added dashboard and cli flags

Face_Detect with borders.py

@@ -1,6 +1,28 @@
 import cv2
 import numpy as np
+import argparse
+import sys
+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"
+    )
+    return parser
 
 multiplication_factor = 0.05
 
@@ -17,17 +39,47 @@ def get_adjustment_amount(imgSize, currentX, currentY, currentW, currentH):
 
     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"./lbpcascade_frontalface.xml") # CHECK THIS FIRST TROUBLE SHOOTING
+faceCascade_default = cv2.CascadeClassifier(r"./haarcascade_frontalface_default.xml")
+faceCascade_alt = cv2.CascadeClassifier(r"./haarcascade_frontalface_alt.xml")
+faceCascade_alt2 = cv2.CascadeClassifier(r"./haarcascade_frontalface_alt2.xml")
+faceCascade_alttree = cv2.CascadeClassifier(r"./haarcascade_frontalface_alt_tree.xml")
+profileFaceCascade = cv2.CascadeClassifier(r"./haarcascade_profileface.xml")
 
 tmp, frm = cap.read()
 height, width, channels = frm.shape
-print(f"{height*.25}, {width}")
+# 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
@@ -38,16 +90,60 @@ while(True):
     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:
+        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))
-        print(f"Adjust right: {adjustment_required[0]}")
+        if args.output:
-        print(f"Adjust up   : {adjustment_required[1]}")
+            print(f"Adjust right: {adjustment_required[0]}".ljust(90, ' '), flush=True)
+            print(f"Adjust up   : {adjustment_required[1]}", flush=True)
     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()

NN_dataset_generator.py

@@ -0,0 +1,99 @@
+import cv2
+import numpy as np
+
+
+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]
+
+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_default = cv2.CascadeClassifier(r"./haarcascade_frontalface_default.xml")
+faceCascade_alt = cv2.CascadeClassifier(r"./haarcascade_frontalface_alt.xml")
+faceCascade_alt2 = cv2.CascadeClassifier(r"./haarcascade_frontalface_alt2.xml")
+faceCascade_alttree = cv2.CascadeClassifier(r"./haarcascade_frontalface_alt_tree.xml")
+profileFaceCascade = cv2.CascadeClassifier(r"./haarcascade_profileface.xml")
+
+
+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()
+    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:
+        adjustment_required = get_adjustment_amount([width, height], x, y, w, h)
+        cv2.rectangle(frame, (x, y), (x+w, y+h), (255, 255, 255))
+        print(f"Adjust right: {adjustment_required[0]}")
+        print(f"Adjust up   : {adjustment_required[1]}")
+    cv2.imshow('frame', frame)
+
+    if cv2.waitKey(1) & 0xFF == ord('q'):
+        break
+
+cap.release()

shell.nix

@@ -1,9 +1,6 @@
 { pkgs ? import <nixpkgs> {} }:
 let
-  my-python-packages = ps: with ps; [
+
-    numpy
-    # other python packages
-  ];
 in 
 pkgs.mkShell {
   buildInputs = with pkgs.python311Packages; [