def Disease_Detection(image_path):
# initialize the image descriptor
cd = ColorDescriptor((8, 12, 3))
# load the query image and describe it
# query = cv2.imread(args["query"])
filenames = listdir(image_path)
a = [filename for filename in filenames if filename.endswith('.png')]
path = image_path + '/' + a[0]
print(path)
query = cv2.imread(path)
print("query==", query)
features = cd.describe(query)
# perform the search
#searcher = Searcher(args["index"])
searcher = Searcher("../index.csv")
results = searcher.search(features)
results.pop(0)
print("==============", results)
print()
# disease_name = results[1][1].split("_")[0].split("\\")[1] #.... extracting disease name
# print("**********",disease_name)
return (results[1][1].split("_")[0])
# Disease_Detection('/home/vishal/Dropbox/ML/Plant_Disease_Detection/queries')
def find_similar(query):
"""
query (str): path to the img
ex : queries2\img_231.jpg
return
list_img (list) : 3 paths of img similar
"""
# initialize list
list_img = []
# initialize the image descriptor
cd = ColorDescriptor((8, 12, 3))
# load the query image and describe it
query = cv2.imread(query)
features = cd.describe(query)
# perform the search
searcher = Searcher("index2.csv")
results = searcher.search(features)
# display the query
# cv2.imshow("Query", query)
# loop over the results
for (score, resultID) in results:
# add img to list
list_img.append(str(resultID).replace("\\", "/"))
return list_img
def check(limit):
args = {"query": "queries/a.jpg", "index": "whpu.csv"}
#初始化图像描述符
cd = ColorDescriptor((8, 12, 3))
#从磁盘读取待搜索图像
query = cv2.imread(args["query"])
#提取该图像的特征
features = cd.describe(query)
#执行搜索
#使用提取到的特征进行搜索，返回经过排序后的结果列表
searcher = Searcher(args["index"])
results = searcher.search(features, limit)
#显示出待搜索的图像
#cv2.imshow("Query", query)
result_files_data = []
#遍历搜索结果，将相应的图像显示在屏幕上
for (score, resultID) in results:
result = "dataset/" + resultID
result_files_data.append([score, result])
return result_files_data
def search():
if request.method == "POST":
RESULTS_ARRAY = []
# get url
image_url = request.form.get('img')
try:
# initialize the image descriptor
ird = ImageRetrieveDeep("", "")
path_img = os.path.join(os.getcwd(), image_url[1:])
des = ird.r_mac_descriptor(path_img)
# perform the search
searcher = Searcher(des)
results = searcher.search(des, "static/dataset_jpg", "descriptor")
# 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[::-1][:10]))
except:
# return error
jsonify({"sorry": "Sorry, no results! Please try again."}), 500
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))
# load the query image and describe it
from skimage import io
query = io.imread(image_url)
features = cd.describe(query)
# perform the search
searcher = Searcher(INDEX)
results = searcher.search(features)
# 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[::-1][:3]))
except:
# return error
jsonify({"sorry": "Sorry, no results! Please try again."}), 500
def search_results(filename, index_file):
# load the query image and describe it
query = cv2.imread(filename)
features = cd.describe(query)
# perform the search
searcher = Searcher(index_file)
results = searcher.search(features)
def show_results_imgs(self):
if (self.count == 0 and self.tf_input.get() == ""):
tkMessageBox.showwarning(
"Alert", "Please enter a search term, or upload image.")
else:
if (self.filename == self.currFilename):
self.isSameFile = True
else:
self.isSameFile = False
# user now wants to commence the search; load frame for displaying results of query
self.isUploadingImage = False
# flush out old results
self.result_img_frame.destroy()
self.result_img_frame = Frame(self.master)
self.result_img_frame.pack()
flags = self.get_options()
# print(flags)
# perform the search
searcher = Searcher("index.csv", "index_semantics.csv",
"index_text.csv", "index_deeplearning.csv",
flags, self.tf_input.get())
results = searcher.search(self.queryfeatures, self.querysemantics,
self.querytext, self.querycategory)
if (len(results) == 0):
pass
else:
# show result pictures
COLUMNS = 8
image_count = 0
for (score, resultID) in results:
# load the result image and display it
image_count += 1
r, c = divmod(image_count - 1, COLUMNS)
im = Image.open(resultID)
resized = im.resize((100, 100), Image.ANTIALIAS)
tkimage = ImageTk.PhotoImage(resized)
myvar = Label(self.result_img_frame, image=tkimage)
myvar.image = tkimage
myvar.grid(row=r, column=c)
if (self.toLogResults):
numRelevant = logResults.get_precision(self.queryImageCategory,
results)
print(numRelevant)
self.currFilename = self.filename
self.result_img_frame.mainloop()
def search():
if request.method == "POST":
'''file = request.files['file']
filename = secure_filename(file.filename)
file.save(os.path.join(app.config['UPLOAD_FOLDER'], filename))
RESULTS_ARRAY = []
print(filename)'''
RESULTS_ARRAY = []
# get url
image_url = request.form.get('img')
print(image_url)
try:
# initialize the image descriptor
cd = ColorDescriptor((8, 12, 3))
# load the query image and describe it
from skimage import io
import cv2
#query = io.imread("/home/kene/Documents/PyImageSearch/3D Histogram Descriptor Method With Web Interface/app/"+image_url)
query = cv2.imread("/home/kene/Documents/Projects/Image-Search-Engine/app/static/queries/"+image_url);
#query = io.imread(image_url)
#query = (query * 255).astype("uint8")
#(r, g, b) = cv2.split(query)
#query = cv2.merge([b, g, r])
#query = cv2.cvtColor(query, cv2.COLOR_BGR2RGB)
features = cd.describe(query)
# perform the search
searcher = Searcher(INDEX)
results = searcher.search(features)
# 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[::-1][:101]), preview="queries/"+image_url)
#resultSet=jsonify(results=(RESULTS_ARRAY[::-1][:5]))
#return render_template('index.html', preview = "static/queries/"+filename, resultSet = jsonify(results=(RESULTS_ARRAY[::-1][:5])))
except Exception, e:
print(str(e))
# return error
return jsonify({"sorry": "Sorry, no results! Please try again."}), 500
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))
# load the query image and describe it
from skimage import io
import cv2
# query = io.imread(image_url)
# query = (query * 255).astype("uint8")
# (r, g, b) = cv2.split(query)
# query = cv2.merge([b, g, r])
image_url = "app/" + image_url[1:]
# print "图像url路径:", image_url
# print os.getcwd()
# print sys.path[0]
query = cv2.imread(image_url)
# print "读取成功！"
features = cd.describe(query)
# print "描述子生成成功"
# perform the search
searcher = Searcher(INDEX)
results = searcher.search(features)
# 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
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))
# load the query image and describe it
from skimage import io
import cv2
# query = io.imread(image_url)
# query = (query * 255).astype("uint8")
# (r, g, b) = cv2.split(query)
# query = cv2.merge([b, g, r])
image_url = "app/" + image_url[1:]
# print "图像url路径:", image_url
# print os.getcwd()
# print sys.path[0]
query = cv2.imread(image_url)
# print "读取成功！"
features = cd.describe(query)
# print "描述子生成成功"
# perform the search
searcher = Searcher(INDEX)
results = searcher.search(features)
# 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
def predict(self,location):
# initialize the image descriptor
cd = ColorDescriptor((8, 12, 3))
print(location)
query = cv2.imread(location)
features = cd.describe(query)
searcher = Searcher("index.csv")
results = searcher.search(features)
final_res = []
for (score, resultID) in results:
final_res.append(resultID)
print(score,resultID)
return final_res[0]
def upload():
file = request.files['file']
print('upload', file)
if file and allowed_file(file.filename):
filename = secure_filename(file.filename)
file.save(os.path.join(app.config['UPLOAD_FOLDER'], filename))
if request.method == "POST":
RESULTS_ARRAY = []
# get url
try:
# initialize the image descriptor
cd = ColorDescriptor((8, 12, 3))
# load the query image and describe it
from skimage import io
filename = os.path.join(app.config['UPLOAD_FOLDER'], filename)
print '###' + filename
query = io.imread(filename)
print 'shape'
features = cd.describe(query)
# perform the search
searcher = Searcher(INDEX)
results = searcher.search(features)
# 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[::-1][:3]),
file=file.filename)
except:
# return error
return jsonify({"sorry":
"Sorry, no results! Please try again."}), 500
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))
# load the query image and describe it
from skimage import io
import cv2
query = io.imread(image_url)
# query = (query * 255).astype("uint8")
# (r, g, b) = cv2.split(query)
# query = cv2.merge([b, g, r])
# cv2.imshow("Image", query)
query = cv2.cvtColor(query, cv2.COLOR_BGR2RGB)
features = cd.describe(query)
# perform the search
searcher = Searcher(INDEX)
results = searcher.search(features)
# 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[::-1]))
# return 'ok'
except Exception as ex:
template = "An exception of type {0} occurred. Arguments:\n{1!r}"
message = template.format(type(ex).__name__, ex.args)
print(message)
return jsonify({"sorry":
"Sorry, no results! Please try again."}), 500
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))
# load the query image and describe it
from skimage import io
import cv2
print("image_url ", image_url)
path_img = os.path.join(os.getcwd(), image_url[1:])
query = cv2.imread(path_img, cv2.COLOR_BGR2RGB)
print("describe")
features = cd.describe(query)
print("Searcher")
# perform the search
searcher = Searcher(INDEX)
print("search")
results = searcher.search(features)
print(INDEX)
print("image")
# loop over the results, displaying the score and image name
for (score, resultID) in results:
RESULTS_ARRAY.append({
"image": str(resultID),
"score": str(score)
})
print("Before success")
# return success
return jsonify(results=(RESULTS_ARRAY[::-1][:3]))
except:
# return error
jsonify({"sorry": "Sorry, no results! Please try again."}), 500
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))
# load the query image and describe it
from skimage import io
import cv2
query = cv2.imread(
os.path.join(os.path.dirname(__file__),
'static/images/' + image_url))
features = cd.describe(query)
# perform the search
searcher = Searcher(INDEX)
results = searcher.search(features)
# 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[:101]),
preview="images/" + image_url)
except Exception as e:
print(str(e))
# return error
return jsonify({"sorry":
"Sorry, no results! Please try again."}), 500
def func2(command):
folder = 'target'
command = os.path.join(folder, command)
# initialize the image descriptor
cd = ColorDescriptor((8, 12, 3))
# load the query image and describe it
query = cv2.imread(command)
features = cd.describe(query)
# perform the search
searcher = Searcher("index.csv")
results = searcher.search(features)
# loop over the results
result = []
for (score, resultp) in results:
url, imgname, imgurl = resultp
doct = {"url": url, 'name': imgname, 'imgurl': imgurl}
result.append(doct)
return result
def Disease_Detection(image_path):
# initialize the image descriptor
cd = ColorDescriptor((8, 12, 3))
# load the query image and describe it
# query = cv2.imread(args["query"])
filelist = [
file for file in os.listdir(image_path) if file.endswith('.png')
]
query = cv2.imread(image_path + filelist[0])
# query = cv2.imread( 'D:\\projects\\Plant_Disease_Detection\\dataset\\CollarRot_1.png' )
features = cd.describe(query)
# perform the search
#searcher = Searcher(args["index"])
searcher = Searcher(image_path + "/index.csv")
results = searcher.search(features)
results.pop(0)
disease_name = results[1][1].split("_")[0].split("\\")[
1] #.... extracting disease name
return (disease_name)
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)
c = 0
r = []
# 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)
r.append(result)
cv2.waitKey(0)
os.chdir(
'C:\\Users\\Aaron Stone\\Desktop\\Minor Repository\\search-engine\\results'
)
# 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)
#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('monkey.jpg')
#%%
features = cd.describe(query)
#%%
# perform the search
searcher = Searcher('index.csv')
results = searcher.search(features)
# display the query
cv2.imwrite("Query.jpg", query)
#%%
# loop over the results
import os
path = 'C:/Users/User/Documents/fyp/image-extraction/module1/results'
length = len(results)
with open("C:/Users/User/Documents/fyp/image-extraction/module1/results/candidate.txt","w") as f1:
for i, (score,resultID) in zip(range(length),results):
x = resultID.split(".")
y = str(x[0]) + '.txt'
with open(y) as f:
def call_back(imagepath):
print(imagepath)
# if status.get() == 1:
e = tk.StringVar()
out = tk.Label(root, textvariable=e)
out.grid(row=8, columnspan=5)
global i
# i += 1
e.set(imagepath)
# nowtime = os.popen('date')
# print(nowtime.read())
# command like : python search.py --index index.csv --query queries/108100.png --result-path dataset
imageID = imagepath[imagepath.rfind("\\") + 1:]
querypath = "queries/" + imageID
# load the query image and describe it
query = cv2.imread(querypath)
features = cd.describe(query)
# perform the search
searcher = Searcher("index.csv")
results = searcher.search(features)
# display the query
# cv2.imshow("Query", query)
# testpath = r"C:\Users\DYL18\Desktop\Graduation_design\vacation-image-search-engine\dataset\103500.png"
'''
a = Image.open(testpath)
b = resize(w_box, h_box, a)
d = ImageTk.PhotoImage(image=b)
# imageOutput = tk.Button(root,text = "haha")
imageOutput = tk.Button(root, image=d, width=w_box, height=h_box)
imageOutput.grid(row=12, column=0, rowspan=6)
print("hahaha")
'''
j = 0
k = 0
#destroy all of button or label here:
global tk_button
for a in tk_button:
print(a)
a.destroy()
tk_button = []
'''
global button_num
if button_num > 0:
button_num = button_num - 1
while button_num != 0:
tk_button[button_num].destroy()
button_num = button_num - 1
# for but in button_num:
# tk_button[but].destroy()
button_num = 0
'''
# tk_im_path = []
# loop over the results
for (score, resultID) in results:
# load the result image and display it
# path2 = "dataset" + "\\" + resultID
path2 = datapath + "\\" + resultID
print(path2)
pil_image2 = Image.open(path2)
# tk_im_path.append(pil_image2)
pil_image_resized2 = resize(w_box, h_box, pil_image2)
# photograph = ImageTk.PhotoImage(pil_image_resized2)
tk_im.append(ImageTk.PhotoImage(pil_image_resized2))
imageOutput = tk.Button(root,
image=tk_im[j],
width=w_box,
height=h_box)
tk_button.append(imageOutput)
tk_button[j].image = tk_im[j]
tk_button[j].grid(row=int(9 + (k * 8)), column=int(j % 6), rowspan=6)
# button_num = button_num + 1
# imageOutput.configure(image=tk_im[j])
# imageOutput.image = tk_im[j]
# imageOutput.grid(row=int(9 + (k * 8)), column=int(j % 6), rowspan=6)
# imageOutput.destroy()
j = j + 1
k = int(j / 6)
# root.update_idletasks()
for a in tk_button:
print(a)
'''
def call_back(self, imagepath):
print(imagepath)
e = tk.StringVar()
out = tk.Label(self.canvas2, textvariable=e)
# self.canvas.create_window(300, 200, window=out)
# self.canvas.grid(row=0, column=0)
global i
e.set(imagepath)
# command like : python search.py --index index.csv --query queries/108100.png --result-path dataset
imageID = imagepath[imagepath.rfind("\\") + 1:]
# * querypath = "queries/" + imageID
# querypath = self.root.queryfolder + "\\" + imageID
querypath = self.root.indexcsvpath + imageID
querypath = imagepath
print("Attention !! this is the query path: ")
print(querypath)
# load the query image and describe it
query = cv2.imread(querypath)
print(query)
# 调试
cv2.imshow("Result", query)
cv2.waitKey(0)
features = cd.describe(query)
# perform the search
# searcher = Searcher("index.csv")
p = self.root.indexcsvpath + "index.csv"
searcher = Searcher(p)
results = searcher.search(features)
j = 0
k = 0
# destroy all of button or label here:
global button_list
for a in button_list:
print(a)
a.destroy()
button_list = []
# tk_im_path = []
# loop over the results
for (score, resultID) in results:
# load the result image and display it
# 必须要选择数据集才能得到正确的datafolder路径并因此得出结果
path2 = self.root.datafolder + "\\" + resultID
print(path2)
pil_image2 = Image.open(path2)
pil_image_resized2 = self.resize(w_box, h_box, pil_image2)
# tk_im.append(ImageTk.PhotoImage(pil_image_resized2))
# imageOutput = tk.Button(root, image=tk_im[j], width=w_box, height=h_box)
a = ImageTk.PhotoImage(pil_image_resized2)
imageOutput = tk.Button(self.canvas2, image=a, width=w_box, height=h_box)
button_list.append(imageOutput)
# button_list[j].image = tk_im[j]
button_list[j].image = a
self.canvas2.create_window(150+250*(int(j % 6)), 100 + 250 * k, window=button_list[j])
# self.canvas2.create_window(350 + 250 * k, 100*(int(j % 6)), window=button_list[j])
# button_list[j].grid(row=int(9 + (k * 8)), column=int(j % 6), rowspan=6)
j = j + 1
k = int(j / 6)
for a in button_list:
print(a)
import numpy as np
import argparse
import cPickle
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 we just indexed")
ap.add_argument("-i", "--index", required = True,
help = "Path to where we stored our index")
args = vars(ap.parse_args())
# load the index and initialize our searcher
index = cPickle.loads(open(args["index"]).read())
searcher = Searcher(index)
# loop over images in the index -- we will use each one as
# a query image
for (query, queryFeatures) in index.items():
# perform the search using the current query
results = searcher.search(queryFeatures);print("query :"+str(query))
# load the query image and display it
path = str(query)#args["dataset"] + "/%s" % (query)
queryImage = cv2.imread(path)
cv2.imshow("Query", queryImage)
print "query: %s" % (query)
# initialize the two montages to display our results --
# we have a total of 25 images in the index, but let's only
# display the top 10 results; 5 images per montage, with
# images that are 400x166 pixels
def searchVideo(query):
index = "index.csv"
# initialize the image descriptor
cd = ColorDescriptor((8, 12, 3))
# load the query video and describe it
temp = "/home/ankita/btp7/CBVR/temp"
i = 0
# print path_video
cap = cv2.VideoCapture("uploadedFiles/" + query)
if cap.isOpened():
rval, frame = cap.read()
else:
rval = False
# print rval
# print query
while rval:
rval, frame = cap.read()
if not rval: break
name = str(i) + '.png'
cv2.imwrite(os.path.join(temp, name), frame)
i = i + 1
if cv2.waitKey(1) & 0xFF == ord('q'):
break
cap.release()
avg_int = []
no_frames = i
# print no_frames
for img in glob.glob(temp + '/*png'):
image = io.imread(img)
im = rgb2gray(image)
avg_int.append(np.mean(im))
# print(np.mean(im))
# for i in avg_int:
# print i
diff = [avg_int[p + 1] - avg_int[p] for p in range(len(avg_int) - 1)]
# y = diff
# plt.plot(y)
# plt.show()
# print diff.index(min(diff))
name1 = temp + '/' + str(diff.index(min(diff))) + '.png'
# print diff.index(max(diff))
name2 = temp + '/' + str(diff.index(max(diff))) + '.png'
# image1 = cv2.imread(name1)
# save_name = fold +"#"+ video.split('.')[0]+'_1.png'
# cv2.imwrite(os.path.join(data ,save_name ), image1)
# image2 = cv2.imread(name2)
# save_name = fold +"#"+ video.split('.')[0]+'_2.png'
# cv2.imwrite(os.path.join(data ,save_name ), image2)
query_image = cv2.imread(name2)
for the_file in os.listdir(temp):
file_path = os.path.join(temp, the_file)
try:
if os.path.isfile(file_path):
os.unlink(file_path)
#elif os.path.isdir(file_path): shutil.rmtree(file_path)
except Exception as e:
print(e)
# query = cv2.imread(args["query"])
features = cd.describe(query_image)
# perform the search
searcher = Searcher(index)
results = searcher.search(features)
result_images = []
# display the query
# cv2.imshow("Query", query_image)
# loop over the results
path_video = []
# path_video.append("uploadedFiles/"+query)
for (score, resultID) in results:
# load the result image and display it
result = cv2.imread("data/" + resultID)
result_images.append(result)
fold = resultID.split('#')[0]
x = resultID.split('#')[1]
name = x.rsplit('_', 1)[0]
# print fold
arr = os.listdir('data/' + fold)
ext = arr[0].split('.')[1]
video = 'data/' + fold + '/' + name + '.' + ext
path_video.append(video)
# print video
# cv2.imshow("Result", result)
# cv2.waitKey(0)
return path_video
def main():
images=glob.glob("uploads\\*.jpg")
for image_file in images:
# img_bgr = cv2.imread(image_file)
# height, width, channel = img_bgr.shape
# img = cv2.cvtColor(img_bgr, cv2.COLOR_BGR2GRAY)
# k=img.shape[0]
# l=img.shape[1]
# original = np.zeros((height, width,3), np.uint8)
# encrypted = np.zeros((height, width,3), np.uint8)
# decrypted = np.zeros((height, width,3), np.uint8)
# img_lbp = np.zeros((height, width,3), np.uint8)
# enc_img_lbp = np.zeros((height, width,3), np.uint8)
# randomnumber={}
# for i in range (k):#traverses through height of the image
# for j in range (l): #traverses through width of the image
# # original[i,j]=img[i,j]
# randomnumber[i,j]=np.random.randint(0,255)
# # img_lbp[i, j] = lbp_calculated_pixel(img, i, j)
# # Encryption
# if(((randomnumber[i,j]>=128) and (img[i,j]>=128))):
# encrypted[i,j]=img[i,j]-128
# temp=img[i,j]-128
# elif(((randomnumber[i,j]<128) and (img[i,j]<128))):
# encrypted[i,j]=img[i,j]
# temp=img[i,j]
# elif(((randomnumber[i,j]>=128) and (img[i,j]<128))):
# encrypted[i,j]=img[i,j]+128
# temp=img[i,j]+128
# else:
# encrypted[i,j]=img[i,j]
# temp=img[i,j]
# # enc_img_lbp[i, j] = lbp_calculated_pixel(img, i, j)
DoG(image_file)
key=cv2.imread('DoG\\'+image_file.split('\\')[1])
img1 = cv2.cvtColor(key, cv2.COLOR_BGR2GRAY)
# cv2.imshow("k",img1)
# cv2.waitKey(0)
# cv2.destroyAllWindows()
height, width, channel = key.shape
k1=img1.shape[0]
l1=img1.shape[1]
key_lbp = np.zeros((height, width,3), np.uint8)
for i in range (k1):#traverses through height of the image
for j in range (l1):
key_lbp[i, j] = lbp_calculated_pixel(img1, i, j)
hist_lbp = cv2.calcHist([key_lbp], [0], None, [256], [0, 256])
features = [str(i)[1:-2] for i in hist_lbp]
searcher = Searcher("index.csv")
results = searcher.search(features)
#saving encrypted image
#print(image_file);
# encrypt_image=Image.fromarray(encrypted)
#imageID = image_file[image_file.rfind("/") + 1:]
#encrypt_image.save("encryptedimages\\"+image_file.split('\\')[1])
# output.write("%s,%s\n" % (image_file.split('\\')[1],",".join(features)))
#cv2.imshow("Query", img_bgr)
for (score, resultID) in results:
# load the result image and display it
#print(resultID)
result = cv2.imread("encryptedimages"+ "\\" + resultID.split(".")[0]+".bmp")
random_re2=cv2.imread("random\\"+resultID.split(".")[0]+".bmp")
random_re1 = cv2.cvtColor(random_re2, cv2.COLOR_BGR2GRAY)
en_re1 = cv2.cvtColor(result, cv2.COLOR_BGR2GRAY)
height, width, channel = result.shape
k2=random_re1.shape[0]
l2=random_re1.shape[1]
decrypted = np.zeros((height, width,3), np.uint8)
for i in range (k2):#traverses through height of the image
for j in range (l2):
if(((random_re1[i,j]<128) and (en_re1[i,j]<128))):
decrypted[i,j]=en_re1[i,j]
elif(((random_re1[i,j]>=128) and (en_re1[i,j]<128))):
decrypted[i,j]=en_re1[i,j]+128
elif(((random_re1[i,j]>=128) and (en_re1[i,j]>=128))):
decrypted[i,j]=en_re1[i,j]-128
else:
decrypted[i,j]=en_re1[i,j]
cv2.imwrite("resultimages\\"+resultID,decrypted)
# cv2.imshow(resultID, result)
# cv2.waitKey(0)
# encrypt_image.save("resultimages\\"+image_file.split('\\')[1])
#output.close()
# caluclate histogram and plot it
# hist_lbp = cv2.calcHist([img_lbp], [0], None, [256], [0, 256])
# enc_hist_lbp = cv2.calcHist([enc_img_lbp], [0], None, [256], [0, 256])
end=time.time()
print(end-start )
hog = cv2.HOGDescriptor(winSize, blockSize, blockStride,
cellSize, nbins, derivAperture,
winSigma, histogramNormType,
L2HysThreshold, gammaCorrection,
nlevels)
queryFeatures = hog.compute(queryImage, winStride, padding,
locations)
eps = 1e-7
queryFeatures = queryFeatures.astype("float")
queryFeatures /= (queryFeatures.sum() + eps)
# load the index perform the search
file = open(args["index"] + ".pkl", 'rb')
index = pickle.load(file)
searcher = Searcher(index)
results = searcher.search(queryFeatures)
# initialize the two montages to display our results --
# we have a total of 25 images in the index, but let's only
# display the top 10 results; 5 images per montage, with
# images that are 400x166 pixels
montageA = np.zeros((166 * 5, 400, 3), dtype="uint8")
montageB = np.zeros((166 * 5, 400, 3), dtype="uint8")
# loop over the top ten results
for j in range(0, 10):
# grab the result (we are using row-major order) and
# load the result image
(score, imageName) = results[j]
path = args["dataset"] + os.sep + "%s" % (imageName)
def searcher():
return Searcher(indexPath, verbose)
output.write(str(args["my_query"])+"\n")
output.write(str(args["l5"])+"\n")
elif(int(args["l0"]) == 5):
output.write(str(args["l0"])+"\n")
output.write(str(args["l1"])+"\n")
output.write(str(args["l2"])+"\n")
output.write(str(args["l3"])+"\n")
output.write(str(args["l4"])+"\n")
output.write(str(args["my_query"])+"\n")
# output.write(str(features)+"\n")
# perform the search
#client = pymongo.MongoClient("localhost", 27017)
#db = client.mm
#row = db.index.find({ "tags" : args["tags"] })
searcher = Searcher()
results = searcher.search(args["my_query"], 8, args["descript"])
# display the query
#cv2.imshow("Query", query)
#for (score, resultID) in results:
#print(resultID)
# loop over the results
i = 0
def call_back(imagepath):
print(imagepath)
e = tk.StringVar()
out = tk.Label(root, textvariable=e)
out.grid(row=8, columnspan=5)
global i
e.set(imagepath)
# command like : python search.py --index index.csv --query queries/108100.png --result-path dataset
imageID = imagepath[imagepath.rfind("\\") + 1:]
querypath = "queries/" + imageID
# load the query image and describe it
query = cv2.imread(querypath)
features = cd.describe(query)
# perform the search
searcher = Searcher("index.csv")
results = searcher.search(features)
j = 0
k = 0
#destroy all of button or label here:
global button_list
for a in button_list:
print(a)
a.destroy()
button_list = []
# tk_im_path = []
# loop over the results
for (score, resultID) in results:
# load the result image and display it
path2 = datapath + "\\" + resultID
print(path2)
pil_image2 = Image.open(path2)
pil_image_resized2 = resize(w_box, h_box, pil_image2)
# tk_im.append(ImageTk.PhotoImage(pil_image_resized2))
# imageOutput = tk.Button(root, image=tk_im[j], width=w_box, height=h_box)
a = ImageTk.PhotoImage(pil_image_resized2)
imageOutput = tk.Button(root, image=a, width=w_box, height=h_box)
button_list.append(imageOutput)
# button_list[j].image = tk_im[j]
button_list[j].image = a
button_list[j].grid(row=int(9 + (k * 8)), column=int(j % 6), rowspan=6)
j = j + 1
k = int(j / 6)
for a in button_list:
print(a)
import argparse
import cv2
import numpy
from sklearn import datasets, svm, metrics
import tensorflow==2.0
# 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
cd = ColorDescriptor((8, 12, 3))
query = cv2.imread(args["query"]) # load query image and describe it
features = cd.describe(query)
searcher = Searcher(args["index"]) # perform the search process
results = searcher.search(features)
cv2.imshow("Query", query) # display query
for (score, resultID) in results:
result = cv2.imread(args["result_path"] + "/" + resultID) # load the result
cv2.imshow("Result", result) # display it
cv2.waitKey(0)
def search():
# --- argument list --- #
# construct the argument parse and parse the arguments
ap = argparse.ArgumentParser()
ap.add_argument("-prediction", "--prediction_file", required=True,
help="path to the prediction file to be parsed")
ap.add_argument("-exif", "--exif_file", required=True,
help="path to the exif file to be parsed")
# mode to search for guns or just objects
ap.add_argument("-s", "--search_list", type=str, default="gun",
help="list of search items delimited by comma")
ap.add_argument('-v', '--verbose', action='store_true',
help="Print lots of debugging statements")
ap.add_argument("-k", "--top_k", type=int, default=20,
help="retrieve the top-k predictions, default is 20")
ap.add_argument("-t", "--threshold", type=float,
help="probability threshold value in decimals ex. 0.75, default is 0.50")
args = vars(ap.parse_args())
# --- validation --- #
# ensure that the arguments supplied are pathnames
if not os.path.isfile(args["prediction_file"]):
raise AssertionError("The --prediction_file command line argument should exist and should be write-able.")
# ensure that the path is write-able or exists
if not os.path.isfile(args["exif_file"]):
raise AssertionError("The --exif_file command line argument should exist and should be write-able.")
pred_file = args["prediction_file"]
exif_file = args["exif_file"]
verbose = False
if args["verbose"]:
verbose = True
threshold = None
if args["threshold"]:
threshold = args["threshold"]
top_k = 20
if args["top_k"]:
top_k = args["top_k"]
always_verbose = True
# show argument list
s = "[INFO] Argument List:\n" + "\n".join([("-->{}: {}".format(x, args[x])) for x in args])
log(s, always_verbose) # always display
# TODO - list suggestion from imagenet 1000 classes
# DEBUG:
# args["search_list"] = "gun,water,SCUBA diver, van"
mode, search_list = validate_search_items(args["search_list"])
log("[INFO] Starting to load prediction file {} and exif file {} ...".format(pred_file, exif_file), always_verbose)
index_path = os.path.dirname(__file__)
s = Searcher(index_path, verbose)
image_list = []
image_path_list = []
## --------------- prediction --------------- ##
if 1 == mode: # guns
image_list = s.search_gun(pred_file, top_k, threshold)
elif 2 == mode: # others
image_list = s.search_list(pred_file, search_list, top_k, threshold)
else:
print("[INFO] Dont waste my time, nothing to search so no results found")
if len(image_list) > 0:
image_path_list = list(map(lambda x: x[0], image_list))
if verbose:
for img in image_list:
log("\t{} | {} | {:.2f}%".format(img[0], img[1], float(img[2]) * 100), verbose)
log("[INFO] Total images found: {}".format(len(image_path_list)), always_verbose)
## --------------- exif info --------------- ##
exif_info = parse_exif(exif_file, image_path_list, verbose)
log("[INFO] Total exif information: {}".format(len(exif_info)), always_verbose)
# print("length exif_info=",len(exif_info))
# print(image_list)
# TODO: Convert to json results
log("[INFO] Completed Search ", always_verbose)
from pyimagesearch import logger
conf = logger.Logger()
# 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 we just indexed")
ap.add_argument("-i", "--index", required=True,
help="Path to where we stored our index")
args = vars(ap.parse_args())
print 'start waiting:', time.strftime('%H:%M:%S')
# load the index and initialize our searcher
index = cPickle.loads(open(args["index"]).read())
searcher = Searcher(index)
# loop over images in the index -- we will use each one as
# a query image
for (query, queryFeatures) in index.items():
# perform the search using the current query
results = searcher.search(queryFeatures)
# load the query image and display it
path = args["dataset"] + "/%s" % (query)
queryImage = cv2.imread(path)
# cv2.imshow("Query", queryImage)
print "query: %s" % (query)
# # initialize the two montages to display our results --
# # we have a total of 25 images in the index, but let's only
# import the necessary packages
from pyimagesearch.colordescriptor import ColorDescriptor
from pyimagesearch.searcher import Searcher
import cv2
query = raw_input("Query:")
# initialize the image descriptor
cd = ColorDescriptor((8, 12, 3))
# load the query image and describe it
query = cv2.imread(query)
features = cd.describe(query)
# perform the search
searcher = Searcher("index.csv")
results = searcher.search(features)
# display the query
cv2.imshow("Query", query)
# loop over the results
for (score, result) in results:
url,resultname,resulturl = result
print url,resultname,resulturl
# load the query image and show it
queryImage = cv2.imread(args["query"])
cv2.imshow("Query", queryImage)
print("queryImage " + str(queryImage))
print "query: %s" % (args["query"])
# describe the query in the same way that we did in
# index.py -- a 3D RGB histogram with 8 bins per
# channel
desc = RGBHistogram([8, 8, 8])
queryFeatures = desc.describe(queryImage)
# load the index perform the search
index = cPickle.loads(open(args["index"]).read())
searcher = Searcher(index)
results = searcher.search(queryFeatures)
# initialize the two montages to display our results --
# we have a total of 25 images in the index, but let's only
# display the top 10 results; 5 images per montage, with
# images that are 400x166 pixels
montageA = np.zeros(
(len(queryImage) * 5, len(queryImage[0]), 3),
dtype="uint8") #np.zeros((166 * 5, 400, 3), dtype = "uint8")
montageB = np.zeros(
(len(queryImage) * 5, len(queryImage[0]), 3),
dtype="uint8") #np.zeros((166 * 5, 400, 3), dtype = "uint8")
# loop over the top ten results
for j in xrange(0, 10):
def callback(file):
args={'result_path': 'dataset', 'index': 'index.csv', 'query': ''}
args['query']=file
# # 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)
#
return results
# display the query
#cv2.imshow("Query", query)
#loop over the results
# count=0
# print "Note: lower % of matching shows maximum similarity."
# for (score, resultID) in results:
# # load the result image and display it
# i=0
# total=100
# perc=int(score)
# while(i<=perc):
# total-=5+i;
# i+=1
# print "Image "+resultID +" got "+str(total+5)+"% similarity and "+'%2.3f'%score+" score.."
# result = cv2.imread(args["result_path"] + "/" + resultID)
# cv2.imwrite("/home/venkat/Review/output/"+str(score)+" "+resultID, result)
# count+=1
# print "Got "+str(count)+" similer results...."
# root = Tk()
# root.geometry('{}x{}'.format(1330, 760))
# root.title("Content Based Image Retrieval")
# label=Label(root,text = 'Resultant similer images..')
# label.bind()
# c=0
# r=0
# path='/home/venkat/CBIR/dataset/'
# output='/home/venkat/CBIR/'
# print "Note: lower % of matching shows maximum similarity."
# for (score, resultID) in results:
# # load the result image and display it
# i=0
# total=100
# perc=int(score)
# while(i<=perc):
# total-=5;
# i+=1
# print "Image "+resultID +" got "+str(total)+"% similarity and "+'%2.3f'%score+" score.."
# path1 = Image.open("/home/venkat/Review/dataset/"+resultID)
# path1=path1.resize((250, 250),Image.ANTIALIAS)
# img1 = ImageTk.PhotoImage(path1)
# myvar=Label(root,image = img1)
# myvar.image = img1
# myvar.grid(row=r,column=c)
# c+=250
# if(c>1000):
# r+=250
# c=0
# root.mainloop()
# USAGE
# python search.py --index index.csv --query queries/103100.png --result-path dataset
