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search.py
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search.py
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from transform import WaveletTransform
from featuredetector import SegmentFeatures
import argparse
import cv2
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import numpy as np
import time
import csv
# example
# python search.py --images jpg --query 100000.jpg --type s --limit 10
def showImages(images, titles = None):
#print(titles)
plt.switch_backend('TkAgg')
fig=plt.figure(figsize=(25, 25))
l = len(images)
rows = np.ceil(l/5)
n = 0
for img in images:
a = fig.add_subplot(rows, np.ceil(l/rows), n + 1)
a.set_title(titles[n])
plt.axis('off')
plt.imshow(img)
n += 1
mng = plt.get_current_fig_manager()
mng.window.state('zoomed')
plt.show()
def stdFilter(img, queryImg):
beta = 0.5
std1 = np.std(np.array(img)[:64])
queryStd1 = np.std(np.array(queryImg)[:64])
mintheta1 = std1 * beta
maxtheta1 = std1 / beta
std2 = np.std(np.array(img)[64:128])
queryStd2 = np.std(np.array(queryImg)[64:128])
mintheta2 = std2 * beta
maxtheta2 = std2 / beta
std3 = np.std(np.array(img)[128:])
queryStd3 = np.std(np.array(queryImg)[128:])
mintheta3 = std3 * beta
maxtheta3 = std3 / beta
#print(str(queryStd1) + "... std1:" + str(std1) + " ... maxtheta1: " + str(maxtheta1) + " ... mintheta1: " + str(mintheta1))
#print(str(queryStd2) + "... std2:" + str(std2) + " ... maxtheta2: " + str(maxtheta2) + " ... mintheta2: " + str(mintheta2))
#print(str(queryStd3) + "... std3:" + str(std3) + " ... maxtheta3: " + str(maxtheta3) + " ... mintheta3: " + str(mintheta3))
if (mintheta1 < queryStd1 < maxtheta1) or (mintheta2 < queryStd2 < maxtheta2 and mintheta3 < queryStd3 < maxtheta3):
return True
return False
def euclideanDistance(img, queryImg):
dist = 0.4 * np.linalg.norm(np.array(queryImg[:64])-np.array(img[:64]))
dist+= 0.3 * np.linalg.norm(np.array(queryImg[64:128])-np.array(img[64:128]))
dist+= 0.3 * np.linalg.norm(np.array(queryImg[128:])-np.array(img[128:]))
#print(dist)
if dist < 1000:
return dist
return -1
def search(queryFeatures, limit, indexPath):
results = {}
with open(indexPath) as f:
reader = csv.reader(f)
for row in reader:
features = [float(x) for x in row[1:]]
d = -1
if stdFilter(features, queryFeatures):
d = euclideanDistance(features, queryFeatures)
results[row[0]] = d
f.close()
results = sorted([(v, k) for (k, v) in results.items()])
distFilter = {}
for r in results:
#print(r[0])
#if -1 < r[0] < 502 and len(distFilter) < limit:
if -1 < r[0] and len(distFilter) < limit:
distFilter[r[1]] = r[0]
distFilter = sorted([(v, k) for (k, v) in distFilter.items()])
#print(distFilter)
#print(results[0])
return distFilter
# =========================================================================
start_time = time.time()
# construct the argument parser and parse the arguments
ap = argparse.ArgumentParser()
ap.add_argument("-i", "--images", required = True,
help = "Folder database with saved features")
ap.add_argument("-q", "--query", required = True,
help = "Name of query image")
ap.add_argument("-t", "--type", required = True,
help = "Type of feature detection. w = avelet, s=segmentation")
ap.add_argument("-l", "--limit", required = True,
help = "Number of results to return")
args = vars(ap.parse_args())
imgdir = args["images"]
imgname = imgdir + "/" + args["query"]
fdtype = args["type"]
f = "data/" + imgdir + "_" + fdtype + ".csv"
# initialize the image descriptor
cd = SegmentFeatures((8, 12, 3))
if fdtype == "w":
cd = WaveletTransform()
imgResults = []
imgNames = []
query = cv2.imread(imgname)
imgResults.append(query)
imgNames.append("Query: " + imgname[imgname.rfind("/") + 1:])
query = cv2.resize(query, (128, 128), interpolation = cv2.INTER_LINEAR)
if fdtype == "w":
features = cd.findFeatures(query, imgdir)
else:
features = cd.findFeatures(query)
# perform the search
results = search(features, int(args["limit"]), f)
print("Found matching images in %.2fs" % (time.time() - start_time))
# loop over the results
for (score, resultID) in results:
#print(resultID + " " + str(score))
result = cv2.imread(resultID)
#result = cv2.resize(result, (512, 512), interpolation = cv2.INTER_LINEAR)
tmp = "Query: " + resultID[resultID.rfind("\\") + 1:]
if (tmp not in imgNames):
imgResults.append(result)
imgNames.append(resultID[resultID.rfind("\\") + 1:] + "\nDistance: " + str(round(score,2)))
showImages(imgResults, imgNames)