Face Detection with OpenCV

In this first example we’ll learn how to apply face detection with OpenCV to the video.

First we need to import some required libraries

import os
import cv2
import argparse
import numpy as np
import matplotlib.pyplot as plt

We need to change the path to the path where our model and pictures are.

os.chdir("D:\\DataHacker.rs\\OpenCV Face Detection")

Define parameters:

# DNN stands for OpenCV: Deep Neural Networks
DNN = "TF" # Or CAFFE, or any other suported framework
min_confidence = 0.5 # minimum probability to filter weak detections

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These files can be downloaded from the Internet, or created and trained manually.

For Caffe:

• res10_300x300_ssd_iter_140000_fp16.caffemodel

• deploy.prototxt

For Tensorflow:

• opencv_face_detector_uint8.pb

• opencv_face_detector.pbtxt

# load our serialized model from disk
print("[INFO] loading model...")

if DNN == "CAFFE":
    modelFile = "res10_300x300_ssd_iter_140000_fp16.caffemodel"
    configFile= "deploy.prototxt"
    
    # Here we need to read our pre-trained neural net created using Caffe
    net = cv2.dnn.readNetFromCaffe(configFile, modelFile)
else:
    modelFile = "opencv_face_detector_uint8.pb"
    configFile= "opencv_face_detector.pbtxt"
    
    # Here we need to read our pre-trained neural net created using Tensorflow
    net = cv2.dnn.readNetFromTensorflow(modelFile, configFile)
    
print("[INFO] model loaded.")

[INFO] loading model...
[INFO] model loaded.