working example

Face_Detect with borders.py

@@ -1,31 +1,24 @@
 import cv2
 import numpy as np
 
-def check_within(imgSize, baseMultiplier, currentX, currentY, currentW, currentH):
 
-    baseYbottom = imgSize[0] * baseMultiplier
-    baseXRight = imgSize[1] * baseMultiplier
-    upperMultiplier = 1 - baseMultiplier
-    baseYTop = imgSize[0] * upperMultiplier
-    baseXLeft = imgSize[1] * upperMultiplier
-    currentBottomY = currentY + currentH
-    currentRightX = currentX + currentW
+multiplication_factor = 0.05
 
-    print(str(baseYbottom) + "    " + str(currentY))
+def get_adjustment_amount(imgSize, currentX, currentY, currentW, currentH):
 
-    if baseYbottom > currentY:
-        return (255, 255, 255)
-    elif baseYTop < currentBottomY:
-        return (0, 0, 0)
-    elif baseXRight > currentX:
-        return (255, 0, 0)
-    elif baseXLeft < currentRightX:
-        return (0, 0, 255)
-    else:
-        return(0, 255, 0)
+    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"C:\Users\Owner\Desktop\Face Detection\haar_frontface.xml") # CHECK THIS FIRST TROUBLE SHOOTING
+faceCascade = cv2.CascadeClassifier(r"./lbpcascade_frontalface.xml") # CHECK THIS FIRST TROUBLE SHOOTING
 
 tmp, frm = cap.read()
 height, width, channels = frm.shape
@@ -45,34 +38,16 @@ while(True):
     minSize=(30, 30)
     )
 
-    print("Found {0} faces!".format(len(faces)))
 
     # Draw a rectangle around the faces
     for (x, y, w, h) in faces:
-        color = check_within([height, width], .1, x, y, w, h)
-        cv2.rectangle(frame, (int(width*.1), int(height*.1)), (int(width*.9), int(height*.9)), (150, 0 , 150))
-        cv2.rectangle(frame, (x, y), (x+w, y+h), color)
-        cv2.imshow('frame', frame)
+        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()
-cap.destroyAllWindows()
-
-
-# you can save the image with 
-# cv2.imwrite('watchgray.png',img)
-
-'''
-instead of the above code you can replace everything below 
-img = cv2....
-with
-
-plt.imshow(img, cmap = 'gray', interpolation = 'bicubic')
-plt.xticks([]), plt.yticks([])  # to hide tick values on X and Y axis
-plt.plot([200,300,400],[100,200,300],'c', linewidth=5)
-plt.show()
-
-for a matplotlib chart
-'''

shell.nix

@@ -1,9 +1,13 @@
 { pkgs ? import <nixpkgs> {} }:
 let
   my-python-packages = ps: with ps; [
-    opencv4
     numpy
     # other python packages
   ];
-  my-python = pkgs.python3.withPackages my-python-packages;
-in my-python.env
+in 
+pkgs.mkShell {
+  buildInputs = with pkgs.python311Packages; [
+    numpy
+    (opencv4.override { enableGtk2 = true; })
+  ];
+}