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@@ -10,36 +10,14 @@ app = Flask(__name__)
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app.config["IMAGE_UPLOADS"] = "C:/Users/Bizgaze/PycharmProjects/face_recogniction/People"
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#datasetPath = "/opt/bizgaze/events.bizgaze.app/wwwroot/_files/1/Gallery/"
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#peoplePath = "/opt/bizgaze/events.bizgaze.app/wwwroot/_files/People/"
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-@app.route('/', methods=['GET'])
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-def home():
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- return render_template('index.html')
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-
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-@app.route('/Display', methods=['GET', "POST"])
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-def Display():
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- return render_template('Display.html')
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-@app.route("/upload", methods=["GET", "POST"])
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-def upload():
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- if request.method == "POST":
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-
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- if request.files:
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-
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- image = request.files["image"]
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- try:
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- image.save(os.path.join(
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- app.config["IMAGE_UPLOADS"], image.filename))
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- except IsADirectoryError:
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- return render_template('index.html')
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- # image.save(os.path.join(
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- # app.config["IMAGE_UPLOADS"], image.filename))
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-
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- print("Image saved")
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- return redirect(request.url)
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+@app.route('/', methods=['GET'])
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+def home():
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+ return render_template('index.html')
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- return 'ok'
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@app.route('/predict', methods=["GET", "POST"])
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@@ -47,9 +25,11 @@ def predict():
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Dataset = request.get_json()
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a = Dataset
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peoplePath = a['People']
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- print(peoplePath)
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+
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+ #print(peoplePath1)
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datasetPath = a['Gallery']
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- print(datasetPath)
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+
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+ #print(datasetPath)
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print('starting')
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def saveEncodings(encs, names, fname="encodings.pickle"):
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@@ -200,7 +180,9 @@ def predict():
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if os.path.exists(path):
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pass
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else:
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- os.mkdir(path)
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+ if not os.path.exists(path):
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+ os.makedirs(path)
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+ # os.mkdir(path,exist_ok=True)
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cv2.imwrite(path + "/" + imageName, image)
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x = []
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c = (path1 + "/" + imageName)
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@@ -331,8 +313,8 @@ def predict():
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"""
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- processKnownPeopleImages()
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- processDatasetImages()
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+ processKnownPeopleImages(peoplePath)
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+ processDatasetImages(datasetPath)
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# shutil.make_archive('./Images', 'zip','./output')
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# p='./Images.zip'
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# return send_file(p,as_attachment=True)
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@@ -364,7 +346,6 @@ def predict():
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df.rename(columns={first_column_name: 'col'}, inplace=True)
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#print(df)
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z = df['col'].str.split('/', expand=True)
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-
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z['ImagePath'] = z[3]
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result = z.drop([0,1,3], axis=1)
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@@ -387,7 +368,7 @@ def predict():
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# Rename the first column
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df.rename(columns={first_column_name: 'col'}, inplace=True)
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print(df)
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- df1 = df['col'].str.split("/", expand=True)
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+ df1 = df['col'].str.split("\\", expand=True)
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df1.rename({df1.columns[-2]: 'abc'}, axis=1, inplace=True)
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#print('this is df1')
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#print(df1)
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@@ -400,7 +381,7 @@ def predict():
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merge.rename({merge.columns[-1]: 'Matched'}, axis=1, inplace=True)
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merge['EventName'] = merge['abc']
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- merge['Imagepath']="/_files/1/Gallery/"+merge['EventName']+'/'+ + merge['test']
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+ merge['Imagepath']= datasetPath+merge['EventName']+'/'+ + merge['test']
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frames = [merge, mergesplit]
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@@ -414,85 +395,6 @@ def predict():
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#############################################################################################
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- # merge.rename({merge.columns[-3]: 'ImagePath'}, axis=1, inplace=True)
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- #
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- # # print(merge)
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- # merge1 = merge.iloc[:, -2]
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- # merge12= merge.iloc[:, -3]
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- #
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- # # merge1.rename({merge1.columns[-1]: 'abc'}, axis=1, inplace=True)
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- # merge2 = merge.iloc[:, -1].str.split(".", expand=True)
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- # merge2.rename({merge2.columns[-1]: 'drop'}, axis=1, inplace=True)
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- # #merge2.rename({merge2.columns[-2]: 'ImageName'}, axis=1, inplace=True)
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- # print('this is merge1')
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- # print(merge1)
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- # print('this is merge2')
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- # print(merge2)
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- # mergefinal = pd.concat([merge1, merge2], axis=1, join='inner')
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- # # print(mergefinal)
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- # # print('-----------------')
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- #
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- # mergefinal.drop(columns=mergefinal.columns[-1], axis=1, inplace=True)
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- # # print(mergefinal)
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- # # print('--------------------------------------------------------------------------------')
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- # # mergefinal.rename({mergefinal.columns[-1]: 'ImageName'}, axis=1, inplace=True)
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- # # print('this is filename')
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- # # print(mergefinal)
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- # #mergefinal.rename({mergefinal.columns[-2]: 'EventName'}, axis=1, inplace=True)
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- # # print('this is foldername')
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- # # print(mergefinal)
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- #
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- # frames = [mergefinal, merge12]
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- #
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- # r = pd.concat(frames, axis=1, join='inner')
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- #
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- #
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- # r.to_csv('Imagepath1.csv', index=False)
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- # r.to_json('Imagepath1.json', orient="records")
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- # import shutil
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- # import os
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- # # import shutil module
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- # import shutil
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- #
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- # # import os module
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- # import os
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- # #################### move code#############
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- # # base path
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- # base_path = 'C:\\Users\\Bizgaze\\PycharmProjects\\face_recogniction\\move'
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- # import os
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- # dir_list = []
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- # rootdir = 'C:\\Users\\Bizgaze\\PycharmProjects\\face_recogniction\\Dataset'
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- # for file in os.listdir(rootdir):
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- # d = os.path.join(rootdir, file)
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- # if os.path.isdir(d):
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- # dir_list.append(d)
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- #
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- # # list of directories we want to move.
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- # # dir_list = ['test2', 'test4', 'test5', 'does_not_exist']
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- #
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- # # path to destination directory
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- # # dest = os.path.join(base_path, 'dest')
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- #
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- # print("Before moving directories:")
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- # print(os.listdir(base_path))
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- #
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- # # traverse each directory in dir_list
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- # for dir_ in dir_list:
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- #
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- # # create path to the directory in the
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- # # dir_list.
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- # source = os.path.join(base_path, dir_)
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- #
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- # # check if it is an existing directory
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- # if os.path.isdir(source):
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- # # move to destination path
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- # shutil.move(source, base_path)
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- #
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- # print("After moving directories:")
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- # print(os.listdir(base_path))
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-
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print("Completed")
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if __name__ == "__main__":
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