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main.py
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from flask import Flask
from flask import Flask, render_template, Response, redirect, request, session, abort, url_for
from camera import VideoCamera
from camera2 import VideoCamera2
from datetime import datetime
from datetime import date
import datetime
import random
from random import seed
from random import randint
import cv2
import numpy as np
import threading
import os
import time
import shutil
import imagehash
import PIL.Image
from PIL import Image
from PIL import ImageTk
import urllib.request
import urllib.parse
from urllib.request import urlopen
import webbrowser
#from midas.model_loader import default_models, load_model
import argparse
import pyttsx3
import mysql.connector
mydb = mysql.connector.connect(
host="localhost",
user="root",
passwd="",
charset="utf8",
database="artificial_vision"
)
app = Flask(__name__)
##session key
app.secret_key = 'abcdef'
@app.route('/',methods=['POST','GET'])
def index():
cnt=0
act=""
msg=""
ff=open("det.txt","w")
ff.write("1")
ff.close()
ff1=open("photo.txt","w")
ff1.write("1")
ff1.close()
ff11=open("img.txt","w")
ff11.write("1")
ff11.close()
ff=open("person.txt","w")
ff.write("")
ff.close()
if request.method == 'POST':
username1 = request.form['uname']
password1 = request.form['pass']
mycursor = mydb.cursor()
mycursor.execute("SELECT count(*) FROM admin where username=%s && password=%s",(username1,password1))
myresult = mycursor.fetchone()[0]
if myresult>0:
result=" Your Logged in sucessfully**"
return redirect(url_for('admin1'))
else:
result="Your logged in fail!!!"
return render_template('index.html',msg=msg,act=act)
@app.route('/monitor',methods=['POST','GET'])
def monitor():
cnt=0
act=""
msg=""
return render_template('monitor.html',msg=msg,act=act)
@app.route('/login', methods=['POST','GET'])
def login():
result=""
ff1=open("photo.txt","w")
ff1.write("1")
ff1.close()
if request.method == 'POST':
username1 = request.form['uname']
password1 = request.form['pass']
mycursor = mydb.cursor()
mycursor.execute("SELECT count(*) FROM admin where username=%s && password=%s",(username1,password1))
myresult = mycursor.fetchone()[0]
if myresult>0:
result=" Your Logged in sucessfully**"
return redirect(url_for('admin1'))
else:
result="Your logged in fail!!!"
return render_template('login.html',result=result)
@app.route('/admin1',methods=['POST','GET'])
def admin1():
act=request.args.get("act")
mycursor = mydb.cursor()
if request.method == 'POST':
detail = request.form['detail']
mycursor.execute("SELECT max(id)+1 FROM train_data")
maxid = mycursor.fetchone()[0]
if maxid is None:
maxid=1
sql = "INSERT INTO train_data(id, utype, detail,fimg) VALUES (%s, %s, %s, %s)"
val = (maxid, '', detail, '')
print(sql)
mycursor.execute(sql, val)
mydb.commit()
return redirect(url_for('add_photo',vid=maxid))
mycursor.execute("SELECT * FROM train_data")
value = mycursor.fetchall()
###
if act=="del":
did=request.args.get("did")
mycursor.execute("SELECT count(*) FROM vt_face where vid=%s",(did,))
cn = mycursor.fetchone()[0]
if cn>0:
mycursor.execute("SELECT * FROM vt_face where vid=%s",(did,))
dd = mycursor.fetchall()
for ds in dd:
os.remove("static/frame/"+ds[2])
mycursor.execute("delete from vt_face where vid=%s",(did,))
mydb.commit()
mycursor.execute("delete from train_data where id=%s",(did,))
mydb.commit()
return redirect(url_for('admin1'))
###
return render_template('admin1.html',value=value)
@app.route('/admin2',methods=['POST','GET'])
def admin2():
act=request.args.get("act")
mycursor = mydb.cursor()
mycursor.execute("SELECT * FROM train_data")
value = mycursor.fetchall()
###
if act=="del":
did=request.args.get("did")
mycursor.execute("SELECT count(*) FROM vt_face where vid=%s",(did,))
cn = mycursor.fetchone()[0]
if cn>0:
mycursor.execute("SELECT * FROM vt_face where vid=%s",(did,))
dd = mycursor.fetchall()
for ds in dd:
os.remove("static/frame/"+ds[2])
os.remove("images_db/"+ds[2])
mycursor.execute("delete from vt_face where vid=%s",(did,))
mydb.commit()
mycursor.execute("delete from train_data where id=%s",(did,))
mydb.commit()
return redirect(url_for('admin1'))
###
return render_template('admin2.html',value=value)
@app.route('/page1',methods=['POST','GET'])
def page1():
return render_template('page1.html')
def getImagesAndLabels(path):
detector = cv2.CascadeClassifier("haarcascade_frontalface_default.xml");
imagePaths = [os.path.join(path,f) for f in os.listdir(path)]
faceSamples=[]
ids = []
for imagePath in imagePaths:
PIL_img = Image.open(imagePath).convert('L') # convert it to grayscale
img_numpy = np.array(PIL_img,'uint8')
id = int(os.path.split(imagePath)[-1].split(".")[1])
faces = detector.detectMultiScale(img_numpy)
for (x,y,w,h) in faces:
faceSamples.append(img_numpy[y:y+h,x:x+w])
ids.append(id)
return faceSamples,ids
@app.route('/add_photo',methods=['POST','GET'])
def add_photo():
vid = request.args.get('vid')
ff1=open("photo.txt","w")
ff1.write("2")
ff1.close()
#ff2=open("mask.txt","w")
#ff2.write("face")
#ff2.close()
act = request.args.get('act')
cursor = mydb.cursor()
cursor.execute("SELECT * FROM train_data where id=%s",(vid,))
value = cursor.fetchone()
name=value[2]
ff=open("user.txt","w")
ff.write(name)
ff.close()
ff=open("user1.txt","w")
ff.write(vid)
ff.close()
if request.method=='POST':
vid=request.form['vid']
fimg="v"+vid+".jpg"
cursor.execute('delete from vt_face WHERE vid = %s', (vid, ))
mydb.commit()
ff=open("det.txt","r")
v=ff.read()
ff.close()
vv=int(v)
v1=vv-1
vface1="User."+vid+"."+str(v1)+".jpg"
i=2
while i<vv:
cursor.execute("SELECT max(id)+1 FROM vt_face")
maxid = cursor.fetchone()[0]
if maxid is None:
maxid=1
vface="User."+vid+"."+str(i)+".jpg"
sql = "INSERT INTO vt_face(id, vid, vface) VALUES (%s, %s, %s)"
val = (maxid, vid, vface)
print(val)
cursor.execute(sql,val)
mydb.commit()
i+=1
cursor.execute('update train_data set fimg=%s WHERE id = %s', (vface1, vid))
mydb.commit()
shutil.copy('static/faces/f1.jpg', 'static/photo/'+vface1)
##########
##Training face
# Path for face image database
path = 'dataset'
recognizer = cv2.face.LBPHFaceRecognizer_create()
# function to get the images and label data
print ("\n [INFO] Training faces. It will take a few seconds. Wait ...")
faces,ids = getImagesAndLabels(path)
recognizer.train(faces, np.array(ids))
# Save the model into trainer/trainer.yml
recognizer.write('trainer/trainer.yml') # recognizer.save() worked on Mac, but not on Pi
# Print the numer of faces trained and end program
print("\n [INFO] {0} faces trained. Exiting Program".format(len(np.unique(ids))))
#################################################
cursor = mydb.cursor()
cursor.execute("SELECT * FROM vt_face where vid=%s",(vid, ))
dt = cursor.fetchall()
for rs in dt:
##Preprocess
path="static/frame/"+rs[2]
path2="static/process1/"+rs[2]
mm2 = PIL.Image.open(path).convert('L')
rz = mm2.resize((200,200), PIL.Image.ANTIALIAS)
rz.save(path2)
'''img = cv2.imread(path2)
dst = cv2.fastNlMeansDenoisingColored(img, None, 10, 10, 7, 15)
path3="static/process2/"+rs[2]
cv2.imwrite(path3, dst)'''
#noice
img = cv2.imread('static/process1/'+rs[2])
dst = cv2.fastNlMeansDenoisingColored(img, None, 10, 10, 7, 15)
fname2='ns_'+rs[2]
cv2.imwrite("static/process1/"+fname2, dst)
######
##bin
image = cv2.imread('static/process1/'+rs[2])
original = image.copy()
kmeans = kmeans_color_quantization(image, clusters=4)
# Convert to grayscale, Gaussian blur, adaptive threshold
gray = cv2.cvtColor(kmeans, cv2.COLOR_BGR2GRAY)
blur = cv2.GaussianBlur(gray, (3,3), 0)
thresh = cv2.adaptiveThreshold(blur,255,cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY_INV,21,2)
# Draw largest enclosing circle onto a mask
mask = np.zeros(original.shape[:2], dtype=np.uint8)
cnts = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if len(cnts) == 2 else cnts[1]
cnts = sorted(cnts, key=cv2.contourArea, reverse=True)
for c in cnts:
((x, y), r) = cv2.minEnclosingCircle(c)
cv2.circle(image, (int(x), int(y)), int(r), (36, 255, 12), 2)
cv2.circle(mask, (int(x), int(y)), int(r), 255, -1)
break
# Bitwise-and for result
result = cv2.bitwise_and(original, original, mask=mask)
result[mask==0] = (0,0,0)
###cv2.imshow('thresh', thresh)
###cv2.imshow('result', result)
###cv2.imshow('mask', mask)
###cv2.imshow('kmeans', kmeans)
###cv2.imshow('image', image)
###cv2.waitKey()
cv2.imwrite("static/process1/bin_"+rs[2], thresh)
###RPN - Segment
img = cv2.imread('static/process1/'+rs[2])
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
ret, thresh = cv2.threshold(gray,0,255,cv2.THRESH_BINARY_INV+cv2.THRESH_OTSU)
kernel = np.ones((3,3),np.uint8)
opening = cv2.morphologyEx(thresh,cv2.MORPH_OPEN,kernel, iterations = 2)
# sure background area
sure_bg = cv2.dilate(opening,kernel,iterations=3)
# Finding sure foreground area
dist_transform = cv2.distanceTransform(opening,cv2.DIST_L2,5)
ret, sure_fg = cv2.threshold(dist_transform,0.7*dist_transform.max(),255,0)
# Finding unknown region
sure_fg = np.uint8(sure_fg)
segment = cv2.subtract(sure_bg,sure_fg)
img = Image.fromarray(img)
segment = Image.fromarray(segment)
path3="static/process2/fg_"+rs[2]
segment.save(path3)
####
img = cv2.imread('static/process2/fg_'+rs[2])
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
ret, thresh = cv2.threshold(gray,0,255,cv2.THRESH_BINARY_INV+cv2.THRESH_OTSU)
kernel = np.ones((3,3),np.uint8)
opening = cv2.morphologyEx(thresh,cv2.MORPH_OPEN,kernel, iterations = 2)
# sure background area
sure_bg = cv2.dilate(opening,kernel,iterations=3)
# Finding sure foreground area
dist_transform = cv2.distanceTransform(opening,cv2.DIST_L2,5)
ret, sure_fg = cv2.threshold(dist_transform,0.7*dist_transform.max(),255,0)
# Finding unknown region
sure_fg = np.uint8(sure_fg)
segment = cv2.subtract(sure_bg,sure_fg)
img = Image.fromarray(img)
segment = Image.fromarray(segment)
path3="static/process2/fg_"+rs[2]
segment.save(path3)
'''
img = cv2.imread(path2)
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
ret, thresh = cv2.threshold(gray,0,255,cv2.THRESH_BINARY_INV+cv2.THRESH_OTSU)
# noise removal
kernel = np.ones((3,3),np.uint8)
opening = cv2.morphologyEx(thresh,cv2.MORPH_OPEN,kernel, iterations = 2)
# sure background area
sure_bg = cv2.dilate(opening,kernel,iterations=3)
# Finding sure foreground area
dist_transform = cv2.distanceTransform(opening,cv2.DIST_L2,5)
ret, sure_fg = cv2.threshold(dist_transform,0.7*dist_transform.max(),255,0)
# Finding unknown region
sure_fg = np.uint8(sure_fg)
segment = cv2.subtract(sure_bg,sure_fg)
img = Image.fromarray(img)
segment = Image.fromarray(segment)
path3="static/process2/"+rs[2]
segment.save(path3)
'''
#####
image = cv2.imread(path2)
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
edged = cv2.Canny(gray, 50, 100)
image = Image.fromarray(image)
edged = Image.fromarray(edged)
path4="static/process3/"+rs[2]
edged.save(path4)
##
###
cursor.execute("SELECT count(*) FROM vt_face where vid=%s",(vid, ))
cnt = cursor.fetchone()[0]
if cnt>10:
return redirect(url_for('view_photo',vid=vid,act='success'))
else:
return redirect(url_for('message',vid=vid))
cursor.execute("SELECT * FROM train_data")
data = cursor.fetchall()
return render_template('add_photo.html',data=data, vid=vid)
def kmeans_color_quantization(image, clusters=8, rounds=1):
h, w = image.shape[:2]
samples = np.zeros([h*w,3], dtype=np.float32)
count = 0
for x in range(h):
for y in range(w):
samples[count] = image[x][y]
count += 1
compactness, labels, centers = cv2.kmeans(samples,
clusters,
None,
(cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 10000, 0.0001),
rounds,
cv2.KMEANS_RANDOM_CENTERS)
centers = np.uint8(centers)
res = centers[labels.flatten()]
return res.reshape((image.shape))
##MiDAS - Distance Estimation
def process(device, model, model_type, image, input_size, target_size, optimize, use_camera):
global first_execution
if "openvino" in model_type:
if first_execution or not use_camera:
print(f" Input resized to {input_size[0]}x{input_size[1]} before entering the encoder")
first_execution = False
sample = [np.reshape(image, (1, 3, *input_size))]
prediction = model(sample)[model.output(0)][0]
prediction = cv2.resize(prediction, dsize=target_size,
interpolation=cv2.INTER_CUBIC)
else:
sample = torch.from_numpy(image).to(device).unsqueeze(0)
if optimize and device == torch.device("cuda"):
if first_execution:
print(" Optimization to half-floats activated. Use with caution, because models like Swin require\n"
" float precision to work properly and may yield non-finite depth values to some extent for\n"
" half-floats.")
sample = sample.to(memory_format=torch.channels_last)
sample = sample.half()
if first_execution or not use_camera:
height, width = sample.shape[2:]
print(f" Input resized to {width}x{height} before entering the encoder")
first_execution = False
prediction = model.forward(sample)
prediction = (
torch.nn.functional.interpolate(
prediction.unsqueeze(1),
size=target_size[::-1],
mode="bicubic",
align_corners=False,
)
.squeeze()
.cpu()
.numpy()
)
return prediction
def create_side_by_side(image, depth, grayscale):
"""
Take an RGB image and depth map and place them side by side. This includes a proper normalization of the depth map
for better visibility.
Args:
image: the RGB image
depth: the depth map
grayscale: use a grayscale colormap?
Returns:
the image and depth map place side by side
"""
depth_min = depth.min()
depth_max = depth.max()
normalized_depth = 255 * (depth - depth_min) / (depth_max - depth_min)
normalized_depth *= 3
right_side = np.repeat(np.expand_dims(normalized_depth, 2), 3, axis=2) / 3
if not grayscale:
right_side = cv2.applyColorMap(np.uint8(right_side), cv2.COLORMAP_INFERNO)
if image is None:
return right_side
else:
return np.concatenate((image, right_side), axis=1)
def run(input_path, output_path, model_path, model_type="dpt_beit_large_512", optimize=False, side=False, height=None,
square=False, grayscale=False):
"""Run MonoDepthNN to compute depth maps.
Args:
input_path (str): path to input folder
output_path (str): path to output folder
model_path (str): path to saved model
model_type (str): the model type
optimize (bool): optimize the model to half-floats on CUDA?
side (bool): RGB and depth side by side in output images?
height (int): inference encoder image height
square (bool): resize to a square resolution?
grayscale (bool): use a grayscale colormap?
"""
print("Initialize")
# select device
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
print("Device: %s" % device)
model, transform, net_w, net_h = load_model(device, model_path, model_type, optimize, height, square)
# get input
if input_path is not None:
image_names = glob.glob(os.path.join(input_path, "*"))
num_images = len(image_names)
else:
print("No input path specified. Grabbing images from camera.")
# create output folder
if output_path is not None:
os.makedirs(output_path, exist_ok=True)
print("Start processing")
if input_path is not None:
if output_path is None:
print("Warning: No output path specified. Images will be processed but not shown or stored anywhere.")
for index, image_name in enumerate(image_names):
print(" Processing {} ({}/{})".format(image_name, index + 1, num_images))
# input
original_image_rgb = utils.read_image(image_name) # in [0, 1]
image = transform({"image": original_image_rgb})["image"]
# compute
with torch.no_grad():
prediction = process(device, model, model_type, image, (net_w, net_h), original_image_rgb.shape[1::-1],
optimize, False)
# output
if output_path is not None:
filename = os.path.join(
output_path, os.path.splitext(os.path.basename(image_name))[0] + '-' + model_type
)
if not side:
utils.write_depth(filename, prediction, grayscale, bits=2)
else:
original_image_bgr = np.flip(original_image_rgb, 2)
content = create_side_by_side(original_image_bgr*255, prediction, grayscale)
cv2.imwrite(filename + ".png", content)
utils.write_pfm(filename + ".pfm", prediction.astype(np.float32))
else:
with torch.no_grad():
fps = 1
video = VideoStream(0).start()
time_start = time.time()
frame_index = 0
while True:
frame = video.read()
if frame is not None:
original_image_rgb = np.flip(frame, 2) # in [0, 255] (flip required to get RGB)
image = transform({"image": original_image_rgb/255})["image"]
prediction = process(device, model, model_type, image, (net_w, net_h),
original_image_rgb.shape[1::-1], optimize, True)
original_image_bgr = np.flip(original_image_rgb, 2) if side else None
content = create_side_by_side(original_image_bgr, prediction, grayscale)
cv2.imshow('MiDaS Depth Estimation - Press Escape to close window ', content/255)
if output_path is not None:
filename = os.path.join(output_path, 'Camera' + '-' + model_type + '_' + str(frame_index))
cv2.imwrite(filename + ".png", content)
alpha = 0.1
if time.time()-time_start > 0:
fps = (1 - alpha) * fps + alpha * 1 / (time.time()-time_start) # exponential moving average
time_start = time.time()
print(f"\rFPS: {round(fps,2)}", end="")
if cv2.waitKey(1) == 27: # Escape key
break
frame_index += 1
print()
print("Finished")
#############
@app.route('/view_photo',methods=['POST','GET'])
def view_photo():
ff1=open("photo.txt","w")
ff1.write("1")
ff1.close()
vid=""
value=[]
if request.method=='GET':
vid = request.args.get('vid')
mycursor = mydb.cursor()
mycursor.execute("SELECT * FROM vt_face where vid=%s",(vid, ))
value = mycursor.fetchall()
if request.method=='POST':
print("Training")
vid=request.form['vid']
#shutil.copy('static/img/11.png', 'static/process4/'+rs[2])
return redirect(url_for('view_photo1',vid=vid))
return render_template('view_photo.html', result=value,vid=vid)
##CNN Classification
def CNN(self):
train_data_preprocess = ImageDataGenerator(
rescale = 1./255,
shear_range = 0.2,
zoom_range = 0.2,
horizontal_flip = True)
test_data_preprocess = (1./255)
train = train_data_preprocess.flow_from_directory(
'dataset/training',
target_size = (128,128),
batch_size = 32,
class_mode = 'binary')
test = train_data_preprocess.flow_from_directory(
'dataset/test',
target_size = (128,128),
batch_size = 32,
class_mode = 'binary')
## Initialize the Convolutional Neural Net
# Initialising the CNN
cnn = Sequential()
# Step 1 - Convolution
# Step 2 - Pooling
cnn.add(Conv2D(32, (3, 3), input_shape = (128, 128, 3), activation = 'relu'))
cnn.add(MaxPooling2D(pool_size = (2, 2)))
# Adding a second convolutional layer
cnn.add(Conv2D(32, (3, 3), activation = 'relu'))
cnn.add(MaxPooling2D(pool_size = (2, 2)))
# Step 3 - Flattening
cnn.add(Flatten())
# Step 4 - Full connection
cnn.add(Dense(units = 128, activation = 'relu'))
cnn.add(Dense(units = 1, activation = 'sigmoid'))
# Compiling the CNN
cnn.compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics = ['accuracy'])
history = cnn.fit_generator(train,
steps_per_epoch = 250,
epochs = 25,
validation_data = test,
validation_steps = 2000)
plt.plot(history.history['acc'])
plt.plot(history.history['val_acc'])
plt.title('Model Accuracy')
plt.ylabel('accuracy')
plt.xlabel('epoch')
plt.legend(['train', 'test'], loc='upper left')
plt.show()
plt.plot(history.history['loss'])
plt.plot(history.history['val_loss'])
plt.title('Model Loss')
plt.ylabel('loss')
plt.xlabel('epoch')
plt.legend(['train', 'test'], loc='upper left')
plt.show()
test_image = image.load_img('\\dataset\\', target_size=(128,128))
test_image = image.img_to_array(test_image)
test_image = np.expand_dims(test_image, axis=0)
result = cnn.predict(test_image)
print(result)
if result[0][0] == 1:
print('feature extracted and classified')
else:
print('none')
@app.route('/message',methods=['POST','GET'])
def message():
s1=""
vid = request.args.get('vid')
act = request.args.get('act')
value=[]
mycursor = mydb.cursor()
mycursor.execute("SELECT count(*) FROM vt_face where vid=%s",(vid, ))
cnt = mycursor.fetchone()[0]
return render_template('message.html', vid=vid)
@app.route('/pro1',methods=['POST','GET'])
def pro1():
s1=""
vid = request.args.get('vid')
act = request.args.get('act')
value=[]
mycursor = mydb.cursor()
mycursor.execute("SELECT count(*) FROM vt_face where vid=%s",(vid, ))
cnt = mycursor.fetchone()[0]
if act is None:
act=1
act1=int(act)-1
act2=int(act)+1
act3=str(act2)
n=10
if act1<n:
s1="1"
mycursor.execute("SELECT * FROM vt_face where vid=%s limit %s,1",(vid, act1))
value = mycursor.fetchone()
else:
s1="2"
return render_template('pro1.html', value=value,vid=vid, act=act3,s1=s1)
@app.route('/pro2',methods=['POST','GET'])
def pro2():
s1=""
vid = request.args.get('vid')
act = request.args.get('act')
value=[]
mycursor = mydb.cursor()
mycursor.execute("SELECT count(*) FROM vt_face where vid=%s",(vid, ))
cnt = mycursor.fetchone()[0]
if act is None or act=='0':
act=1
act1=int(act)-1
act2=int(act)+1
act3=str(act2)
n=10
if act1<n:
s1="1"
mycursor.execute("SELECT * FROM vt_face where vid=%s limit %s,1",(vid, act1))
value = mycursor.fetchone()
else:
s1="2"
return render_template('pro2.html', value=value,vid=vid, act=act3,s1=s1)
@app.route('/pro3',methods=['POST','GET'])
def pro3():
s1=""
vid = request.args.get('vid')
act = request.args.get('act')
value=[]
mycursor = mydb.cursor()
mycursor.execute("SELECT count(*) FROM vt_face where vid=%s",(vid, ))
cnt = mycursor.fetchone()[0]
if act is None:
act=1
act1=int(act)-1
act2=int(act)+1
act3=str(act2)
n=10
if act1<n:
s1="1"
mycursor.execute("SELECT * FROM vt_face where vid=%s limit %s,1",(vid, act1))
value = mycursor.fetchone()
else:
s1="2"
return render_template('pro3.html', value=value,vid=vid, act=act3,s1=s1)
@app.route('/pro4',methods=['POST','GET'])
def pro4():
s1=""
vid = request.args.get('vid')
act = request.args.get('act')
value=[]
mycursor = mydb.cursor()
mycursor.execute("SELECT count(*) FROM vt_face where vid=%s",(vid, ))
cnt = mycursor.fetchone()[0]
if act is None:
act=1
act1=int(act)-1
act2=int(act)+1
act3=str(act2)
n=10
if act1<n:
s1="1"
mycursor.execute("SELECT * FROM vt_face where vid=%s limit %s,1",(vid, act1))
value = mycursor.fetchone()
else:
s1="2"
return render_template('pro4.html', value=value,vid=vid, act=act3,s1=s1)
@app.route('/pro5',methods=['POST','GET'])
def pro5():
s1=""
vid = request.args.get('vid')
act = request.args.get('act')
value=[]
mycursor = mydb.cursor()
mycursor.execute("SELECT count(*) FROM vt_face where vid=%s",(vid, ))
cnt = mycursor.fetchone()[0]
if act is None:
act=1
act1=int(act)-1
act2=int(act)+1
act3=str(act2)
n=10
if act1<n:
s1="1"
mycursor.execute("SELECT * FROM vt_face where vid=%s limit %s,1",(vid, act1))
value = mycursor.fetchone()
else:
s1="2"
return render_template('pro5.html', value=value,vid=vid, act=act3,s1=s1)
@app.route('/pro6',methods=['POST','GET'])
def pro6():
s1=""
vid = request.args.get('vid')
act = request.args.get('act')
value=[]
mycursor = mydb.cursor()
mycursor.execute("SELECT count(*) FROM vt_face where vid=%s",(vid, ))
cnt = mycursor.fetchone()[0]
if act is None:
act=1
act1=int(act)-1
act2=int(act)+1
act3=str(act2)
n=10
if act1<n:
s1="1"
mycursor.execute("SELECT * FROM vt_face where vid=%s limit %s,1",(vid, act1))
value = mycursor.fetchone()
else:
s1="2"
return render_template('pro6.html', value=value,vid=vid, act=act3,s1=s1)
@app.route('/pro7',methods=['POST','GET'])
def pro7():
s1=""
vid = request.args.get('vid')
act = request.args.get('act')
value=[]
mycursor = mydb.cursor()
mycursor.execute("SELECT count(*) FROM vt_face where vid=%s",(vid, ))
cnt = mycursor.fetchone()[0]
if act is None:
act=1
act1=int(act)-1
act2=int(act)+1
act3=str(act2)
n=10
if act1<n:
s1="1"
mycursor.execute("SELECT * FROM vt_face where vid=%s limit %s,1",(vid, act1))
value = mycursor.fetchone()
else:
s1="2"
return render_template('pro7.html', value=value,vid=vid, act=act3,s1=s1)
'''@app.route('/view_photo1',methods=['POST','GET'])
def view_photo1():
vid = request.args.get('vid')
value=[]
mycursor = mydb.cursor()
mycursor.execute("SELECT * FROM vt_face where vid=%s",(vid, ))
value = mycursor.fetchall()
return render_template('view_photo1.html', result=value,vid=vid)