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SadhulaSaiKumar
2023-08-29 06:05:56 +00:00
parent 1866d5678e
commit 4cdbec6f68
2 changed files with 557 additions and 540 deletions
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Events/src/multi_pcs.py
+1 -1
View File
@@ -487,7 +487,7 @@ def eventwise():
with multiprocessing.Pool(processes=10) as pool:
results = pool.map(download,url_list)
pool.close()
return "none"
return "Done"
if __name__ == "__main__":
app.run(host="0.0.0.0",port=8081)
Events/src/myproject.py
+65 -48
View File
@@ -8,8 +8,8 @@ import cv2
app = Flask(__name__)
app.config["IMAGE_UPLOADS"] = "C:/Users/Bizgaze/PycharmProjects/face_recogniction/People"
datasetPath = "./Gallery/"
peoplePath = "./guestimage/"
datasetPath = "/opt/bizgaze/events.bizgaze.app/wwwroot/_files/1/Gallery/"
peoplePath = "/opt/bizgaze/events.bizgaze.app/wwwroot/_files/People/"
@app.route('/', methods=['GET'])
def home():
return render_template('index.html')
@@ -44,7 +44,9 @@ def upload():
@app.route('/predict', methods=["GET", "POST"])
def predict():
print('starting')
def saveEncodings(encs, names, fname="encodings.pickle"):
print('encoding')
"""
Save encodings in a pickle file to be used in future.
@@ -153,7 +155,7 @@ def predict():
"""
duplicateName = ""
distance = 0.0
matches = face_recognition.compare_faces(known_encodings, unknown_encoding, tolerance=0.5)
matches = face_recognition.compare_faces(known_encodings, unknown_encoding, tolerance=0.47)
face_distances = face_recognition.face_distance(known_encodings, unknown_encoding)
best_match_index = np.argmin(face_distances)
distance = face_distances[best_match_index]
@@ -324,9 +326,9 @@ def predict():
processKnownPeopleImages()
processDatasetImages()
shutil.make_archive('./Images', 'zip','./output')
p='./Images.zip'
return send_file(p,as_attachment=True)
# shutil.make_archive('./Images', 'zip','./output')
# p='./Images.zip'
# return send_file(p,as_attachment=True)
# import pandas as pd
@@ -343,52 +345,65 @@ def predict():
##############################csv creation code ##############################
# import pandas as pd
# q = pd.DataFrame(p)
# m = q
# print(m)
# # x.drop(x.columns[Unnam], axis=1, inplace=True)
# df = m.groupby([0], as_index=False).count()
# z = df[0].str.split('/', expand=True)
import pandas as pd
q = pd.DataFrame(p)
m = q
#print(m)
# x.drop(x.columns[Unnam], axis=1, inplace=True)
df = m.groupby([0], as_index=False).count()
first_column_name = df.columns[0]
# z['ImagePath'] = z[3]
# Rename the first column
df.rename(columns={first_column_name: 'col'}, inplace=True)
#print(df)
z = df['col'].str.split('/', expand=True)
# result = z.drop([0,1,3], axis=1)
# result.rename({result.columns[-1]: 'test'}, axis=1, inplace=True)
# # print(result)
# result.to_csv('results1.csv')
# import pandas as pd
# import os
# c = []
# for root, dirs, files in os.walk("./Dataset", topdown=False):
# for name in files:
# # print(name)
# L = os.path.join(root, name)
# c.append(L)
# df = pd.DataFrame(c)
z['ImagePath'] = z[3]
# df1 = df[0].str.split("/", expand=True)
# df1.rename({df1.columns[-2]: 'abc'}, axis=1, inplace=True)
# print('this is df1')
# print(df1)
# df1.rename({df1.columns[-1]: 'test'}, axis=1, inplace=True)
# merge = pd.merge(df1, result, on='test', how='left')
# merge.to_csv('merge.csv')
# mergesplit = merge.loc[:,'test'].str.split(".", expand=True)
# mergesplit.rename({mergesplit.columns[-2]: 'ImageName'}, axis=1, inplace=True)
# mergesplit = mergesplit.loc[:,'ImageName' ]
result = z.drop([0,1,3], axis=1)
result.rename({result.columns[-1]: 'test'}, axis=1, inplace=True)
# print(result)
result.to_csv('results1.csv')
import pandas as pd
import os
c = []
for root, dirs, files in os.walk(datasetPath, topdown=False):
for name in files:
# print(name)
L = os.path.join(root, name)
c.append(L)
df = pd.DataFrame(c)
#print('seconfdf')
# merge.rename({merge.columns[-1]: 'Matched'}, axis=1, inplace=True)
# merge['EventName'] = merge['abc']
# merge['Imagepath']="/_files/1/Gallery/"+merge['EventName']+'/'+ + merge['test']
first_column_name = df.columns[0]
# frames = [merge, mergesplit]
# Rename the first column
df.rename(columns={first_column_name: 'col'}, inplace=True)
print(df)
df1 = df['col'].str.split("/", expand=True)
df1.rename({df1.columns[-2]: 'abc'}, axis=1, inplace=True)
#print('this is df1')
#print(df1)
df1.rename({df1.columns[-1]: 'test'}, axis=1, inplace=True)
merge = pd.merge(df1, result, on='test', how='left')
merge.to_csv('merge.csv')
mergesplit = merge.loc[:,'test'].str.split(".", expand=True)
mergesplit.rename({mergesplit.columns[-2]: 'ImageName'}, axis=1, inplace=True)
mergesplit = mergesplit.loc[:,'ImageName' ]
# r = pd.concat(frames, axis=1, join='inner')
# r=r.iloc[:,3:]
# print(r)
# r.to_csv('path.csv', index=False)
# r.to_json(r'./matched.json', orient="records")
merge.rename({merge.columns[-1]: 'Matched'}, axis=1, inplace=True)
merge['EventName'] = merge['abc']
merge['Imagepath']="/_files/1/Gallery/"+merge['EventName']+'/'+ + merge['test']
frames = [merge, mergesplit]
r = pd.concat(frames, axis=1, join='inner')
r=r.iloc[:,3:]
#print(r)
r.to_csv('path.csv', index=False)
#r.to_json(r'./matched.json', orient="records")
column_list = ['Matched','Imagepath', 'ImageName', 'EventName']
r[column_list].to_json('matched.json', orient="records")
#############################################################################################
@@ -477,8 +492,10 @@ def predict():
main()
# return render_template('index.html')
p = './matched.json'
return send_file(p,as_attachment=True)
return 'ALL IMAGES MATCHED'
# return 'ALL IMAGES MATCHED'
@app.route('/json')
@@ -488,6 +505,6 @@ def json():
if __name__ == "__main__":
app.run(host="0.0.0.0",port=8081,debug=True)
app.run(host="0.0.0.0",port=8081)