def searchimg(self, index1='index1.csv', index2='index2.csv', result_path='jpg'):
cd = ColorDescriptor((8,12,3))
vd = Vgg16Descriptor((224,224,3))
query = cv2.imread("./query/" + self.filename)
feature1 = cd.describe(query)
feature2 = vd.describe(self.querypath)
searcher = Searcher(index1,index2)
results = searcher.search(feature1,feature2)
result0 = cv2.resize(query,(128,128),interpolation=cv2.INTER_CUBIC)
name = locals()
i = 1
for (score, resultID) in results:
name['result%d'%i] = cv2.imread(result_path + "/" + resultID)
name['result%d'%i] = cv2.resize(name['result%d'%i],(128,128),interpolation=cv2.INTER_CUBIC)
i = i + 1
result_0 = np.hstack(name['result%d'%i] for i in range(1,6))
result_1 = np.hstack(name['result%d'%i] for i in range(6,11))
result = np.vstack((result_0,result_1))
cv2.imwrite("./result/%s" % self.filename, result)
return self.filename
def cal_center_region_hist(self, image): # initialize the color descriptor # using 8 Hue bins, 12 Saturation bins, and 3 Value bins cd = ColorDescriptor((8, 12, 3)) # convert the image to the HSV color space and initialize # the features used to quantify the image image = cv2.cvtColor(image, cv2.COLOR_BGR2HSV) features = [] # grab the dimensions and compute the center of the image (h, w) = image.shape[:2] (cX, cY) = (int(w * 0.5), int(h * 0.5)) # construct an elliptical mask representing the center of the image (axesX, axesY) = (int(w * 0.75) / 2, int(h * 0.75) / 2) ellipMask = np.zeros(image.shape[:2], dtype = "uint8") cv2.ellipse(ellipMask, (cX, cY), (axesX, axesY), 0, 0, 360, 255, -1) # extract a color histogram from the elliptical region and # update the feature vector hist = cd.histogram(image, ellipMask) hist = self.kmeans_region_features(hist) features.extend(hist.flatten()) # return the feature vector return features
def main_fun(self):
# initialize the color descriptor
cd = ColorDescriptor((8, 12, 3))
# open the output index file for writing
output = open(self.path, "w")
#data contains the path to your images folder
data = '/home/farhan/project/CBIR/my_contrib/test_dataset'
# use glob to grab the image paths and loop over them
types = ['/*.png', '/*.jpg']
# os.chdir(data)
for filetype in types:
for imagePath in glob.glob(data + filetype):
# 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()
def main_fun(self):
# initialize the color descriptor
cd = ColorDescriptor((8, 12, 3))
# open the output index file for writing
output = open(self.path, "w")
#data contains the path to your images folder
data='/home/farhan/project/CBIR/my_contrib/test_dataset'
# use glob to grab the image paths and loop over them
types=['/*.png','/*.jpg']
# os.chdir(data)
for filetype in types:
for imagePath in glob.glob(data+filetype):
# 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()
def indexpy(dataset, index):
print('hello')
cd = ColorDescriptor((8, 12, 3))
output = open(index, "w")
for imagePath in glob.glob(dataset + "/*.jpg"):
imageId = imagePath[imagePath.rfind("/") + 1:]
image = cv2.imread(imagePath)
features = cd.describe(image)
features = [str(f) for f in features]
output.write("%s,%s\n" % (imageId, ",".join(features)))
output.close()
def cal_region_hist(self, image): # initialize the color descriptor # using 8 Hue bins, 12 Saturation bins, and 3 Value bins cd = ColorDescriptor((8, 12, 3)) # convert the image to the HSV color space and initialize # the features used to quantify the image image = cv2.cvtColor(image, cv2.COLOR_BGR2HSV) features = [] # grab the dimensions and compute the center of the image (h, w) = image.shape[:2] (cX, cY) = (int(w * 0.5), int(h * 0.5)) # divide the image into four rectangles/segments (top-left, # top-right, bottom-right, bottom-left) segments = [(0, cX, 0, cY), (cX, w, 0, cY), (cX, w, cY, h), (0, cX, cY, h)] # construct an elliptical mask representing the center of the # image (axesX, axesY) = (int(w * 0.75) / 2, int(h * 0.75) / 2) ellipMask = np.zeros(image.shape[:2], dtype = "uint8") cv2.ellipse(ellipMask, (cX, cY), (axesX, axesY), 0, 0, 360, 255, -1) # loop over the segments for (startX, endX, startY, endY) in segments: # construct a mask for each corner of the image, subtracting # the elliptical center from it cornerMask = np.zeros(image.shape[:2], dtype = "uint8") cv2.rectangle(cornerMask, (startX, startY), (endX, endY), 255, -1) cornerMask = cv2.subtract(cornerMask, ellipMask) # extract a color histogram from the image, then update the # feature vector hist = cd.histogram(image, cornerMask) hist = self.kmeans_region_features(hist) features.extend(hist.flatten()) # extract a color histogram from the elliptical region and # update the feature vector hist = cd.histogram(image, ellipMask) hist = self.kmeans_region_features(hist) features.extend(hist.flatten()) # return the feature vector return features
def search(img):
# initialize the image descriptor
cd = ColorDescriptor((8, 12, 3))
# load the query image and describe it
#query = cv2.imread(args["query"])
#query = cv2.imread("queries/2.png")
features = cd.describe(img)
# perform the search
searcher = Searcher("feature.csv")
results = searcher.search(features)
return results
def create_features(path):
# initialize the color descriptor
cd = ColorDescriptor((4, 4, 2))
# use glob to grab the image paths and loop over them
image_names, X = [], []
for imagePath in listdir(path):
# extract the image ID (i.e. the unique filename) from the image
# path and load the image itself
imageID = basename(imagePath).split('.')[0]
image = cv2.imread(join(path,imagePath))
# describe the image
features = cd.describe(image)
image_names.append(imageID)
X.append(features)
return np.array(image_names), np.array(X)
def search_cut(self):
# initialize the image descriptor
cd = ColorDescriptor((8, 12, 3))
# load the query image and describe it
query = cv2.imread("out.jpg")
features = cd.describe(query)
# perform the search
searcher = Searcher("index.csv")
results = searcher.search(features)
# loop over the results
for (score, resultID) in results:
# load the result image and display it
result = cv2.imread(resultID)
cv2.imshow("Result", result)
cv2.waitKey(0)
def create_features(path):
# initialize the color descriptor
cd = ColorDescriptor((4, 4, 2))
# use glob to grab the image paths and loop over them
image_names, X = [], []
for imagePath in listdir(path):
# extract the image ID (i.e. the unique filename) from the image
# path and load the image itself
imageID = basename(imagePath).split('.')[0]
image = cv2.imread(join(path, imagePath))
# describe the image
features = cd.describe(image)
image_names.append(imageID)
X.append(features)
return np.array(image_names), np.array(X)
def search(index, query, result_path):
# initialize the image descriptor
cd = ColorDescriptor((8, 12, 3))
# load the query image and describe it
query1 = cv2.imread(query)
features = cd.describe(query1)
# perform the search
searcher = Searcher(index)
results = searcher.search(features)
# display the query
#cv2.imshow("Query", query1)
search_output = []
for (score, resultID) in results:
#print(result_path+"/"+resultID)
full_path = result_path + "/" + resultID
#result=cv2.imread(result_path+"/"+resultID)
#cv2.imshow("Result", result)
#cv2.waitKey(0)
search_output.append(resultID)
#print(search_output)
return search_output
def upload_file():
if request.method == 'POST':
# check if the post request has the file part
if 'file' not in request.files:
flash('No file part')
return redirect(request.url)
file = request.files['file']
if file.filename == '':
flash('No file selected for uploading')
return redirect(request.url)
if file and allowed_file(file.filename):
filename = secure_filename(file.filename)
file.save(os.path.join(app.config['UPLOAD_FOLDER'], filename))
# initialize the image descriptor
cd = ColorDescriptor((8, 12, 3))
# load the query image and describe it
photos_result = []
query = cv2.imread(app.config['UPLOAD_FOLDER']+"/"+filename)
features = cd.describe(query)
query_photo = {"score": 100, "photo": "queries/"+filename}
photos_result.append(query_photo)
# perform the search
searcher = Searcher("./index.csv")
results = searcher.search(features)
# loop over the results
for (score, resultID) in results:
score = log10(abs(score))
# load the result image and display it
result = {"score": score, "photo": "dataset/"+resultID}
photos_result.append(result)
return render_template('result.html', results=photos_result)
else:
flash('Allowed file types are txt, pdf, png, jpg, jpeg, gif')
return redirect(request.url)
def func3(command):
findresult = []
cd = ColorDescriptor((8, 12, 3))
query = cv2.imread(command)
features = cd.descriptor(query)
searcher1 = Searcher("index.csv")
results = searcher1.search(features)
for (score, resultID) in results:
findresult.append(resultID[:-4])
STORE_DIR = "index1"
vm_env = lucene.getVMEnv()
vm_env.attachCurrentThread()
directory = SimpleFSDirectory(File(STORE_DIR))
searcher = IndexSearcher(DirectoryReader.open(directory))
analyzer = WhitespaceAnalyzer(Version.LUCENE_CURRENT)
res = []
for imgID in findresult:
if imgID == '':
return
query = QueryParser(Version.LUCENE_CURRENT, "name",
analyzer).parse(imgID)
scoreDocs = searcher.search(query, 9).scoreDocs
for scoreDoc in scoreDocs:
doc = searcher.doc(scoreDoc.doc)
try:
res.append([
doc.get("name"),
doc.get("collect_num"),
doc.get("zhuliao").split(' '),
doc.get("zuofa").split('\n'),
doc.get("img_url"),
doc.get("url")
])
except:
pass
return res
def search(filename):
#print(query)
cd = ColorDescriptor((8, 12, 3))
query = cv2.imread(filename)
query = cv2.resize(query, (416, 416))
filepath = 'static/' + filename
cv2.imwrite(filepath, query)
features = cd.describe(query)
searcher = Searcher()
results = searcher.search(features)
#cv2.imshow("Query", query)
# loop over the results
for (score, resultID) in results:
# load the result image and display it
#print(resultID)
img = mongo.db.myImages.find({'filename': resultID}, {
"_id": 0,
"name": 0,
"filename": 0
})
l = []
for x in img:
#print(x)
x = x['images'][0]
#print('hey')
#print(x)
fout = fs.get(x['imageID'])
im = np.frombuffer(fout.read(), dtype=np.uint8)
im = np.reshape(im, x['shape'])
# cv2.imshow("Result", im)
# cv2.waitKey(0)
return results
def main_search(self):
cd = ColorDescriptor((8, 12, 3))
# load the query image and describe it
query = cv2.imread('test_dataset/'+self.name)
features = cd.describe(query)
# perform the search
searcher = Searcher(self.file_path)
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(self.data_path + "/" + resultID)
r=300.0/result.shape[1]
dim=(300,int(result.shape[0]*r))
resized = cv2.resize(result, dim, interpolation = cv2.INTER_AREA)
cv2.imshow("RESULT", resized)
cv2.waitKey(0)
cv2.destroyAllWindows()
def main_search(self):
cd = ColorDescriptor((8, 12, 3))
# load the query image and describe it
query = cv2.imread('test_dataset/' + self.name)
features = cd.describe(query)
# perform the search
searcher = Searcher(self.file_path)
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(self.data_path + "/" + resultID)
r = 300.0 / result.shape[1]
dim = (300, int(result.shape[0] * r))
resized = cv2.resize(result, dim, interpolation=cv2.INTER_AREA)
cv2.imshow("RESULT", resized)
cv2.waitKey(0)
cv2.destroyAllWindows()
def query():
if request.method == "POST":
f = request.files["file"]
if not (f and allowed_file(f.filename)):
return jsonify({
"error": 1001,
"msg": "请检查上传的图片类型，仅限于png、PNG、jpg、JPG、bmp"
})
basepath = os.path.dirname(__file__) # 当前文件所在路径
# 注意：没有的文件夹一定要先创建，不然会提示没有该路径
upload_path = os.path.join(basepath, "static/queries",
secure_filename(f.filename))
# upload_path = os.path.join(basepath, 'static/images','test.jpg') #注意：没有的文件夹一定要先创建，不然会提示没有该路径
f.save(upload_path)
import cv2
# 使用Opencv转换一下图片格式和名称
img = cv2.imread(upload_path)
cv2.imwrite(os.path.join(basepath, "static/queries", "test.jpg"), img)
RESULTS_ARRAY = []
SCORE_ARRAY = []
cd = ColorDescriptor((8, 12, 3))
features = get_features(cd, FEATURES, img)
searcher = Searcher(INDEX)
results = searcher.search(features, SEARCHER)
# loop over the results, displaying the score and image name
for (score, resultID) in results:
RESULTS_ARRAY.append(resultID)
SCORE_ARRAY.append(score)
return render_template(
"query_ok.html",
results=(RESULTS_ARRAY[:5]),
scores=(SCORE_ARRAY[:5]),
name=f.filename,
)
return render_template("query.html")
def search():
if request.method == "POST":
RESULTS_ARRAY = []
# get url
image_url = request.form.get("img")
try:
# initialize the image descriptor
cd = ColorDescriptor((8, 12, 3))
import cv2
image_url = "polydomino/" + image_url[1:]
query = cv2.imread(image_url)
features = get_features(cd, FEATURES, query)
# perform the search
searcher = Searcher(INDEX)
results = searcher.search(features, SEARCHER)
# loop over the results, displaying the score and image name
for (score, resultID) in results:
RESULTS_ARRAY.append({
"image": str(resultID),
"score": str(score)
})
# return success
return jsonify(results=(RESULTS_ARRAY[:5]))
except:
# return error
jsonify({"sorry": "Sorry, no results! Please try again."}), 500
import cv2
import json
import csv
from scipy.stats.mstats import kruskalwallis
"""
Indexes a set of images by corner and center using a HSV color histogram and stores indices. Requires colordescriptor.py.
"""
# ap = argparse.ArgumentParser()
# ap.add_argument("-d", "--dataset", required = True,
# help = "Path to the directory that contains the images to be indexed")
# ap.add_argument("-i", "--index", required = True,
# help = "Path to where the computed index will be stored")
# args = vars(ap.parse_args())
dataset = 'Opistognathidae'
# initialize the color descriptor
cd = ColorDescriptor(
(8, 12, 3)) # HSV binning -- 8 hue, 12 saturation, 3 value
# open output
# output = open(args["index"], "w")
kw = []
# loop over images
# for imagePath in glob.glob(args["dataset"] + "/*.jpg"):
for imagePath in glob.glob(dataset + "/*.jpg"):
imageID = imagePath[imagePath.rfind("/") + 1:]
image = cv2.imread(imagePath)
# describe image
features = cd.describe(image)
import argparse
import cv2
import time
# construct the argument parser and parse the arguments
ap = argparse.ArgumentParser()
ap.add_argument("-i", "--index", default='index.csv',
help="Path to where the computed index will be stored")
ap.add_argument("-l", "--limit", default=10,
help="Query result limit")
ap.add_argument("-d", "--dist", default='euclidean',
help="Distance model")
ap.add_argument("-q", "--query", required=True,
help="Path to the query image")
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, int(args["limit"]), args["dist"])
# loop over the results
for (score, resultID) in results:
# load the result image and display it
print resultID
# import the necessary packages
from colordescriptor import ColorDescriptor
import os
import matplotlib.pyplot as plt
from datetime import datetime as d
# initialize the color descriptor
cd = ColorDescriptor()
dir_images = 'Corel'
imgs = os.listdir(dir_images)
# Set the timer
now = d.now()
current = now.strftime("%H:%M:%S")
print(current)
# Store imageID and LBP features
output = open("lbp_value.csv", "w")
for imgnm in imgs:
img_rgb = plt.imread(os.path.join(dir_images,imgnm))
imageID = imgnm
features = cd.calculate_lbp(img_rgb)
# write the features to file
features = [str(f) for f in features]
output.write("%s,%s\n" % (imageID, ",".join(features)))
output.close()
from colordescriptor import ColorDescriptor
import argparse
import glob
import cv2
# construct an argument parser
ap = argparse.ArgumentParser()
ap.add_argument("-d", "--dataset", required=True,
help = "Path to the directory that contains the images to be indexed")
ap.add_argument("-i", "--index", required=True,
help = "Path of file where the computed index will be stored")
# vars() return a dict, it can be retrieved as args[dataset]/args[index]
args = vars(ap.parse_args())
# in HSV, 8 bins for hue, 12 bins for saturation, 3 for values(brightness)
cd = ColorDescriptor((8, 12, 3))
# open output index file for writing
output = open(args["index"], "w")
# use glob to grab the image files in path and iterate them
for imagePath in glob.glob(args["dataset"] + "/*.jpg"):
# extract the image ID(i.e. the unique filename)
imageID = imagePath[imagePath.rfind('/') + 1 :]
image = cv2.imread(imagePath)
features = cd.describe(image)
# write the features to file
features = [str(f) for f in features]
output.write("%s,%s\n" % (imageID, ",".join(features)))
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 = ShapeDescriptor()
#cd = ZernikeMoments(50)
#cd = HogDescriptor()
cd = ColorDescriptor()
# load the query image and describe it
query = cv2.imread(args["query"])
query = cv2.cvtColor(query, cv2.COLOR_BGR2GRAY)
# pad the image with extra white pixels to ensure the
# edges of the pokemon are not up against the borders
# of the image
#query = cv2.copyMakeBorder(query, 15, 15, 15, 15,
# cv2.BORDER_CONSTANT, value = 255)
# invert the image and threshold it
#thresh = cv2.bitwise_not(query)
#thresh[thresh > 0] = 255
# initialize the outline image, find the outermost
# contours (the outline) of the pokemone, then draw
import glob
import cv2
# construct the argument parser and parse the arguments
ap = argparse.ArgumentParser()
ap.add_argument("-d", "--dataset", required = True,
help = "Path to the directory that contains the images to be indexed")
ap.add_argument("-i", "--index", required = True,
help = "Path to where the computed index will be stored")
ap.add_argument("-m", "--mask", required = True,
help = "Path to image mask (same size as images) to break into a grid")
args = vars(ap.parse_args())
print args
# initialize the color descriptor
cd = ColorDescriptor((8, 12, 3),args.mask)
# open the output index file for writing
output = open(args["index"], "w")
# use glob to grab the image paths and loop over them
for imagePath in glob.glob(args["dataset"] + "/*.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
### --dataset: the path to our dog images directory
### --index: the output CSV file containing the image filename and the features associated with each image
ap = argparse.ArgumentParser()
ap.add_argument(
"-d",
"--dataset",
required=True,
help="Path to the directory that contains the images to be indexed")
ap.add_argument("-i",
"--index",
required=True,
help="Path to where the computed index will be stored")
args = vars(ap.parse_args())
# Initialzie the color descriptor - Extract the features
cd = ColorDescriptor((8, 12, 3)) # (Hue, Saturation, Value)
# Write the extracted features into a CSV file
csv = open(args["index"], "w")
for imagePath in glob.glob(args["dataset"] + "/*.png"):
# Assign a unique ID to the extracted images
imageID = imagePath[imagePath.rfind("/") + 1:]
image = cv2.imread(imagePath)
features = cd.describe(image)
features = [str(f) for f in features]
csv.write("%s,%s\n" % (imageID, ",".join(features)))
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", cd.image_resizer(query))
# loop over the results
for (score, resultID) in results:
import glob
import cv2
ap = argparse.ArgumentParser()
ap.add_argument(
"-d",
"--dataset",
required=True,
help="Path to the directory that contains the images to be indexed")
ap.add_argument("-i",
"--index",
required=True,
help="Path to where the computed index will be stored")
args = vars(ap.parse_args())
cd = ColorDescriptor((8, 12, 3))
output = open(args["index"], "w")
# use glob to grab the image paths and loop over them
for imagePath in glob.glob(args["dataset"] + "/*.png"):
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)))
from colordescriptor import ColorDescriptor
from searcher import Searcher
import argparse
import cv2
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())
cd = ColorDescriptor((8, 12, 3))
query = cv2.imread(args["query"])
features = cd.describe(query)
searcher = Searcher(args["index"])
results = searcher.search(features)
query = cv2.resize(query, (960, 540))
cv2.namedWindow("Query", cv2.WINDOW_NORMAL)
cv2.imshow("Query", query)
cv2.waitKey(0)
for (score, resultID) in results:
result = cv2.imread(args["result_path"] + "/" + resultID)
#result = cv2.resize(result, (960, 540))
cv2.namedWindow("Result", cv2.WINDOW_NORMAL)
cv2.imshow("Result", result)
#print(args["result_path"] + "/" + resultID)
cv2.waitKey(0)
# import the necessary packages
from colordescriptor import ColorDescriptor
from searcher import Searcher
import cv2, numpy as np
import matplotlib.pyplot as plt
from datetime import datetime as d
# initialize the image descriptor
cd = ColorDescriptor()
# load the query image
query = cv2.imread('queries/3_301.jpg')
# Set the timer
now = d.now()
current = now.strftime("%H:%M:%S")
print(current)
# describe LBP features and perform the search
lbp_features = cd.calculate_lbp(query)
lbp_searcher = Searcher("lbp_value.csv")
lbp_results = lbp_searcher.search(lbp_features)
print(lbp_results)
# Set the timer
now = d.now()
current = now.strftime("%H:%M:%S")
print(current)
# Plot result images from LBP
from dbhelper import DBHelper
DB = DBHelper()
from sklearn.externals import joblib
from sklearn.preprocessing import scale
from utilities.generateSiftFeatures import generateSiftFeatures
from utilities.generateORBFeatures import generateORBFeatures
from utilities.readImage import readImage
from utilities.showImage import showImage
from utilities.getOpening import getOpening
# initialize the color descriptor
cd = ColorDescriptor((8, 12, 3))
def chi2_distance(feature_vector, features, eps = 1e-10):
dists = []
# compute the chi-squared distance
for i in range(0,len(features)):
try:
d = 0.5 * np.sum([((a - b) ** 2) / (a + b + eps) for (a, b) in zip(feature_vector, features[i])])
dists.append(d)
except:
print features[i], type(features[i])
# return the chi-squared distance
return np.asarray(dists)
"""
ap = argparse.ArgumentParser()
ap.add_argument("-d", "--dataset", required = True,
help = "Path to the directory that contains the images to be indexed")
args = vars(ap.parse_args())
folderPath = args["dataset"]
if not os.path.exists(folderPath + '/Pass'):
os.makedirs(folderPath + '/Pass')
if not os.path.exists(folderPath + '/Fail'):
os.makedirs(folderPath + '/Fail')
# initialize the color descriptor
cd = ColorDescriptor((8, 12, 3)) # HSV binning -- 8 hue, 12 saturation, 3 value
features = []
ctrcorr = []
images = glob.glob(folderPath+'/*.jpg')
for pic in images:
image = cv2.imread(pic)
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
ctr = []
temp = cd.describe(image)
features.append(temp)
for i in temp[0:4]:
from zernika import ZernikeMoments
import argparse
import glob
import cv2
# construct the argument parser and parse the arguments
ap = argparse.ArgumentParser()
ap.add_argument("-d", "--dataset", required = True,
help = "Path to the directory that contains the images to be indexed")
ap.add_argument("-i", "--index", required = True,
help = "Path to where the computed index will be stored")
args = vars(ap.parse_args())
# initialize the Shape descriptor
cd = ColorDescriptor(21)
# open the output index file for writing
output = open(args["index"], "w")
# use glob to grab the image paths and loop over them
for imagePath in glob.glob(args["dataset"] + "/*.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()
# write the features to file
# import the necessary packages
from colordescriptor import ColorDescriptor
import argparse
import glob
import cv2
# construct the argument parser and parse the arguments
ap = argparse.ArgumentParser()
ap.add_argument("-d", "--dataset", required = True,
help = "Path to the directory that contains the images to be indexed")
ap.add_argument("-i", "--index", required = True,
help = "Path to where the computed index will be stored")
args = vars(ap.parse_args())
# initialize the color descriptor
cd = ColorDescriptor((4, 8, 2))
# open the output index file for writing
output = open(args["index"], "w")
# use glob to grab the image paths and loop over them
for imagePath in glob.glob(args["dataset"] + "/*.jpg"):
# 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
required=True,
help="Path to the result path")
ap.add_argument(
"-m",
"--mask",
required=True,
help="Path to image mask (same size as images) to break into a grid")
ap.add_argument(
"-g",
"--gridsize",
required=True,
help="Dimension for a single width/height for the square grid mask")
args = vars(ap.parse_args())
# initialize the image descriptor
cd = ColorDescriptor((8, 12, 3), args["mask"], args["gridsize"])
# load the query image and describe it
query = cv2.imread(args["query"])
features = cd.describe(query)
# perform the search
searcher = Searcher(args["index"])
scores, image_names = searcher.search(features)
# display the query
cv2.imshow("Query", query)
# loop over the results
for x in range(0, len(scores)):
resultID = image_names[x]
# -u = path to the image you want to upload
# -i = path to the csv index containing all feature vectors
from colordescriptor import ColorDescriptor
from searcher import Searcher
from imutils import build_montages
import argparse
import cv2
ap = argparse.ArgumentParser()
ap.add_argument("-i", "--index", type=str, default="index.csv")
ap.add_argument("-u", "--upload", required=True)
args = vars(ap.parse_args())
cd = ColorDescriptor((8, 12, 3))
upload = cv2.imread(args["upload"])
features = cd.describe(upload)
searcher = Searcher(args["index"])
results = searcher.search(features)
i = 0
locs = []
similars = []
for (score, resultID) in results:
result = cv2.imread(resultID)
if (i < 1):
if score < 1.0:
title = "Found: " + resultID
else:
title = "Not Found - Best Match: " + resultID
