How to make IMG + FILTER object convertable for glTexImage2D

[buzzdarkyear]

I'm using a tool where an image is adjusted with a zoom function. Because of this it changes into a object which make me not able to apply glTextImage2D on it. I was wondering how I could achieve this.

This question is related to the issue: https://python-forum.io/thread-36043.html
I'm new to this forum so if this is forbidden, my apologies and please let me know so I'll delete it. I'm keeping it there just in case someone has another way of approaching the problem. In this question I'm just asking how to make output of this function applicable on glTextImage2D.

Class that creates img with zoom function:

class Frame:
        boxIsVisible = False
    
        def __init__(self, img, box):
            self.zoom = 0.4
            self.img = img
            self.box = box
            x, y, w, h = box.dim
            self.postFilterBox = BoundingBox(x, y, w, h)
    
        def setZoom(self, amount):
            self.zoom = min(max(amount, 0.01), 0.99)
    
        def filter(self):
    
            # Declare basic variables
            screenHeight = self.img.shape[0]
            screenWidth = self.img.shape[1]
            screenRatio = float(screenWidth) / screenHeight
    
            (boxX, boxY, boxW, boxH) = self.box.dim
            distX1 = boxX
            distY1 = boxY                               # dist refers to the distances in front of and
            distX2 = screenWidth - distX1 - boxW        # behind the face detection box
            distY2 = screenHeight - distY1 - boxH       # EX: |---distX1----[ :) ]--distX2--|
    
            # Equalize x's and y's to shortest length
            if distX1 > distX2:
                distX1 = distX2
            if distY1 > distY2:
                distY1 = distY2
    
            distX = distX1      # Set to an equal distance value
            distY = distY1
    
            # Trim sides to match original aspect ratio
            centerX = distX + (boxW / 2.0)
            centerY = distY + (boxH / 2.0)
            distsRatio = centerX / centerY
    
            if screenRatio < distsRatio:
                offset = centerX - (centerY * screenRatio)
                distX -= offset
            elif screenRatio > distsRatio:
                offset = centerY - (centerX / screenRatio)
                distY -= offset
    
            # Make screen to box ratio constant
            # (constant can be changed as ZOOM in main.py)
            if screenWidth > screenHeight:
                distX = min(0.5 * ((boxW / self.zoom) - boxW), distX)
                distY = min(((1.0 / screenRatio) * (distX + (boxW / 2.0))) - (boxH / 2.0), distY)
            else:
                distY = min(0.5 * ((boxH / self.zoom) - boxH), distY)
                distX = min((screenRatio * (distY + (boxH / 2.0))) - (boxW / 2.0), distX)
    
            # Crop image to match distance values
            newX = int(boxX - distX)
            newY = int(boxY - distY)
            newW = int(2 * distX + boxW)
            newH = int(2 * distY + boxH)
            self.crop([newX, newY, newW, newH])
    
            # Resize image to fit original resolution
            resizePercentage = float(screenWidth) / newW
            self.img = cv2.resize(self.img, (screenWidth, screenHeight))
            for i in range(4):
                self.postFilterBox.dim[i] = int(self.postFilterBox.dim[i] * resizePercentage)
    
            # Flip Filtered image on y-axis
            self.img = cv2.flip(self.img, 2)
    
        def drawBox(self):
            (x, y, w, h) = self.postFilterBox.dim
            if x > 0:
                cv2.rectangle(self.img, (x, y), (x + w, y + h), (255, 255, 255), 2)
    
        def crop(self, dim):
            x, y, w, h = dim
            self.img = self.img[y:y + h, x:x + w]
            self.postFilterBox.dim[0] -= x
            self.postFilterBox.dim[1] -= y
    
        def show(self):
            if self.boxIsVisible:
                self.drawBox()
            cv2.imshow("Dolly Zoom", self.img)

My attempt:

    box = BoundingBox(-1, -1, -1, -1)

    # loop
    while(True):
        for event in pygame.event.get():
           if event.type == pygame.QUIT:
               pygame.quit()
               quit()

        #zoom filters
        ZOOM = 0.75
        SHOW_BOX = True  # Show detection box around the largest detected face
        SCALE_FACTOR = 1.2
        MIN_NEIGHBORS = 8
        MINSIZE = (60, 60)

        face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + "haarcascade_frontalface_default.xml")

        #capture & zoom (on face)
        ret, img = cap.read(0)
        img = cv2.flip(img, 1)
        img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
        face = face_cascade.detectMultiScale(
            img,
            scaleFactor=SCALE_FACTOR,
            minNeighbors=MIN_NEIGHBORS,
            minSize=MINSIZE,
        )

        boxes = np.array(face)

        # Linear interpolate bounding box to dimensions of largest detected box
        if boxes.size > 0:
            boxLrg = largestBox(boxes)
            if box.dim[0] == -1:
                box = boxLrg
            else:
                box.lerpShape(boxLrg)

        # Setup frame properties and perform filter
        frame = Frame(img, box)
        frame.boxIsVisible = SHOW_BOX
        frame.setZoom(ZOOM)
        frame.filter()
        box = frame.box

        print(frame)

        # Copy the output from frame function into the sender texture
        glBindTexture(GL_TEXTURE_2D, senderTextureID)
        glTexImage2D( GL_TEXTURE_2D, 0, GL_RGB, width, height, 0, GL_RGB, GL_UNSIGNED_BYTE, frame)

It only shows when I use img, but then it just shows the capture and not the zoom.

glTexImage2D( GL_TEXTURE_2D, 0, GL_RGB, width, height, 0, GL_RGB, GL_UNSIGNED_BYTE, img)

Print output with img assigned:

    [ 44  42  56]
      [ 44  42  57]
      [ 45  43  58]]
    
     [[ 32  41  72]
      [ 35  44  75]
      [ 35  45  74]
      ...
      [ 44  42  55]
      [ 44  42  56]
      [ 46  44  59]]]

Print output with frame assigned:

 <Frame.Frame object at 0x0000012574EB4BA8>
    dict_keys([<class 'numpy.ndarray'>])