i,'.\t Score: ', score, '\t\t | image: ', resultID
	
	cv2.waitKey(0)