Merge branch 'IT18052152_Givindu' into 'master'

V1.16

See merge request !87

abnormal_behavior_detection/WeaponDetection/Knife_Detection.py

+import csv
+import glob
+import os
+import time
+
+import pandas as pd
+import mysql.connector as msql
+from mysql.connector import Error
+import cv2
+import cv2 as cv
+import numpy as np
+import imutils
+
+count = 1
+start = time.time()
+
+def weaponDetection():
+    cap = cv.VideoCapture('E:\\BACKBONE\\abnormal_behavior_detection\\frames\\Generated Video Output\\Output Video.avi')
+    whT = 320
+    confThreshold = 0.1
+    nmsThreshold = 0.1
+
+    directory = 'Suspects with Weapons'
+    parent_dir = "E:/BACKBONE/abnormal_behavior_detection/frames/"
+    path = os.path.join(parent_dir, directory)
+    os.mkdir(path)
+
+
+    classesFile = "E:/BACKBONE/abnormal_behavior_detection/Models/Knife Detection/obj.names"
+    classNames = []
+    with open(classesFile, 'rt') as f:
+        classNames = f.read().rstrip('\n').split('\n')
+
+    print("  ")
+    print("STEP 3 ON PROGRESS...")
+
+    ## Model Files
+    modelConfiguration = "E:/BACKBONE/abnormal_behavior_detection/Models/Knife Detection/yolov4-custom.cfg"
+    modelWeights = "E:/BACKBONE/abnormal_behavior_detection/Models/Knife Detection/yolov4-custom_last.weights"
+    net = cv.dnn.readNetFromDarknet(modelConfiguration, modelWeights)
+    net.setPreferableBackend(cv.dnn.DNN_BACKEND_OPENCV)
+    net.setPreferableTarget(cv.dnn.DNN_TARGET_CPU)
+
+
+    def findObjects(outputs, img):
+        hT, wT, cT = img.shape
+        bbox = []
+        classIds = []
+        confs = []
+        global count
+        Num = 0
+        field = ["ID", "Incident Type", "Used Weapon", "Time"]
+
+        for output in outputs:
+            for det in output:
+                scores = det[5:]
+                classId = np.argmax(scores)
+                confidence = scores[classId]
+                if confidence > confThreshold:
+                    w, h = int(det[2] * wT), int(det[3] * hT)
+                    x, y = int((det[0] * wT) - w / 2), int((det[1] * hT) - h / 2)
+                    bbox.append([x, y, w, h])
+                    classIds.append(classId)
+                    confs.append(float(confidence))
+
+        indices = cv.dnn.NMSBoxes(bbox, confs, confThreshold, nmsThreshold)
+
+        for i in indices:
+            i = i[0]
+            box = bbox[i]
+            x, y, w, h = box[0], box[1], box[2], box[3]
+            if classIds[i] == 1:
+            # print(x,y,w,h)
+                text = "{:.4f}%".format((confs[i])*100)
+                print("Suspect Detected : ", "[", text, "]")
+                #cv.rectangle(img, (x, y), (x + w, y + h), (255, 0, 255), 2)
+                #cv.putText(img, f'{classNames[classIds[i]].upper()} {int(confs[i] * 100)}%',
+                #           (x, y - 10), cv.FONT_HERSHEY_SIMPLEX, 0.6, (255, 0, 255), 2)
+
+                cv2.imwrite('E:/BACKBONE/abnormal_behavior_detection/frames/Suspects with Weapons/frame%d.jpg' % count, img)
+                count += 1
+
+                # Create CSV File
+                type = "Stabbing Attack"
+                weapon = "Knife used as the weapon"
+                with open('E:/BACKBONE/abnormal_behavior_detection/CSV/Abnormal Activity.csv', 'w', newline='') as file:
+                    Num += 1
+                    TimeD = time.time() - start
+                    arr = [Num, type, weapon, TimeD]
+                    writer = csv.writer(file)
+                    writer.writerow(field)
+                    writer.writerow(arr)
+
+
+    while True:
+        success, img = cap.read()
+        if success != False:
+            img = imutils.resize(img, width=600)
+            blob = cv.dnn.blobFromImage(img, 1 / 255, (whT, whT), [0, 0, 0], 1, crop=False)
+            net.setInput(blob)
+            layersNames = net.getLayerNames()
+            outputNames = [(layersNames[i[0] - 1]) for i in net.getUnconnectedOutLayers()]
+            outputs = net.forward(outputNames)
+            findObjects(outputs, img)
+
+
+            #cv.imshow('Image', img)
+            #key = cv2.waitKey(1)
+            #if key == ord('q'):
+             #   break
+
+        else:
+            break
+
+    try:
+        conn = msql.connect(host='127.0.0.1', port=3306,
+                            database='criminal_investigation', user='root',
+                            password='')
+        if conn.is_connected():
+            # Inserting csv file data to database
+            criminalData = pd.read_csv('E:/BACKBONE/abnormal_behavior_detection/CSV/Abnormal Activity.csv', index_col=False, delimiter=',')
+            criminalData.head()
+            print("  ")
+            print(criminalData)
+            cursor = conn.cursor()
+            cursor.execute("select database();")
+            record = cursor.fetchone()
+            print("  ")
+            print("You're connected to database: ", record)
+            for i, row in criminalData.iterrows():
+                sql = "INSERT INTO criminal_investigation.behavior_details VALUES (%s,%s,%s,%s)"
+                cursor.execute(sql, tuple(row))
+                print("Record inserted")
+                print("Values inserted")
+            conn.commit()
+    except Error as e:
+        print("Error while connecting to MySQL", e)
+
+    cap.release()
+    cv2.destroyAllWindows()
+    print("  ")
+    print("*** ALL STEPS EXECUTED SUCCESSFULLY ***")
+
+# main()