Object-detection

Demo Code for test

import cv2 as cv import numpy as np

#Write down conf, nms thresholds,inp width/height confThreshold = 0.25 nmsThreshold = 0.40 inpWidth = 416 inpHeight = 416

#Load names of classes and turn that into a list classesFile = "coco.names" classes = None

with open(classesFile,'rt') as f: classes = f.read().rstrip('\n').split('\n')

#Model configuration modelConf = 'yolov3.cfg' modelWeights = 'yolov3.weights'

def postprocess(frame, outs): frameHeight = frame.shape[0] frameWidth = frame.shape[1]

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




for out in outs:
    for detection in out:
        
        scores = detection [5:]
        classID = np.argmax(scores)
        confidence = scores[classID]

        if confidence > confThreshold:
            centerX = int(detection[0] * frameWidth)
            centerY = int(detection[1] * frameHeight)

            width = int(detection[2]* frameWidth)
            height = int(detection[3]*frameHeight )

            left = int(centerX - width/2)
            top = int(centerY - height/2)

            classIDs.append(classID)
            confidences.append(float(confidence))
            boxes.append([left, top, width, height])

indices = cv.dnn.NMSBoxes (boxes,confidences, confThreshold, nmsThreshold )

indices = cv.dnn.NMSBoxes(boxes, confidences, confThreshold, nmsThreshold)
for i in indices:
    i = i[0]
    box = boxes[i]
    left = box[0]
    top = box[1]
    width = box[2]
    height = box[3]
    
    drawPred(classIDs[i], confidences[i], left, top, left + width, top + height)

def drawPred(classId, conf, left, top, right, bottom): # Draw a bounding box. cv.rectangle(frame, (left, top), (right, bottom), (255, 178, 50), 3)

label = '%.2f' % conf

# Get the label for the class name and its confidence
if classes:
    assert (classId < len(classes))
    label = '%s:%s' % (classes[classId], label)

#A fancier display of the label from learnopencv.com 
# Display the label at the top of the bounding box
#labelSize, baseLine = cv.getTextSize(label, cv.FONT_HERSHEY_SIMPLEX, 0.5, 1)
#top = max(top, labelSize[1])
#cv.rectangle(frame, (left, top - round(1.5 * labelSize[1])), (left + round(1.5 * labelSize[0]), top + baseLine),
             #(255, 255, 255), cv.FILLED)
# cv.rectangle(frame, (left,top),(right,bottom), (255,255,255), 1 )
#cv.putText(frame, label, (left, top), cv.FONT_HERSHEY_SIMPLEX, 0.75, (0, 0, 0), 1)
cv.putText(frame, label, (left,top), cv.FONT_HERSHEY_SIMPLEX, 2, (255, 255, 255), 3)

def getOutputsNames(net): # Get the names of all the layers in the network layersNames = net.getLayerNames()

# Get the names of the output layers, i.e. the layers with unconnected outputs
return [layersNames[i[0] - 1] for i in net.getUnconnectedOutLayers()]

#Set up the net

net = cv.dnn.readNetFromDarknet(modelConf, modelWeights) net.setPreferableBackend(cv.dnn.DNN_BACKEND_OPENCV) net.setPreferableTarget(cv.dnn.DNN_TARGET_CPU)

#Process inputs winName = 'DL OD with OpenCV' cv.namedWindow(winName, cv.WINDOW_NORMAL) cv.resizeWindow(winName, 1000,1000)

cap = cv.VideoCapture(0)

while cv.waitKey(1) < 0:

#get frame from video
hasFrame, frame = cap.read()

#Create a 4D blob from a frame

blob = cv.dnn.blobFromImage(frame, 1/255, (inpWidth, inpHeight), [0,0,0], 1, crop = False)

#Set the input the the net
net.setInput(blob)
outs = net.forward (getOutputsNames(net))


postprocess (frame, outs)

#show the image
cv.imshow(winName, frame)