Merge pull request #1790 from gratusrichard/master

added face detection implementation using yunet

Face-Detection/Face Detection using Yunet/.gitignore

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+face_detection_yunet_2023mar.onnx

Face-Detection/Face Detection using Yunet/README.md

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+# YuNet Face Detection Model
+
+YuNet is a highly efficient and accurate face detection model developed by the Intel OpenVINO team. It is designed for real-time face detection and can detect multiple faces in an image, along with their facial landmarks.
+
+## Key Features
+
+- **High Accuracy**: YuNet is known for its high detection accuracy and reliability in various environments and conditions.
+- **Real-time Performance**: Optimized for real-time performance, making it suitable for applications requiring fast processing.
+- **Facial Landmarks**: In addition to detecting faces, YuNet also provides precise facial landmarks, such as eyes, nose, and mouth corners.
+
+# Face Detection using YuNet
+
+This repository contains a simple face detection script using the YuNet model for detecting faces in images. The script is written in Python and utilizes OpenCV for image processing and face detection.
+
+## Prerequisites
+
+Ensure you have the following installed on your system:
+- Python 3.6+
+- OpenCV (including the `opencv-contrib-python` package)
+- NumPy
+
+## Installation
+
+
+1. Install the required Python packages.
+   ```sh
+   pip install opencv-python opencv-contrib-python numpy
+   ```
+
+2. Download the pre-trained YuNet model.
+   If the model is not available locally, the script will automatically download it.
+
+## Usage
+
+Run the face detection script with the following command:
+```sh
+python face_detection.py -p /path/to/your/image.jpg
+```
+
+Replace `/path/to/your/image.jpg` with the actual path to the image you want to process.
+
+## Script Overview
+
+### face_detection.py
+
+This is the main script for face detection. It includes the following key functionalities:
+
+- **Model Loading:** Downloads the YuNet model if not already available.
+- **Image Reading:** Reads the input image specified by the user.
+- **Face Detection:** Detects faces and facial landmarks in the image using the YuNet model.
+- **Visualization:** Draws bounding boxes and landmarks on detected faces and displays the results.
+
+### Key Functions
+
+- `visualize_face_detections(image_path, detections)`: Visualizes the face detections on the image.
+

Face-Detection/Face Detection using Yunet/app.py

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+from utils import get_dataset
+import os
+import sys
+import cv2
+import argparse
+import time
+import numpy as np
+
+
+
+parser = argparse.ArgumentParser('-p','--path','image path for processing')
+
+
+args = parser.parse_args()
+
+if not os.path.exists('./face_detection_yunet_2023mar.onnx'):
+
+    print("pretrained model not available, downloading from repository")
+    suc = get_dataset()
+
+    if suc:
+        pass
+    else:
+        print("model could not be downloaded.")
+
+        print(sys.exis())
+
+
+detector = cv2.FaceDetectorYN.create("/content/face_detection_yunet_2023mar_int8.onnx", "", (320, 320),score_threshold = 0.8)
+img = cv2.imread()
+img_W = int(img.shape[1])
+img_H = int(img.shape[0])
+detector.setInputSize((img_W, img_H))
+start_time = time.time()
+detections = detector.detect(img)[1]
+end_time = time.time()
+elapsed_time_ms = ( end_time - start_time ) * 1000
+print(f"Processing time: {elapsed_time_ms:.2f} milliseconds")
+
+
+
+def visualize_face_detections(image_path, detections):
+    image = cv2.imread(image_path)
+
+    for detection in detections:
+        x, y, width, height = map(int, detection[:4])
+        right_eye = tuple(map(int, detection[4:6]))
+        left_eye = tuple(map(int, detection[6:8]))
+        nose_tip = tuple(map(int, detection[8:10]))
+        right_mouth_corner = tuple(map(int, detection[10:12]))
+        left_mouth_corner = tuple(map(int, detection[12:14]))
+        face_score = detection[14]
+
+        cv2.rectangle(image, (x, y), (x + width, y + height), (0, 255, 0), 2)
+
+        cv2.circle(image, right_eye, 3, (255, 0, 0), -1)
+        cv2.circle(image, left_eye, 3, (0, 0, 255), -1)
+        cv2.circle(image, nose_tip, 3, (0, 255, 0), -1)
+        cv2.circle(image, right_mouth_corner, 3, (255, 0, 255), -1)
+        cv2.circle(image, left_mouth_corner, 3, (0, 255, 255), -1)
+
+        cv2.putText(image, f"fs: {face_score:.2f}", (x, y - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255), 1)
+
+    cv2.imshow(image)
+
+image_path = args.path
+visualize_face_detections(image_path, detections)

Face-Detection/Face Detection using Yunet/utils.py

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+import requests
+
+
+
+
+
+def get_dataset():
+    pretrained_dataset_url= "https://github.com/opencv/opencv_zoo/raw/main/models/face_detection_yunet/face_detection_yunet_2023mar.onnx?download="
+
+    file_name = "face_detection_yunet_2023mar.onnx"
+
+
+    response = requests.get(url=pretrained_dataset_url)
+
+    if response.status_code == 200:
+        with open(file_name, 'wb') as file:
+            file.write(response.content)
+        print("File downloaded successfully.")
+        return 1
+    else:
+        print("Failed to download file. Status code:", response.status_code)
+        return 0