def test_mixed(resource, capsys):
main(resource('mixed.txt'))
out, err = capsys.readouterr()
score = float(re.search('score of (.+?) with', out).group(1))
assert score <= 0.3
assert score >= -0.3
def test_neutral(resource, capsys):
main(resource('neutral.txt'))
out, err = capsys.readouterr()
magnitude = float(re.search('magnitude of (.+?)', out).group(1))
assert magnitude <= 2.0
def my_form_post():
global path_for_rating
global objects_2D_arr
global tags_2D_arr
global categories_2D_arr
global adult_2D_arr
global senti_2D_arr
global average_rating
try:
user_ip=request.environ.get('HTTP_X_REAL_IP', request.remote_addr)
print("IP of incoming connection is",user_ip)
print(request.args)
if(len(request.args)!=1):
website=(request.args['website'])
text=(request.args['fname'])
captcha_response=request.args['g-recaptcha-response']
print(text+" this is text"+website)
web_header="http://www.twitter.com"
if(website=="Facebook"):
web_header=""
elif(website=="Reddit"):
web_header=""
elif(website=="Instagram"):
web_header=""
#Profile_URL=Profile_URL_Generator(web_header,text)
Profile_URL=text
#print("URL genreated=",Profile_URL)
if((captcha_response!="" and captcha_response!=None)):# and URL_CHECKER(Profile_URL)):
#webbrowser.open(Profile_URL)
average_rating=0
objects_2D_arr = []
tags_2D_arr = []
categories_2D_arr = []
adult_2D_arr = []
senti_2D_arr = []
if(website=="Facebook"):
username = extract_username(Profile_URL)
with open('input.txt',"w") as f:
f.write(Profile_URL)
f.close()
temp_path="C:\\Users\\Vaibhav\\PycharmProjects\\SMA_1\\Data\\"+username
if(not os.path.isdir(temp_path)):
Facebook_scraper.main()
temp_path="C:\\Users\\Vaibhav\\PycharmProjects\\SMA_1\\Data\\"+username+"\\Uploaded Photos"
if(os.path.isdir(temp_path)):
results = Msft_Vision_onlocalImage.main('C:\\Users\\Vaibhav\\PycharmProjects\\SMA_1\\Data\\' + username + '\\Uploaded Photos')
else:
results = Msft_Vision_onlocalImage.main('C:\\Users\\Vaibhav\\PycharmProjects\\SMA_1\\Data\\' + username + '\\Tagged Photos')
#results=Msft_Vision_onlocalImage.main('C:\\Users\\Vaibhav\\PycharmProjects\\SMA_1\\Data\\'+username+'\\Uploaded Photos')
#two_D_arr=json.dumps([['Task', 'Hours per Day'],['Work', 8],['Eat', 2],['TV', 4],['Gym', 2],['Sleep', 8]])
objects=results[0]
tags=results[1]
categories=results[2]
adults=results[3]
objects.insert(0,['Task', 'Hours per Day'])
tags.insert(0,['Task', 'Hours per Day'])
categories.insert(0, ['Task', 'Hours per Day'])
adults.insert(0, ['Task', 'Hours per Day'])
objects_2D_arr=json.dumps(objects)
tags_2D_arr = json.dumps(tags)
categories_2D_arr = json.dumps(categories)
adult_2D_arr = json.dumps(adults)
string_list=get_all_strings("C:\\Users\\Vaibhav\\PycharmProjects\\SMA_1\\Data\\"+username,"Facebook")
path_for_rating="C:\\Users\\Vaibhav\\PycharmProjects\\SMA_1\\Data\\"+username
if(os.path.isfile(path_for_rating+"\\Rating.csv")):
file = pd.read_csv(path_for_rating+"\\Rating.csv")
temp_rating=0
size=0
for index, row in file.iterrows():
temp_rating+=row['Rating']
size+=1
average_rating=temp_rating/size
documents = create_array_of_dictionaries(string_list, "Reddit")
results2 = sentiment_analysis.main(documents)
senti_arr = []
print(results2)
print(len(results2["documents"]))
print((string_list))
length=len(results2["documents"])
cd=datetime.date.today()
for i in range(0, len(results2["documents"])):
try:
# print(string_list[i][0])
#templi = string_list[i][0].split(" ")
templi=cd-datetime.timedelta(i)
print(templi)
# senti_arr.append([i+1, results2["documents"][i]["score"]])
senti_arr.append([templi.strftime("%d-%b-%Y"), results2["documents"][i]["score"]])
except Exception as e:
print(e)
# print(senti_arr)
senti_arr.insert(0, ['Sentiment Score', 'Sentiment Score'])
print(senti_arr)
senti_2D_arr = json.dumps(senti_arr)
#return render_template('pie_chart.html',input=objects_2D_arr)
return render_template('simplpc-4.html',object=objects_2D_arr,tag=tags_2D_arr,category=categories_2D_arr,adult=adult_2D_arr,senti=senti_2D_arr,Rating=average_rating)
if(website=="Reddit"):
username = re.search(r'https://www.reddit.com/user/([^/?]+)', Profile_URL).group(1)
if(Profile_URL[len(Profile_URL)-1]=='/'):
Profile_URL=Profile_URL[:-1]
#reddit_scrape.main(Profile_URL)
if (not os.path.isdir('C:\\Users\\Vaibhav\\PycharmProjects\\SMA_1\\reddit\\' + username)):
reddit_scrape.main(Profile_URL,username)
results=Msft_Vision_onlocalImage.main('C:\\Users\\Vaibhav\\PycharmProjects\\SMA_1\\reddit\\'+username)
path_for_rating='C:\\Users\\Vaibhav\\PycharmProjects\\SMA_1\\reddit\\'+username
if (os.path.isfile(path_for_rating + "\\Rating.csv")):
file = pd.read_csv(path_for_rating + "\\Rating.csv")
temp_rating = 0
size = 0
for index, row in file.iterrows():
temp_rating += row['Rating']
size += 1
average_rating = temp_rating / size
objects = results[0]
tags = results[1]
categories = results[2]
adults = results[3]
objects.insert(0, ['Task', 'Hours per Day'])
tags.insert(0, ['Task', 'Hours per Day'])
categories.insert(0, ['Task', 'Hours per Day'])
adults.insert(0, ['Task', 'Hours per Day'])
objects_2D_arr = json.dumps(objects)
tags_2D_arr = json.dumps(tags)
categories_2D_arr = json.dumps(categories)
adult_2D_arr = json.dumps(adults)
string_list=get_all_strings("C:\\Users\\Vaibhav\\PycharmProjects\\SMA_1\\reddit\\"+username+"\\"+"Reddit_response.csv","Reddit")
print(string_list)
documents=create_array_of_dictionaries(string_list,"Reddit")
results2=sentiment_analysis.main(documents)
senti_arr = []
print(results2)
print(len(results2["documents"]))
print((string_list))
for i in range(0, len(results2["documents"])):
try:
# print(string_list[i][0])
templi = string_list[i][0].split(" ")
# senti_arr.append([i+1, results2["documents"][i]["score"]])
senti_arr.append([templi[0], results2["documents"][i]["score"]])
except Exception as e:
print(e)
# print(senti_arr)
senti_arr.insert(0, ['Sentiment Score', 'Sentiment Score'])
print(senti_arr)
senti_2D_arr = json.dumps(senti_arr)
# return render_template('pie_chart.html',input=objects_2D_arr)
return render_template('simplpc-4.html', object=objects_2D_arr, tag=tags_2D_arr, category=categories_2D_arr,
adult=adult_2D_arr,senti=senti_2D_arr,Rating=average_rating)
if(website=="Twitter"):
username=re.search(r'https://twitter.com/([^/?]+)', Profile_URL).group(1)
if(not os.path.isdir('C:\\Users\\Vaibhav\\PycharmProjects\\SMA_1\\twitter\\'+username)):
Twitter3.get_all_tweets(username)
string_list=get_all_strings("C:\\Users\\Vaibhav\\PycharmProjects\\SMA_1\\twitter\\"+username+"\\"+username+"_tweets.csv","Twitter")
print(string_list)
documents=create_array_of_dictionaries(string_list,"Twitter")
results2=sentiment_analysis.main(documents)
results=Msft_Vision_onlocalImage.main('C:\\Users\\Vaibhav\\PycharmProjects\\SMA_1\\twitter\\'+username)
path_for_rating='C:\\Users\\Vaibhav\\PycharmProjects\\SMA_1\\twitter\\'+username
if (os.path.isfile(path_for_rating + "\\Rating.csv")):
file = pd.read_csv(path_for_rating + "\\Rating.csv")
temp_rating = 0
size = 0
for index, row in file.iterrows():
temp_rating += row['Rating']
size += 1
average_rating = temp_rating / size
objects = results[0]
tags = results[1]
categories = results[2]
adults = results[3]
objects.insert(0, ['Task', 'Hours per Day'])
tags.insert(0, ['Task', 'Hours per Day'])
categories.insert(0, ['Task', 'Hours per Day'])
adults.insert(0, ['Task', 'Hours per Day'])
senti_arr=[]
print(results2)
print(len(results2["documents"]))
print((string_list))
for i in range(0,len(results2["documents"])):
try:
#print(string_list[i][0])
templi=string_list[i][0].split(" ")
#senti_arr.append([i+1, results2["documents"][i]["score"]])
senti_arr.append([templi[0],results2["documents"][i]["score"]])
except Exception as e:
print(e)
#print(senti_arr)
senti_arr.insert(0, ['Sentiment Score', 'Sentiment Score'])
print(senti_arr)
senti_2D_arr=json.dumps(senti_arr)
objects_2D_arr = json.dumps(objects)
tags_2D_arr = json.dumps(tags)
categories_2D_arr = json.dumps(categories)
adult_2D_arr = json.dumps(adults)
print(results2)
return render_template('simplpc-4.html', object=objects_2D_arr, tag=tags_2D_arr, category=categories_2D_arr,
adult=adult_2D_arr,senti=senti_2D_arr,Rating=average_rating)
else:
return render_template('Invalid_Profile.html')
else:
print(path_for_rating)
path_for_rating2=path_for_rating+"\\Rating.csv"
user_rating=1
for key in request.args:
user_rating=key
exist=False
if(os.path.isfile(path_for_rating2)):
print("Path found")
file = pd.read_csv(path_for_rating2)
for index,row in file.iterrows():
ip=row['IP']
print(ip)
if(user_ip==ip):
exist=True
break
print("existence is",exist)
if(not exist):
if (not os.path.isfile(path_for_rating2)):
with open(path_for_rating2, mode='a', encoding='utf-8', newline='') as f:
count = 0
fieldnames = ['IP', 'Rating']
writer = csv.DictWriter(f, fieldnames=fieldnames)
writer.writeheader()
writer.writerow({'IP':user_ip,"Rating":user_rating})
else:
with open(path_for_rating2, mode='a', encoding='utf-8', newline='') as f:
count = 0
fieldnames = ['IP', 'Rating']
writer = csv.DictWriter(f, fieldnames=fieldnames)
writer.writerow({'IP': user_ip, "Rating": user_rating})
if (os.path.isfile(path_for_rating + "\\Rating.csv")):
file = pd.read_csv(path_for_rating + "\\Rating.csv")
temp_rating = 0
size = 0
for index, row in file.iterrows():
temp_rating += row['Rating']
size += 1
average_rating = temp_rating / size
return render_template('simplpc-4.html', object=objects_2D_arr, tag=tags_2D_arr, category=categories_2D_arr,
adult=adult_2D_arr, senti=senti_2D_arr,Rating=average_rating)
except Exception as e:
print(e)
return render_template('Invalid_Profile.html')
def test_neg(resource, capsys):
main(resource('neg.txt'))
out, err = capsys.readouterr()
score = float(re.search('score of (.+?) with', out).group(1))
magnitude = float(re.search('magnitude of (.+?)', out).group(1))
assert score * magnitude < 0
def test_mixed(resource, capsys):
main(resource('mixed.txt'))
out, err = capsys.readouterr()
score = float(re.search('score of (.+?) with', out).group(1))
assert score <= 0.3
assert score >= -0.3
def test_neutral(resource, capsys):
main(resource('neutral.txt'))
out, err = capsys.readouterr()
magnitude = float(re.search('magnitude of (.+?)', out).group(1))
assert magnitude <= 2.0
def test_neg(resource, capsys):
main(resource('neg.txt'))
out, err = capsys.readouterr()
score = float(re.search('score of (.+?) with', out).group(1))
magnitude = float(re.search('magnitude of (.+?)', out).group(1))
assert score * magnitude < 0
def test_mixed(resource, capsys):
main(resource('mixed.txt'))
out, err = capsys.readouterr()
polarity = float(re.search('polarity of (.+?) with', out).group(1))
assert polarity <= 0.3
assert polarity >= -0.3
def test_pos(resource, capsys):
main(resource('pos.txt'))
out, err = capsys.readouterr()
polarity = float(re.search('polarity of (.+?) with', out).group(1))
magnitude = float(re.search('magnitude of (.+?)', out).group(1))
assert polarity * magnitude > 0
Prend en paramètre un chemin vers une vidéo, analyse les sentiments de l'audio,
et détermine si la personne sur l'image est en train de parler ou non
'''
if __name__ == '__main__':
#vérification des arguments
if len(sys.argv) < 2 or not os.path.isfile(sys.argv[1]):
raise Exception('usage: python video_analysis.py chemin_fichier_video')
#conversion du fichier audio
audio_file = speech_recognition.convert_to_wav(sys.argv[1])
#transcription de l'audio
timestamp, text = speech_recognition.speech_to_text(audio_file)
#analyse des sentiments
sentiments = sentiment_analysis.main(text)
#ouverture de la vidéo
cap = cv2.VideoCapture(sys.argv[1])
#récupération du nombre d'images par seconde
fps = cap.get(cv2.CAP_PROP_FPS)
#récupération de la hauteur de la vidéo
height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
#listes pour récupérer les images et la reconnaissance des visages
frames = []
faces = []
#lecture de la vidéo
def sentiment_analysis():
GSA.main("searchTweetOutput.txt")
