removed old file, renamed main file

NN_dataset_generator.py

@@ -1,99 +0,0 @@
-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()