Full face detection pipeline (see https://medium.com/deepvisionguru/modular-image-processing-pipeline-using-opencv-and-python-generators-9edca3ccb696)

process_images_pipeline.py

import os

from pipeline.load_images import LoadImages
from pipeline.cascade_detect_faces import CascadeDetectFaces
from pipeline.save_faces import SaveFaces
from pipeline.save_summary import SaveSummary
from pipeline.display_summary import DisplaySummary

def parse_args():
    import argparse

    # Parse command line arguments
    ap = argparse.ArgumentParser(description="Image processing pipeline")
    ap.add_argument("-i", "--input", required=True,
                    help="path to input image files")
    ap.add_argument("-o", "--output", default="output",
                    help="path to output directory")
    ap.add_argument("-os", "--out-summary", default=None,
                    help="output JSON summary file name")
    ap.add_argument("-c", "--classifier", default="models/haarcascade/haarcascade_frontalface_default.xml",
                    help="path to where the face cascade resides")

    return vars(ap.parse_args())

def main(args):
    # Create pipeline steps
    load_images = LoadImages(args["input"])
    detect_faces = CascadeDetectFaces(args["classifier"])
    save_faces = SaveFaces(args["output"])
    if args["out_summary"]:
        summary_file = os.path.join(args["output"], args["out_summary"])
        save_summary = SaveSummary(summary_file)
    display_summary = DisplaySummary()

    # Create image processing pipeline
    pipeline = load_images | detect_faces | save_faces
    if args["out_summary"]:
        pipeline |= save_summary
    pipeline |= display_summary

    # Iterate through pipeline
    for _ in pipeline:
        pass

    if args["out_summary"]:
        print(f"[INFO] Saving summary to {summary_file}...")
        save_summary.write()

if __name__ == '__main__':
    args = parse_args()
    main(args)