WGU-Capstone/Face_Detect with borders.py

import cv2
import numpy as np
import argparse
import sys
import time
import os
import datetime

def init_argparse() -> argparse.ArgumentParser:
    parser = argparse.ArgumentParser(
        prog="FaceDetection",
        usage="%(prog)s [OPTION]",
        description="Run face localization"
    )
    parser.add_argument(
        "-v", "--version", action="version", version=f"{parser.prog} version 1.0.1"
    )
    parser.add_argument(
        "-d", "--dashboard", action='store_true', help="Flag to enable live dashboard with statistics - requires terminal width of 90 columns or greater"
    )
    parser.add_argument(
        "-o", "--output", action='store_true', help="show the resultant directions"
    )
    parser.add_argument(
        "-f", "--file", nargs="?", help="File to scan instead of using the camera. Useful for generating training data"
    )
    parser.add_argument(
        "-s", "--no-screen", action='store_true', help="Do not show the successful frames"
    )
    parser.add_argument(
        "-t", "--training-data", action='store_true', help="When set, saves successful face-location images and coordinates to use for future training data"
    )
    return parser

multiplication_factor = 0.05

def get_adjustment_amount(imgSize, currentX, currentY, currentW, currentH):

    current_top_left = [currentX, currentY]
    current_bottom_right = [currentX + currentW, currentY + currentH]

    current_top_right = [currentX + currentW, currentY]

    # find the difference between the left gap and the right gap, divide it by two, and multiply it by the speed scale
    horizontal_adjustment = multiplication_factor * (currentX - (imgSize[0] - current_top_right[0])) / 2
    vertical_adjustment = multiplication_factor * (currentY - (imgSize[0] - current_bottom_right[1])) / 2

    return [horizontal_adjustment, vertical_adjustment]

frames_searched = 1
faces_found = 0
start_time = datetime.datetime.now()

def draw_dashboard(keep_stat_line = False):
    global frames_searched, faces_found, start_time

    elapsed_time = datetime.datetime.now() - start_time

    hours, remainder = divmod(elapsed_time.total_seconds(), 3600)
    minutes, seconds = divmod(remainder, 60)

    f_found = f"{faces_found} Faces found".ljust(16, ' ')
    f_searched = f"{frames_searched} Frames searched".ljust(21, ' ')
    success_rate = f"{round((faces_found / frames_searched) * 100, 1)}% Success rate".ljust(16, ' ')

    if keep_stat_line:
        print(f"{f_found} | {f_searched} | {success_rate} | {round(hours)}h {round(minutes)}m {round(seconds)}s elapsed", flush=True)
    else:
        print(f"{f_found} | {f_searched} | {success_rate} | {round(hours)}h {round(minutes)}m {round(seconds)}s elapsed", end="\r", flush=True)


parser = init_argparse()
args = parser.parse_args()

cap = cv2.VideoCapture(0, cv2.IMREAD_GRAYSCALE) # instead of grayscale you can also use -1, 0, or 1.
faceCascade = cv2.CascadeClassifier(r"./cascades/lbpcascade_frontalface.xml") # CHECK THIS FIRST TROUBLE SHOOTING
faceCascade_default = cv2.CascadeClassifier(r"./cascades/haarcascade_frontalface_default.xml")
faceCascade_alt = cv2.CascadeClassifier(r"./cascades/haarcascade_frontalface_alt.xml")
faceCascade_alt2 = cv2.CascadeClassifier(r"./cascades/haarcascade_frontalface_alt2.xml")
faceCascade_alttree = cv2.CascadeClassifier(r"./cascades/haarcascade_frontalface_alt_tree.xml")
profileFaceCascade = cv2.CascadeClassifier(r"./cascades/haarcascade_profileface.xml")

datestamp = "{:%Y_%m_%d %H_%M_%S}".format(datetime.datetime.now())
output_dir = r"./output/" + datestamp + r"/"


if args.training_data: 
    if not os.path.exists(output_dir):
        os.makedirs(output_dir)
    with open(output_dir + r"found_faces.csv", 'a') as fd:
        fd.write(f"frame_name, x, y, width, height\n")

tmp, frm = cap.read()
height, width, channels = frm.shape
# print(f"{height*.25}, {width}")
del tmp, frm
#Color is 1, grayscale is 0, and the unchanged is -1
while(True):
    ret, frame = cap.read()
    frames_searched += 1
    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
 
    # Detect faces in the image
    faces = faceCascade.detectMultiScale(
    gray,
    scaleFactor=1.1,
    minNeighbors=5,
    minSize=(30, 30)
    )

    if len(faces) == 0:
        faces = faceCascade_default.detectMultiScale(
            gray,
            scaleFactor=1.1,
            minNeighbors=5,
            minSize=(30,30)
        )

    if len(faces) == 0:
        faces = profileFaceCascade.detectMultiScale(
            gray,
            scaleFactor=1.1,
            minNeighbors=5,
            minSize=(30,30)
        )

    if len(faces) == 0:
        faces = faceCascade_alt.detectMultiScale(
            gray,
            scaleFactor=1.1,
            minNeighbors=5,
            minSize=(30,30)
        )

    if len(faces) == 0:
        faces = faceCascade_alt2.detectMultiScale(
            gray,
            scaleFactor=1.1,
            minNeighbors=5,
            minSize=(30,30)
        )

    if len(faces) == 0:
        faces = faceCascade_alttree.detectMultiScale(
            gray,
            scaleFactor=1.1,
            minNeighbors=5,
            minSize=(30,30)
        )

    # Draw a rectangle around the faces
    for (x, y, w, h) in faces:
        if args.training_data: 
            frame_name = frames_searched
            with open(output_dir + r"found_faces.csv", 'a') as fd:
                fd.write(f"frame_{frame_name}.jpg, {x}, {y}, {w}, {h}\n")
            cv2.imwrite(output_dir + f"frame_{frame_name}.jpg", frame)

        faces_found += 1
        adjustment_required = get_adjustment_amount([width, height], x, y, w, h)
        cv2.rectangle(frame, (x, y), (x+w, y+h), (255, 255, 255))

        if args.output:
            print(f"Adjust right: {adjustment_required[0]}".ljust(90, ' '), flush=True)
            print(f"Adjust up   : {adjustment_required[1]}", flush=True)

    if not args.no_screen: 
        cv2.imshow('frame', frame)

    if args.dashboard:
        draw_dashboard()

    if cv2.waitKey(1) & 0xFF == ord('q'):
        break

draw_dashboard(keep_stat_line=True)
cap.release()