How to use the mtcnn.box_utils.get_image_boxes function in mtcnn

To help you get started, we’ve selected a few mtcnn examples, based on popular ways it is used in public projects.

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foamliu / InsightFace-v2 / mtcnn / detector.py View on Github

probs = output[1].data.cpu().numpy()  # shape [n_boxes, 2]

        keep = np.where(probs[:, 1] > thresholds[1])[0]
        bounding_boxes = bounding_boxes[keep]
        bounding_boxes[:, 4] = probs[keep, 1].reshape((-1,))
        offsets = offsets[keep]

        keep = nms(bounding_boxes, nms_thresholds[1])
        bounding_boxes = bounding_boxes[keep]
        bounding_boxes = calibrate_box(bounding_boxes, offsets[keep])
        bounding_boxes = convert_to_square(bounding_boxes)
        bounding_boxes[:, 0:4] = np.round(bounding_boxes[:, 0:4])

        # STAGE 3

        img_boxes = get_image_boxes(bounding_boxes, image, size=48)
        if len(img_boxes) == 0:
            return [], []
        img_boxes = Variable(torch.FloatTensor(img_boxes))
        output = onet(img_boxes)
        landmarks = output[0].data.cpu().numpy()  # shape [n_boxes, 10]
        offsets = output[1].data.cpu().numpy()  # shape [n_boxes, 4]
        probs = output[2].data.cpu().numpy()  # shape [n_boxes, 2]

        keep = np.where(probs[:, 1] > thresholds[2])[0]
        bounding_boxes = bounding_boxes[keep]
        bounding_boxes[:, 4] = probs[keep, 1].reshape((-1,))
        offsets = offsets[keep]
        landmarks = landmarks[keep]

        # compute landmark points
        width = bounding_boxes[:, 2] - bounding_boxes[:, 0] + 1.0

Team-Neighborhood / awesome-face-detection / mtcnn / detector.py View on Github

probs = output[1].data.numpy()  # shape [n_boxes, 2]

    keep = np.where(probs[:, 1] > thresholds[1])[0]
    bounding_boxes = bounding_boxes[keep]
    bounding_boxes[:, 4] = probs[keep, 1].reshape((-1,))
    offsets = offsets[keep]

    keep = nms(bounding_boxes, nms_thresholds[1])
    bounding_boxes = bounding_boxes[keep]
    bounding_boxes = calibrate_box(bounding_boxes, offsets[keep])
    bounding_boxes = convert_to_square(bounding_boxes)
    bounding_boxes[:, 0:4] = np.round(bounding_boxes[:, 0:4])

    # STAGE 3

    img_boxes = get_image_boxes(bounding_boxes, image, size=48)
    if len(img_boxes) == 0: 
        return [], []
    img_boxes = torch.FloatTensor(img_boxes)
    with torch.no_grad():
        output = onet(img_boxes)
    landmarks = output[0].data.numpy()  # shape [n_boxes, 10]
    offsets = output[1].data.numpy()  # shape [n_boxes, 4]
    probs = output[2].data.numpy()  # shape [n_boxes, 2]

    keep = np.where(probs[:, 1] > thresholds[2])[0]
    bounding_boxes = bounding_boxes[keep]
    bounding_boxes[:, 4] = probs[keep, 1].reshape((-1,))
    offsets = offsets[keep]
    landmarks = landmarks[keep]

    # compute landmark points

foamliu / InsightFace / mtcnn / detector.py View on Github

bounding_boxes = [i for i in bounding_boxes if i is not None]
        bounding_boxes = np.vstack(bounding_boxes)

        keep = nms(bounding_boxes[:, 0:5], nms_thresholds[0])
        bounding_boxes = bounding_boxes[keep]

        # use offsets predicted by pnet to transform bounding boxes
        bounding_boxes = calibrate_box(bounding_boxes[:, 0:5], bounding_boxes[:, 5:])
        # shape [n_boxes, 5]

        bounding_boxes = convert_to_square(bounding_boxes)
        bounding_boxes[:, 0:4] = np.round(bounding_boxes[:, 0:4])

        # STAGE 2

        img_boxes = get_image_boxes(bounding_boxes, image, size=24)
        img_boxes = Variable(torch.FloatTensor(img_boxes).to(device))
        output = rnet(img_boxes)
        offsets = output[0].data.cpu().numpy()  # shape [n_boxes, 4]
        probs = output[1].data.cpu().numpy()  # shape [n_boxes, 2]

        keep = np.where(probs[:, 1] > thresholds[1])[0]
        bounding_boxes = bounding_boxes[keep]
        bounding_boxes[:, 4] = probs[keep, 1].reshape((-1,))
        offsets = offsets[keep]

        keep = nms(bounding_boxes, nms_thresholds[1])
        bounding_boxes = bounding_boxes[keep]
        bounding_boxes = calibrate_box(bounding_boxes, offsets[keep])
        bounding_boxes = convert_to_square(bounding_boxes)
        bounding_boxes[:, 0:4] = np.round(bounding_boxes[:, 0:4])

foamliu / InsightFace-v3 / mtcnn / detector.py View on Github

probs = output[1].data.cpu().numpy()  # shape [n_boxes, 2]

            keep = np.where(probs[:, 1] > thresholds[1])[0]
            bounding_boxes = bounding_boxes[keep]
            bounding_boxes[:, 4] = probs[keep, 1].reshape((-1,))
            offsets = offsets[keep]

            keep = nms(bounding_boxes, nms_thresholds[1])
            bounding_boxes = bounding_boxes[keep]
            bounding_boxes = calibrate_box(bounding_boxes, offsets[keep])
            bounding_boxes = convert_to_square(bounding_boxes)
            bounding_boxes[:, 0:4] = np.round(bounding_boxes[:, 0:4])

            # STAGE 3

            img_boxes = get_image_boxes(bounding_boxes, image, size=48)
            if len(img_boxes) == 0:
                return [], []
            img_boxes = Variable(torch.FloatTensor(img_boxes).to(self.device))
            output = self.onet(img_boxes)
            landmarks = output[0].data.cpu().numpy()  # shape [n_boxes, 10]
            offsets = output[1].data.cpu().numpy()  # shape [n_boxes, 4]
            probs = output[2].data.cpu().numpy()  # shape [n_boxes, 2]

            keep = np.where(probs[:, 1] > thresholds[2])[0]
            bounding_boxes = bounding_boxes[keep]
            bounding_boxes[:, 4] = probs[keep, 1].reshape((-1,))
            offsets = offsets[keep]
            landmarks = landmarks[keep]

            # compute landmark points
            width = bounding_boxes[:, 2] - bounding_boxes[:, 0] + 1.0

foamliu / InsightFace-v3 / mtcnn / detector.py View on Github

bounding_boxes = [i for i in bounding_boxes if i is not None]
            bounding_boxes = np.vstack(bounding_boxes)

            keep = nms(bounding_boxes[:, 0:5], nms_thresholds[0])
            bounding_boxes = bounding_boxes[keep]

            # use offsets predicted by pnet to transform bounding boxes
            bounding_boxes = calibrate_box(bounding_boxes[:, 0:5], bounding_boxes[:, 5:])
            # shape [n_boxes, 5]

            bounding_boxes = convert_to_square(bounding_boxes)
            bounding_boxes[:, 0:4] = np.round(bounding_boxes[:, 0:4])

            # STAGE 2

            img_boxes = get_image_boxes(bounding_boxes, image, size=24)
            img_boxes = Variable(torch.FloatTensor(img_boxes).to(self.device))
            output = self.rnet(img_boxes)
            offsets = output[0].data.cpu().numpy()  # shape [n_boxes, 4]
            probs = output[1].data.cpu().numpy()  # shape [n_boxes, 2]

            keep = np.where(probs[:, 1] > thresholds[1])[0]
            bounding_boxes = bounding_boxes[keep]
            bounding_boxes[:, 4] = probs[keep, 1].reshape((-1,))
            offsets = offsets[keep]

            keep = nms(bounding_boxes, nms_thresholds[1])
            bounding_boxes = bounding_boxes[keep]
            bounding_boxes = calibrate_box(bounding_boxes, offsets[keep])
            bounding_boxes = convert_to_square(bounding_boxes)
            bounding_boxes[:, 0:4] = np.round(bounding_boxes[:, 0:4])

foamliu / InsightFace-v2 / mtcnn / detector.py View on Github

bounding_boxes = [i for i in bounding_boxes if i is not None]
        bounding_boxes = np.vstack(bounding_boxes)

        keep = nms(bounding_boxes[:, 0:5], nms_thresholds[0])
        bounding_boxes = bounding_boxes[keep]

        # use offsets predicted by pnet to transform bounding boxes
        bounding_boxes = calibrate_box(bounding_boxes[:, 0:5], bounding_boxes[:, 5:])
        # shape [n_boxes, 5]

        bounding_boxes = convert_to_square(bounding_boxes)
        bounding_boxes[:, 0:4] = np.round(bounding_boxes[:, 0:4])

        # STAGE 2

        img_boxes = get_image_boxes(bounding_boxes, image, size=24)
        img_boxes = Variable(torch.FloatTensor(img_boxes))
        output = rnet(img_boxes)
        offsets = output[0].data.cpu().numpy()  # shape [n_boxes, 4]
        probs = output[1].data.cpu().numpy()  # shape [n_boxes, 2]

        keep = np.where(probs[:, 1] > thresholds[1])[0]
        bounding_boxes = bounding_boxes[keep]
        bounding_boxes[:, 4] = probs[keep, 1].reshape((-1,))
        offsets = offsets[keep]

        keep = nms(bounding_boxes, nms_thresholds[1])
        bounding_boxes = bounding_boxes[keep]
        bounding_boxes = calibrate_box(bounding_boxes, offsets[keep])
        bounding_boxes = convert_to_square(bounding_boxes)
        bounding_boxes[:, 0:4] = np.round(bounding_boxes[:, 0:4])

foamliu / InsightFace / mtcnn / detector.py View on Github

probs = output[1].data.cpu().numpy()  # shape [n_boxes, 2]

        keep = np.where(probs[:, 1] > thresholds[1])[0]
        bounding_boxes = bounding_boxes[keep]
        bounding_boxes[:, 4] = probs[keep, 1].reshape((-1,))
        offsets = offsets[keep]

        keep = nms(bounding_boxes, nms_thresholds[1])
        bounding_boxes = bounding_boxes[keep]
        bounding_boxes = calibrate_box(bounding_boxes, offsets[keep])
        bounding_boxes = convert_to_square(bounding_boxes)
        bounding_boxes[:, 0:4] = np.round(bounding_boxes[:, 0:4])

        # STAGE 3

        img_boxes = get_image_boxes(bounding_boxes, image, size=48)
        if len(img_boxes) == 0:
            return [], []
        img_boxes = Variable(torch.FloatTensor(img_boxes).to(device))
        output = onet(img_boxes)
        landmarks = output[0].data.cpu().numpy()  # shape [n_boxes, 10]
        offsets = output[1].data.cpu().numpy()  # shape [n_boxes, 4]
        probs = output[2].data.cpu().numpy()  # shape [n_boxes, 2]

        keep = np.where(probs[:, 1] > thresholds[2])[0]
        bounding_boxes = bounding_boxes[keep]
        bounding_boxes[:, 4] = probs[keep, 1].reshape((-1,))
        offsets = offsets[keep]
        landmarks = landmarks[keep]

        # compute landmark points
        width = bounding_boxes[:, 2] - bounding_boxes[:, 0] + 1.0

foamliu / Face-Alignment / mtcnn / detector.py View on Github

bounding_boxes = [i for i in bounding_boxes if i is not None]
            bounding_boxes = np.vstack(bounding_boxes)

            keep = nms(bounding_boxes[:, 0:5], nms_thresholds[0])
            bounding_boxes = bounding_boxes[keep]

            # use offsets predicted by pnet to transform bounding boxes
            bounding_boxes = calibrate_box(bounding_boxes[:, 0:5], bounding_boxes[:, 5:])
            # shape [n_boxes, 5]

            bounding_boxes = convert_to_square(bounding_boxes)
            bounding_boxes[:, 0:4] = np.round(bounding_boxes[:, 0:4])

            # STAGE 2

            img_boxes = get_image_boxes(bounding_boxes, image, size=24)
            img_boxes = Variable(torch.FloatTensor(img_boxes).to(self.device))
            output = self.rnet(img_boxes)
            offsets = output[0].data.cpu().numpy()  # shape [n_boxes, 4]
            probs = output[1].data.cpu().numpy()  # shape [n_boxes, 2]

            keep = np.where(probs[:, 1] > thresholds[1])[0]
            bounding_boxes = bounding_boxes[keep]
            bounding_boxes[:, 4] = probs[keep, 1].reshape((-1,))
            offsets = offsets[keep]

            keep = nms(bounding_boxes, nms_thresholds[1])
            bounding_boxes = bounding_boxes[keep]
            bounding_boxes = calibrate_box(bounding_boxes, offsets[keep])
            bounding_boxes = convert_to_square(bounding_boxes)
            bounding_boxes[:, 0:4] = np.round(bounding_boxes[:, 0:4])

Team-Neighborhood / awesome-face-detection / mtcnn / detector.py View on Github

bounding_boxes = [i for i in bounding_boxes if i is not None]
    bounding_boxes = np.vstack(bounding_boxes)

    keep = nms(bounding_boxes[:, 0:5], nms_thresholds[0])
    bounding_boxes = bounding_boxes[keep]

    # use offsets predicted by pnet to transform bounding boxes
    bounding_boxes = calibrate_box(bounding_boxes[:, 0:5], bounding_boxes[:, 5:])
    # shape [n_boxes, 5]

    bounding_boxes = convert_to_square(bounding_boxes)
    bounding_boxes[:, 0:4] = np.round(bounding_boxes[:, 0:4])

    # STAGE 2

    img_boxes = get_image_boxes(bounding_boxes, image, size=24)
    img_boxes = torch.FloatTensor(img_boxes)
    with torch.no_grad():
        output = rnet(img_boxes)
    offsets = output[0].data.numpy()  # shape [n_boxes, 4]
    probs = output[1].data.numpy()  # shape [n_boxes, 2]

    keep = np.where(probs[:, 1] > thresholds[1])[0]
    bounding_boxes = bounding_boxes[keep]
    bounding_boxes[:, 4] = probs[keep, 1].reshape((-1,))
    offsets = offsets[keep]

    keep = nms(bounding_boxes, nms_thresholds[1])
    bounding_boxes = bounding_boxes[keep]
    bounding_boxes = calibrate_box(bounding_boxes, offsets[keep])
    bounding_boxes = convert_to_square(bounding_boxes)
    bounding_boxes[:, 0:4] = np.round(bounding_boxes[:, 0:4])

foamliu / Face-Alignment / mtcnn / detector.py View on Github

probs = output[1].data.cpu().numpy()  # shape [n_boxes, 2]

            keep = np.where(probs[:, 1] > thresholds[1])[0]
            bounding_boxes = bounding_boxes[keep]
            bounding_boxes[:, 4] = probs[keep, 1].reshape((-1,))
            offsets = offsets[keep]

            keep = nms(bounding_boxes, nms_thresholds[1])
            bounding_boxes = bounding_boxes[keep]
            bounding_boxes = calibrate_box(bounding_boxes, offsets[keep])
            bounding_boxes = convert_to_square(bounding_boxes)
            bounding_boxes[:, 0:4] = np.round(bounding_boxes[:, 0:4])

            # STAGE 3

            img_boxes = get_image_boxes(bounding_boxes, image, size=48)
            if len(img_boxes) == 0:
                return [], []
            img_boxes = Variable(torch.FloatTensor(img_boxes).to(self.device))
            output = self.onet(img_boxes)
            landmarks = output[0].data.cpu().numpy()  # shape [n_boxes, 10]
            offsets = output[1].data.cpu().numpy()  # shape [n_boxes, 4]
            probs = output[2].data.cpu().numpy()  # shape [n_boxes, 2]

            keep = np.where(probs[:, 1] > thresholds[2])[0]
            bounding_boxes = bounding_boxes[keep]
            bounding_boxes[:, 4] = probs[keep, 1].reshape((-1,))
            offsets = offsets[keep]
            landmarks = landmarks[keep]

            # compute landmark points
            width = bounding_boxes[:, 2] - bounding_boxes[:, 0] + 1.0

How to use the mtcnn.box_utils.get_image_boxes function in mtcnn

To help you get started, we’ve selected a few mtcnn examples, based on popular ways it is used in public projects.

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