# It is based on the OpenCV project.
import cv2 as cv
import argparse
import sys
import numpy as np
import os.path
# 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
parser = argparse.ArgumentParser(description='Object Detection using YOLO in OPENCV')
parser.add_argument('--image', help='Path to image file.')
parser.add_argument('--video',default='test3_video.mp4', help='Path to video file.')
args = parser.parse_args()
# Load names of classes
classesFile = "coco.names";
classes = None
with open(classesFile, 'rt') as f:
classes = f.read().rstrip('
').split('
')
# Give the configuration and weight files for the model and load the network using them.
modelConfiguration = "yolov3.cfg";
modelWeights = "yolov3.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):
# Draw a bounding box.
# print(classId)
cv.rectangle(frame, (left, top), (right, bottom), (int(classId)*66+10, int(classId)*33+int(classId)*10-10+33, int(classId)*50+85), 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])
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)
# Remove the bounding boxes with low confidence using nms
def postprocess(frame, outs):
frameHeight = frame.shape[0]
frameWidth = frame.shape[1]
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 = []
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)
confidences.append(float(confidence))
boxes.append([left, top, width, height])
# Perform nms to eliminate redundant overlapping boxes with
# lower confidences.
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 postprocess2(frame, outs):
frameHeight = frame.shape[0]
frameWidth = frame.shape[1]
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 = []
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)
confidences.append(float(confidence))
boxes.append([left, top, width, height])
# Perform nms to eliminate redundant overlapping boxes with
# lower confidences.
indices = cv.dnn.NMSBoxes(boxes, confidences, confThreshold, nmsThreshold)
num=[0,0,0,0,0,0]
person = 0
car=0
motorbike=0
bus=0
bicycle=0
for i in indices:
i = i[0]
box = boxes[i]
left = box[0]
top = box[1]
width = box[2]
height = box[3]
print(str(classes[classIds[i]]))
if(str(classes[classIds[i]])=="person"):
person+=1
num=[person,car,motorbike,bus,bicycle]
if(str(classes[classIds[i]])=="car"):
car+=1
num=[person,car,motorbike,bus,bicycle]
if (str(classes[classIds[i]]) == "motorbike"):
motorbike += 1
num = [person, car, motorbike, bus,bicycle]
if (str(classes[classIds[i]]) == "bus"):
bus += 1
num = [person, car, motorbike, bus,bicycle]
if (str(classes[classIds[i]]) == "bicycle"):
bicycle += 1
num = [person, car, motorbike, bus,bicycle]
return num
# Process inputs
winName = 'Deep learning object detection in OpenCV'
cv.namedWindow(winName, cv.WINDOW_NORMAL)
outputFile = "yolo_out_py.avi"
if (args.image):
# Open the image file
if not os.path.isfile(args.image):
print("Input image file ", args.image, " doesn't exist")
sys.exit(1)
cap = cv.VideoCapture(args.image)
outputFile = args.image[:-4] + '_yolo_out_py.jpg'
elif (args.video):
# Open the video file
if not os.path.isfile(args.video):
print("Input video file ", args.video, " doesn't exist")
sys.exit(1)
cap = cv.VideoCapture(args.video)
outputFile = args.video[:-4] + 'test_video_out.avi'
else:
# Webcam input
cap = cv.VideoCapture(0)
# Get the video writer initialized to save the output video
if (not args.image):
vid_writer = cv.VideoWriter(outputFile, cv.VideoWriter_fourcc('M', 'J', 'P', 'G'), 30,
(round(cap.get(cv.CAP_PROP_FRAME_WIDTH)), round(cap.get(cv.CAP_PROP_FRAME_HEIGHT))))
while cv.waitKey(1) < 0:
# get frame from the video
hasFrame, frame = cap.read()
# Stop the program if reached end of video
if not hasFrame:
print("Done processing !!!")
print("Output file is stored as ", outputFile)
cv.waitKey(3000)
# Release device
cap.release()
break
# 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()
label = 'Inference time: %.2f ms' % (t * 1000.0 / cv.getTickFrequency())
cv.putText(frame, label, (0, 15), cv.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255))
label = 'Person num:%s' % (postprocess2(frame,outs)[0])
cv.putText(frame, label, (0, 30), cv.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 0))
label = 'Car num:%s' % (postprocess2(frame, outs)[1])
cv.putText(frame, label, (0, 45), cv.FONT_HERSHEY_SIMPLEX, 0.5, (240, 255, 255))
label = 'Motorbike num:%s' % (postprocess2(frame, outs)[2])
cv.putText(frame, label, (0, 60), cv.FONT_HERSHEY_SIMPLEX, 0.5, (245, 245, 245))
label = 'Bus num:%s' % (postprocess2(frame, outs)[3])
cv.putText(frame, label, (0, 75), cv.FONT_HERSHEY_SIMPLEX, 0.5, (255, 97, 0))
label = 'Bicycle num:%s' % (postprocess2(frame, outs)[4])
cv.putText(frame, label, (0, 90), cv.FONT_HERSHEY_SIMPLEX, 0.5, (255, 230, 201))
# Write the frame with the detection boxes
if (args.image):
cv.imwrite(outputFile, frame.astype(np.uint8));
else:
vid_writer.write(frame.astype(np.uint8))
cv.imshow(winName, frame)
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