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Helmet_detection_YOLOV3.py
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Helmet_detection_YOLOV3.py
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from time import sleep
import cv2 as cv
import argparse
import sys
import numpy as np
import os.path
from glob import glob
#from PIL import image
frame_count = 0 # used in mainloop where we're extracting images., and then to drawPred( called by post process)
frame_count_out=0 # used in post process loop, to get the no of specified class value.
# Initialize the parameters
confThreshold = 0.5 #Confidence threshold
nmsThreshold = 0.4 #Non-maximum suppression threshold
inpWidth = 416 #Width of network's input image
inpHeight = 416 #Height of network's input image
# Load names of classes
classesFile = "obj.names";
classes = None
with open(classesFile, 'rt') as f:
classes = f.read().rstrip('\n').split('\n')
# Give the configuration and weight files for the model and load the network using them.
modelConfiguration = "yolov3-obj.cfg";
modelWeights = "yolov3-obj_2400.weights";
net = cv.dnn.readNetFromDarknet(modelConfiguration, modelWeights)
net.setPreferableBackend(cv.dnn.DNN_BACKEND_OPENCV)
net.setPreferableTarget(cv.dnn.DNN_TARGET_CPU)
# Get the names of the output layers
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()]
# Draw the predicted bounding box
def drawPred(classId, conf, left, top, right, bottom):
global frame_count
# 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)
#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])
#print(label) #testing
#print(labelSize) #testing
#print(baseLine) #testing
label_name,label_conf = label.split(':') #spliting into class & confidance. will compare it with person.
if label_name == 'Helmet':
#will try to print of label have people.. or can put a counter to find the no of people occurance.
#will try if it satisfy the condition otherwise, we won't print the boxes or leave it.
cv.rectangle(frame, (left, top - round(1.5*labelSize[1])), (left + round(1.5*labelSize[0]), top + baseLine), (255, 255, 255), cv.FILLED)
cv.putText(frame, label, (left, top), cv.FONT_HERSHEY_SIMPLEX, 0.75, (0,0,0), 1)
frame_count+=1
#print(frame_count)
if(frame_count> 0):
return frame_count
# Remove the bounding boxes with low confidence using non-maxima suppression
def postprocess(frame, outs):
frameHeight = frame.shape[0]
frameWidth = frame.shape[1]
global frame_count_out
frame_count_out=0
classIds = []
confidences = []
boxes = []
# Scan through all the bounding boxes output from the network and keep only the
# ones with high confidence scores. Assign the box's class label as the class with the highest score.
classIds = [] #have to fins which class have hieghest confidence........=====>>><<<<=======
confidences = []
boxes = []
for out in outs:
for detection in out:
scores = detection[5:]
classId = np.argmax(scores)
confidence = scores[classId]
if confidence > confThreshold:
center_x = int(detection[0] * frameWidth)
center_y = int(detection[1] * frameHeight)
width = int(detection[2] * frameWidth)
height = int(detection[3] * frameHeight)
left = int(center_x - width / 2)
top = int(center_y - height / 2)
classIds.append(classId)
#print(classIds)
confidences.append(float(confidence))
boxes.append([left, top, width, height])
# Perform non maximum suppression to eliminate redundant overlapping boxes with
# lower confidences.
indices = cv.dnn.NMSBoxes(boxes, confidences, confThreshold, nmsThreshold)
count_person=0 # for counting the classes in this loop.
for i in indices:
i = i[0]
box = boxes[i]
left = box[0]
top = box[1]
width = box[2]
height = box[3]
#this function in loop is calling drawPred so, try pushing one test counter in parameter , so it can calculate it.
frame_count_out = drawPred(classIds[i], confidences[i], left, top, left + width, top + height)
#increase test counter till the loop end then print...
#checking class, if it is a person or not
my_class='helmet' #======================================== mycode .....
unknown_class = classes[classId]
if my_class == unknown_class:
count_person += 1
#if(frame_count_out > 0):
print(frame_count_out)
if count_person >= 1:
path = 'test_out/'
frame_name=os.path.basename(fn) # trimm the path and give file name.
cv.imwrite(str(path)+frame_name, frame) # writing to folder.
#print(type(frame))
cv.imshow('img',frame)
cv.waitKey(800)
#cv.imwrite(frame_name, frame)
#======================================mycode.........
# Process inputs
winName = 'Deep learning object detection in OpenCV'
cv.namedWindow(winName, cv.WINDOW_NORMAL)
for fn in glob('images/*.jpg'):
frame = cv.imread(fn)
frame_count =0
# Create a 4D blob from a frame.
blob = cv.dnn.blobFromImage(frame, 1/255, (inpWidth, inpHeight), [0,0,0], 1, crop=False)
# Sets the input to the network
net.setInput(blob)
# Runs the forward pass to get output of the output layers
outs = net.forward(getOutputsNames(net))
# Remove the bounding boxes with low confidence
postprocess(frame, outs)
# Put efficiency information. The function getPerfProfile returns the overall time for inference(t) and the timings for each of the layers(in layersTimes)
t, _ = net.getPerfProfile()
#print(t)
label = 'Inference time: %.2f ms' % (t * 1000.0 / cv.getTickFrequency())
#print(label)
cv.putText(frame, label, (0, 15), cv.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255))
#print(label)