moved cascades to subfolder; added training data output

Face_Detect with borders.py

@@ -2,6 +2,8 @@ import cv2
 import numpy as np
 import argparse
 import sys
+import time
+import os
 import datetime
 
 def init_argparse() -> argparse.ArgumentParser:
@@ -22,6 +24,12 @@ def init_argparse() -> argparse.ArgumentParser:
     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
@@ -65,12 +73,22 @@ 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"./lbpcascade_frontalface.xml") # CHECK THIS FIRST TROUBLE SHOOTING
+faceCascade = cv2.CascadeClassifier(r"./cascades/lbpcascade_frontalface.xml") # CHECK THIS FIRST TROUBLE SHOOTING
-faceCascade_default = cv2.CascadeClassifier(r"./haarcascade_frontalface_default.xml")
+faceCascade_default = cv2.CascadeClassifier(r"./cascades/haarcascade_frontalface_default.xml")
-faceCascade_alt = cv2.CascadeClassifier(r"./haarcascade_frontalface_alt.xml")
+faceCascade_alt = cv2.CascadeClassifier(r"./cascades/haarcascade_frontalface_alt.xml")
-faceCascade_alt2 = cv2.CascadeClassifier(r"./haarcascade_frontalface_alt2.xml")
+faceCascade_alt2 = cv2.CascadeClassifier(r"./cascades/haarcascade_frontalface_alt2.xml")
-faceCascade_alttree = cv2.CascadeClassifier(r"./haarcascade_frontalface_alt_tree.xml")
+faceCascade_alttree = cv2.CascadeClassifier(r"./cascades/haarcascade_frontalface_alt_tree.xml")
-profileFaceCascade = cv2.CascadeClassifier(r"./haarcascade_profileface.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
@@ -129,14 +147,24 @@ while(True):
             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: