added more cascades for better detection rates

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()