V1.22

UI/AbnormalBehaviourDetection.html

@@ -26,7 +26,7 @@
                 <h1 class="mx-auto my-0 text-uppercase">Abnormal Behavior Detection</h1>
                 <h2 class="text-white-50 mx-auto mt-2 mb-5">Detect suspects & abnormal behaviors accompanied by
                     Threatening Weapons (Hand Guns, Knives, Grenade)</h2>
-                <img src="assets/gun.png" alt="Girl in a jacket" width="200" height="200"
+                <img src="assets/gun_new.png" alt="Girl in a jacket" width="200" height="200"
                      style="position: relative;  top: 15px; opacity:90%;">
                 <div class="row justify-content-md-center" style="padding: 36px;">
                     <div class="col col-lg-2">

abnormal_behavior_detection/HumanDetection/Human_Detection_Video.py

@@ -48,7 +48,7 @@ def capture_humans(path):
     while True:
         ret, frame = cap.read()
         if ret != False:
-            frame = imutils.resize(frame, width=800)
+            frame = imutils.resize(frame, width=600)
             total_frames = total_frames + 1
 
             (H, W) = frame.shape[:2]

abnormal_behavior_detection/ScriptsOrder/Scripts_Order.py

@@ -13,7 +13,8 @@ def data(path):
         path = path
     elif file == "Input.avi":
         path = "E:\\BACKBONE\\videos\\" + file
-
+    elif file == "Input.mp4":
+        path = "E:\\BACKBONE\\videos\\" + file
 
     print("path ->" + path)
 

abnormal_behavior_detection/WeaponDetection/Knife_Detection_Images.py

+import glob
+
+import cv2
+import numpy as np
+import matplotlib.pyplot as plt
+import pandas as pd
+
+count = 1
+
+path = glob.glob("C:/Users/Givindu/Desktop/New folder/frames/New/*.jpg")
+for file in path:
+
+    image = plt.imread(file)
+
+    classes = None
+    with open('coco.names', 'r') as f:
+        classes = [line.strip() for line in f.readlines()]
+
+
+    Width = image.shape[1]
+    Height = image.shape[0]
+
+    # read pre-trained model and config file
+    net = cv2.dnn.readNet('yolov3.weights', 'yolov3.cfg')
+
+    # create input blob
+    # set input blob for the network
+    net.setInput(cv2.dnn.blobFromImage(image, 0.00392, (416,416), (0,0,0), True, crop=False))
+
+    # run inference through the network
+    # and gather predictions from output layers
+
+    layer_names = net.getLayerNames()
+    output_layers = [layer_names[i[0] - 1] for i in net.getUnconnectedOutLayers()]
+    outs = net.forward(output_layers)
+
+
+    class_ids = []
+    confidences = []
+    boxes = []
+
+    #create bounding box
+    for out in outs:
+        for detection in out:
+            scores = detection[5:]
+            class_id = np.argmax(scores)
+            confidence = scores[class_id]
+            if confidence > 0.1:
+                center_x = int(detection[0] * Width)
+                center_y = int(detection[1] * Height)
+                w = int(detection[2] * Width)
+                h = int(detection[3] * Height)
+                x = center_x - w / 2
+                y = center_y - h / 2
+                class_ids.append(class_id)
+                confidences.append(float(confidence))
+                boxes.append([x, y, w, h])
+
+
+    indices = cv2.dnn.NMSBoxes(boxes, confidences, 0.1, 0.1)
+
+    #check if is people detection
+    for i in indices:
+        i = i[0]
+        box = boxes[i]
+        if class_ids[i]==0:
+            label = str(classes[class_id])
+            cv2.rectangle(image, (round(box[0]),round(box[1])), (round(box[0]+box[2]),round(box[1]+box[3])), (0, 0, 0), 2)
+            cv2.putText(image, label, (round(box[0])-10,round(box[1])-10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 0), 2)
+
+            cv2.imwrite('C:/Users/Givindu/Desktop/frames/frame' + str(count) + '.jpg', image)
+            count += 1
+
+    #plt.imshow(image)
\ No newline at end of file

abnormal_behavior_detection/WeaponDetection/Weapon_Detection.py

@@ -15,7 +15,7 @@ count = 1
 start = time.time()
 
 def weaponDetection():
-    cap = cv.VideoCapture('E:\\BACKBONE\\abnormal_behavior_detection\\frames\\Generated Video Output\\Output Video.avi')
+    cap = cv.VideoCapture('E:\\BACKBONE`\\abnormal_behavior_detection\\frames\\Generated Video Output\\Output Video.avi')
     whT = 320
     confThreshold = 0.1
     nmsThreshold = 0.1
@@ -72,7 +72,7 @@ def weaponDetection():
 
 
             # Detect handguns
-            if classIds[i] == '0':
+            if classIds[i] == 0:
             # print(x,y,w,h)
                 text = "{:.4f}%".format((confs[i])*100)
                 print("Suspect Detected : ", "[", text, "]")
@@ -97,7 +97,7 @@ def weaponDetection():
 
 
             # Detect knives
-            if classIds[i] == 1:
+            if classIds[i] == '1':
             # print(x,y,w,h)
                 text = "{:.4f}%".format((confs[i])*100)
                 print("Suspect Detected : ", "[", text, "]")