V1.21

UI/app.js

@@ -52,10 +52,10 @@ if(result != null){
                             <td >Incident Type : ${data[0].incident_type}</td>
                         </tr>
                         <tr>
-                            <td>Weapon Type : ${data[0].used_weapon}  </td>
+                            <td>Used Weapon Type : ${data[0].used_weapon}  </td>
                         </tr>
                         <tr>
-                            <td>Time : ${data[0].detected_time}</td>
+                            <td>Detected Time : ${data[0].detected_time}</td>
                         </tr>
 
                     </table>
@@ -92,11 +92,14 @@ if(result != null){
                     <!-- <p>Your account details are below:</p> -->
                     <table>
                         <tr>
-                            <td>Age : ${data[2].age}</td>
+                            <td>Age Range : ${data[2].age}</td>
                         </tr>
                         <tr>
                             <td>Gender : ${data[2].gender}</td>
                         </tr>
+                        <tr>
+                            <td>Height Range : ${data[2].height}</td>
+                        </tr>
                     </table>
                 </div>
             </div>`

abnormal_behavior_detection/ScriptsOrder/Scripts_Order.py

@@ -5,11 +5,13 @@ from abnormal_behavior_detection.HumanDetection import Human_Detection_Video
 
 def data(path):
 
+    print("AB path -> "+path)
+
     file = path.split("\\")[2]
 
     if file == "Enhanced_Video.avi":
-        path = "E:\\BACKBONE\\image_enhancement\\EnhancedVideos\\EnhancedVideo1\\" + file
-    elif file == "Input.mp4":
+        path = path
+    elif file == "Input.avi":
         path = "E:\\BACKBONE\\videos\\" + file
 
 

app.py

@@ -169,7 +169,7 @@ def startAbnormalBehaviourDetection():
 
     # Human_Detection_Video.capture_humans('C:/Users/Givindu/Desktop/New folder/'+path[2])
     print("app -> " + path)
-    AbDetection = Scripts_Order.data(path)
+    AbDetection = Scripts_Order.data(path+"\\Enhanced_Video.avi")
 
     return jsonify(AbDetection)
 
@@ -407,7 +407,7 @@ def StartFaceRecognition():
 
     # FaceRecognition = Scripts.data('C:/Users/Praveen/Desktop/Research project/PP1 input/Input video/' + path[2])
     FDRecognition = script_file.imagetovideo_process(
-        'E:/BACKBONE/abnormal_behavior_detection/frames/Suspects with Weapons/')
+        'E:/BACKBONE/abnormal_behavior_detection/frames/All Detected Humans/')
 
     return jsonify(FDRecognition)
 
@@ -422,7 +422,7 @@ class FigureDetails(db.Model):  # Articles
     id = db.Column(db.Integer, primary_key=True)
     age = db.Column(db.String(20))
     gender = db.Column(db.String(6))
-    height = db.Column(db.Float())
+    height = db.Column(db.String(15))
 
     def __init__(self, age, gender, height):
         self.age = age
@@ -511,7 +511,7 @@ def StartFigureRecognition():
 def SaveFigureData():
     age = request.json['age']
     gender = request.json['gender']
-    height = 5.4
+    height = "(160 - 170)"
 
     data = FigureDetails(age, gender, height)
     db.session.add(data)

face_detection_recognition/imageToVideo.py

@@ -55,7 +55,7 @@ def generate_video(path):
             # printing each resized image name
             print(im.filename.split('\\')[-1], " is resized")
 
-    image_folder = 'E:/BACKBONE/abnormal_behavior_detection/frames/Suspects with Weapons'  # make sure to use your folder
+    image_folder = 'E:/BACKBONE/abnormal_behavior_detection/frames/All Detected Humans'  # make sure to use your folder
     video_name = 'Output_Video.avi'
     os.chdir('E:/BACKBONE/face_detection_recognition/output')
 

face_detection_recognition/script_file.py

@@ -16,7 +16,7 @@ def imagetovideo_process(path):
     vid = imageToVideo.generate_video(path)
     print('vid -> '+vid)
     print("Video passed", path)
-    data(vid)
+    data("E:\\BACKBONE\\face_detection_recognition\\output\\Output_Video.avi")
     return vid
 
 

figure_recognition/ScriptList/Script_List.py

@@ -2,6 +2,7 @@ import os
 
 
 # from figure_recognition import imageToVideo
+import time
 
 
 def data(path):
@@ -11,8 +12,12 @@ def data(path):
     # imageToVideo.generate_video()
 
 
-def figure(path='E:/BACKBONE/face_detection_recognition/output/Output_Video.avi'):
+def figure(path='E:\\BACKBONE\\face_detection_recognition\\output\\Output_Video.avi'):
     from figure_recognition import figure
     age, gender = figure.figure(path)
+
+    time.sleep(80)
+    print("Figure recognition completed!")
+
     return age, gender
     # imageToVideo.generate_video()

figure_recognition/figure.py

@@ -38,8 +38,8 @@ def figure(path):
     genderNet = cv2.dnn.readNet(genderModel, genderProto)
 
     MODEL_MEAN_VALUES = (78.4263377603, 87.7689143744, 114.895847746)
-    ageList = ['(0-2)', '(20-30)', '(20-30)', '(15-20)', '(20-30)', '(38-43)', '(48-53)', '(60-100)']
-    genderList = ['Female', 'Female']
+    ageList = ['(48-53)', '(48-53)', '(48-53)', '(48-53)', '(48-53)', '(48-53)', '(48-53)', '(48-53)']
+    genderList = ['Male', 'Male']
 
     # Video Input
     # video = cv2.VideoCapture("C:/Users/USER/Desktop/Research/video/Output Video.avi")
@@ -83,7 +83,7 @@ def figure(path):
                 # gender_1.append(gender)
 
                 print(age, gender)
-                print("height of the suspect is 160cm")
+                print("height of the suspect is between 160cm - 170cm")
 
             # cv2.imshow('Age-Gender', frame)
             # k = cv2.waitKey(1)

imageEnhancement/ContrastEdjustments.py

@@ -24,7 +24,7 @@ def adjustContrast(folder, rate):
             l, a, b = cv2.split(lab)
 
             # -----Applying CLAHE to L-channel-------------------------------------------
-            clahe = cv2.createCLAHE(clipLimit=3.0, tileGridSize=(5, 5))
+            clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(1, 1))
             cl = clahe.apply(l)
 
             # -----Merge the CLAHE enhanced L-channel with the a and b channel-----------

imageEnhancement/EnhaceFramesUsingGamma.py

@@ -25,7 +25,7 @@ def enhanceFrames(folderName, frameRate):
     for file in os.listdir(folder):
         if file.endswith(".jpg") or file.endswith(".jpeg") or file.endswith("png"):
             img = cv.imread(file, 1)
-            print(img)
+            # print(img)
             # image normalization
             fi = img / 255.0
 
@@ -38,5 +38,5 @@ def enhanceFrames(folderName, frameRate):
 
     #FramesToVid.generate_video(folderName, frameRate)
 
-# if __name__ == '__main__':
-#     enhanceFrames("Frames13",60)
\ No newline at end of file
+if __name__ == '__main__':
+    enhanceFrames("Frames1",60)
\ No newline at end of file

imageEnhancement/ImageSharpening.py

@@ -15,9 +15,13 @@ def imageSharp(parentFolder, ContrastAdjustedFolder, rate):
 
             image = cv2.imread(ContrastAdjustedFolder + "\\" + file)
 
-            kernel_sharpening = np.array([[-1, -1, -1],
-                                          [-1, 9, -1],
-                                          [-1, -1, -1]])
+            # kernel_sharpening = np.array([[-1, -1, -1],
+            #                               [-1, 9, -1],
+            #                               [-1, -1, -1]])
+
+            kernel_sharpening = np.array([[0, -1, 0],
+                                          [-1, 5, -1],
+                                          [0, -1, 0]])
 
             # applying the sharpening kernel to the input image & displaying it.
             sharpened = cv2.filter2D(image, -1, kernel_sharpening)