Merge branch 'IT18028188_Praveen' into 'master'

v1.16

See merge request !92

UI/Criminal_registration.html

@@ -29,14 +29,14 @@
             </div>
             <h1 class="mx-auto my-0 text-uppercase" style="text-align: center;">CRIMINAL REGISTRATION</h1>
 
-            <form id="myform" style="margin-top: 5px;">
+            <form id="myform">
                 <div class="container">
                     <div class="row">
                         <div class="col" style="padding-left: 100px;}">
                             <div class="form-input" style="margin-bottom: 8px;">
                                 <label class="required">Full name</label><br>
                                 <input class="input_f" type="text" name="first_name" id="Name"
-                                       onkeypress="return lettersValidate(event)" required/>
+                                        required/>
                             </div>
                             <div class="form-input" style="margin-bottom: 8px;">
                                 <label for="age" class="required">Age</label><br>

face_detection_recognition/Testing_files/accuracy.py

+import pandas as pd
+import seaborn as sns
+import statsmodels.formula.api as smf
+from sklearn.linear_model import LinearRegression
+from sklearn import metrics
+import numpy as np
+
+# read data into a DataFrame
+data = pd.read_csv('C:\\Users\\Praveen\\Desktop\\test.csv', index_col=0)
+data.head()
+
+# # create a fitted model
+lm1 = smf.ols(formula='check ~ Robbery', data=data).fit()
+# print(lm1.params)
+
+### STATSMODELS ###
+
+# create a fitted model with all three features
+lm1 = smf.ols(formula='check ~ Robbery + Aggraveted_Assualt + Weapons_Dealing +	Mortor_vehicle_Theft +	Kidnapping_Cases +	Murder_cases +	Fighting_in_public', data=data).fit()
+
+# print the coefficients
+print(lm1.params)
+
+# create X and y
+feature_cols = ['Robbery', 'Aggraveted Assualt', 'Weapons Dealing',	'Mortor vehicle Theft',	'Kidnapping Cases',	'Murder cases',	'Fighting in public']
+X = data[feature_cols]
+y = data.check
+
+# instantiate and fit
+lm2 = LinearRegression()
+lm2.fit(X, y)
+
+# print the coefficients
+print(lm2.intercept_)
+print(lm2.coef_)
+
+list(zip(feature_cols, lm2.coef_))
+
+### STATSMODELS ###
+
+# print a summary of the fitted model
+print(lm1.summary())
\ No newline at end of file

face_detection_recognition/Testing_files/snapping_input.py

 # import cv2
 #
 # # input video
-# cap = cv2.VideoCapture("E:/Research/Criminal investigation management system/Input video/video3.mp4")
+# cap = cv2.VideoCapture("C:/Users/Praveen/Desktop/video.mp4")
 #
 # i = 0
 # while cap.isOpened():
 #     flag, frame = cap.read()
 #     if flag == False:
 #         break
-#     cv2.imwrite('E:/Research/Criminal investigation management system/Output snaps/new' + str(i) + '.jpg', frame)
+#     cv2.imwrite('C:/Users/Praveen/Desktop/New folder/new' + str(i) + '.jpg', frame)
 #     i += 1
 #
 # # #Test Commit