images.py

import numpy as np
import cv2



confidenceThreshold = 0.5
NMSThreshold = 0.3

modelConfiguration = 'cfg/yolov3.cfg'
modelWeights = 'yolov3.weights'

labelsPath = 'classes.names'
labels = open(labelsPath).read().strip().split('\n')

np.random.seed(10)
COLORS = np.random.randint(0, 255, size=(len(labels), 3), dtype="uint8")

net = cv2.dnn.readNetFromDarknet(modelConfiguration, modelWeights)

image = cv2.imread('img/1.jpg')

(H, W) = image.shape[:2]

#Determine output layer names
layerName = net.getLayerNames()
layerName = [layerName[i[0] - 1] for i in net.getUnconnectedOutLayers()]

blob = cv2.dnn.blobFromImage(image, 1 / 255.0, (416, 416), swapRB = True, crop = False)
net.setInput(blob)
layersOutputs = net.forward(layerName)

boxes = []
confidences = []
classIDs = []

for output in layersOutputs:
    for detection in output:
        scores = detection[5:]
        classID = np.argmax(scores)
        confidence = scores[classID]
        if confidence > confidenceThreshold:
            box = detection[0:4] * np.array([W, H, W, H])
            (centerX, centerY,  width, height) = box.astype('int')
            x = int(centerX - (width/2))
            y = int(centerY - (height/2))

            boxes.append([x, y, int(width), int(height)])
            confidences.append(float(confidence))
            classIDs.append(classID)

#Apply Non Maxima Suppression
detectionNMS = cv2.dnn.NMSBoxes(boxes, confidences, confidenceThreshold, NMSThreshold)



if(len(detectionNMS) > 0):
    for i in detectionNMS.flatten():
        (x, y) = (boxes[i][0], boxes[i][1])
        (w, h) = (boxes[i][2], boxes[i][3])

        #color = [int(c) for c in COLORS[classIDs[i]]]
        cv2.rectangle(image, (x, y), (x + w, y + h), 3, 2)
        text = 'firearm'
        cv2.putText(image, text, (x, y - 5), cv2.FONT_HERSHEY_SIMPLEX, 0.5, 3, 2)





cv2.imshow('Image', image)
cv2.waitKey(0)