maskrcnn_benchmark/modeling/roi_heads/boundary_head/inference.pybk

# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import numpy as np
import torch
from torch import nn
import torch.nn.functional as F

from maskrcnn_benchmark.structures.bounding_box import BoxList

import cv2

# TODO check if want to return a single BoxList or a composite
# object
class KEPostProcessor(nn.Module):
    """
    From the results of the CNN, post process the kes
    by taking the ke corresponding to the class with max
    probability (which are of fixed size and directly output
    by the CNN) and return the kes in the ke field of the BoxList.

    If a keer object is passed, it will additionally
    project the kes in the image according to the locations in boxes,
    """

    def __init__(self, keer=None):
        super(KEPostProcessor, self).__init__()
        self.keer = keer

    def forward(self, x, boxes):
        """
        Arguments:
            x (Tensor): the ke logits
            boxes (list[BoxList]): bounding boxes that are used as
                reference, one for ech image

        Returns:
            results (list[BoxList]): one BoxList for each image, containing
                the extra field ke
        """
        # ke_prob = x.sigmoid()

        # select kes coresponding to the predicted classes
        num_proposals = x.shape[0]
        labels = [bbox.get_field("labels") for bbox in boxes]
        labels = torch.cat(labels)
        index = torch.arange(num_proposals, device=labels.device)
        ####### outputs

        ke_prob = x[index]
        # print("labels", labels)
        # print("x",x.size())
        # print("ke_",ke_prob.size())
        # assert(0)

        boxes_per_image = [len(box) for box in boxes]
        ke_prob = ke_prob.split(boxes_per_image, dim=0)

        if self.keer:
            ke_prob = self.keer(ke_prob, boxes)

        results = []
        for prob, box in zip(ke_prob, boxes):
            bbox = BoxList(box.bbox, box.size, mode="xyxy")
            for field in box.fields():
                bbox.add_field(field, box.get_field(field))
            bbox.add_field("ke", prob)
            results.append(bbox)

        return results


class KEPostProcessorCOCOFormat(KEPostProcessor):
    """
    From the results of the CNN, post process the results
    so that the kes are pasted in the image, and
    additionally convert the results to COCO format.
    """

    def forward(self, x, boxes):
        # import pycocotools.mask as mask_util
        import numpy as np

        results = super(KEPostProcessorCOCOFormat, self).forward(x, boxes)
        for result in results:
            kes = result.get_field("ke").cpu()
            rles = [
                ke_util.encode(np.array(ke[0, :, :, np.newaxis], order="F"))[0]
                for ke in kes
            ]
            for rle in rles:
                rle["counts"] = rle["counts"].decode("utf-8")
            result.add_field("ke", rles)
        return results


# the next two functions should be merged inside keer
# but are kept here for the moment while we need them
# temporarily gor paste_ke_in_image
def expand_boxes(boxes, scale):
    w_half = (boxes[:, 2] - boxes[:, 0]) * .5
    h_half = (boxes[:, 3] - boxes[:, 1]) * .5
    x_c = (boxes[:, 2] + boxes[:, 0]) * .5
    y_c = (boxes[:, 3] + boxes[:, 1]) * .5

    w_half *= scale
    h_half *= scale

    boxes_exp = torch.zeros_like(boxes)
    boxes_exp[:, 0] = x_c - w_half
    boxes_exp[:, 2] = x_c + w_half
    boxes_exp[:, 1] = y_c - h_half
    boxes_exp[:, 3] = y_c + h_half
    return boxes_exp


def expand_kes(ke, padding):
    N = ke.shape[0]
    M = ke.shape[-1]
    # print("NM ", N ,M)
    pad2 = 2 * padding
    scale = float(M + pad2) / M
    padded_ke = ke.new_zeros((N, 1, M + pad2, M + pad2))
    padded_ke[:, :, padding:-padding, padding:-padding] = ke
    # print("padded_ke ", padded_ke.size())
    return padded_ke, scale


def paste_ke_in_image(ke, box, im_h, im_w, thresh=0.5, padding=1):
    # print("ke ", ke.size(), ke[None].size())
    padded_ke, scale = expand_kes(ke[None], padding=padding)
    ke = padded_ke[0, 0]
    box = expand_boxes(box[None], scale)[0]
    box = box.to(dtype=torch.int32)

    TO_REMOVE = 1
    w = int(box[2] - box[0] + TO_REMOVE)
    h = int(box[3] - box[1] + TO_REMOVE)
    w = max(w, 1)
    h = max(h, 1)

    # Set shape to [batchxCxHxW]
    ke = ke.expand((1, 1, -1, -1))

    # print("ke 2", ke.size())
    # Resize ke
    ke = ke.to(torch.float32)
    ke = F.interpolate(ke, size=(h, w), mode='bilinear', align_corners=False)
    ke = ke[0][0]

    # print("ke3 ", ke.size())

    if thresh >= 0:
        ke = ke > thresh
    else:
        # for visualization and debugging, we also
        # allow it to return an unmodified ke
        ke = (ke * 255).to(torch.uint8)

    im_ke = torch.zeros((im_h, im_w), dtype=torch.uint8)
    x_0 = max(box[0], 0)
    x_1 = min(box[2] + 1, im_w)
    y_0 = max(box[1], 0)
    y_1 = min(box[3] + 1, im_h)

    im_ke[y_0:y_1, x_0:x_1] = ke[
        (y_0 - box[1]) : (y_1 - box[1]), (x_0 - box[0]) : (x_1 - box[0])
    ]
    # print("im_ke ", im_ke.size())
    return im_ke

def scores_to_probs(scores):
    """Transforms CxHxW of scores to probabilities spatially."""
    channels = scores.shape[0]
    for c in range(channels):
        temp = scores[c, :, :]
        max_score = temp.max()
        temp = np.exp(temp - max_score) / np.sum(np.exp(temp - max_score))
        scores[c, :, :] = temp
    return scores

def heatmaps_to_kes(maps, rois):
    # This function converts a discrete image coordinate in a HEATMAP_SIZE x
    # HEATMAP_SIZE image to a continuous ke coordinate. We maintain
    # consistency with ke_to_heatmap_labels by using the conversion from
    # Heckbert 1990: c = d + 0.5, where d is a discrete coordinate and c is a
    # continuous coordinate.
    rois =rois.numpy()
    maps = maps.numpy()
    offset_x = rois[:, 0]
    offset_y = rois[:, 1]

    widths = rois[:, 2] - rois[:, 0]
    heights = rois[:, 3] - rois[:, 1]
    widths = np.maximum(widths, 1)
    heights = np.maximum(heights, 1)
    widths_ceil = np.ceil(widths)
    heights_ceil = np.ceil(heights)

    # NCHW to NHWC for use with OpenCV
    maps = np.transpose(maps, [0, 2, 3, 1])
    # min_size = cfg.KRCNN.INFERENCE_MIN_SIZE
    
    num_kes = 10
    xy_preds = np.zeros(
        (len(rois), 4, num_kes), dtype=np.float32)
    for i in range(len(rois)):
        # if min_size > 0:
            # roi_map_width = int(np.maximum(widths_ceil[i], min_size))
            # roi_map_height = int(np.maximum(heights_ceil[i], min_size))
        # else:
            # roi_map_width = widths_ceil[i]
            # roi_map_height = heights_ceil[i]
        roi_map_width = int(widths_ceil[i])
        roi_map_height = int(heights_ceil[i])

        width_correction = widths[i] / roi_map_width
        height_correction = heights[i] / roi_map_height
        roi_map = cv2.resize(
            maps[i], (roi_map_width, roi_map_height),
            interpolation=cv2.INTER_CUBIC)
        # Bring back to CHW
        roi_map = np.transpose(roi_map, [2, 0, 1])
        roi_map_probs = scores_to_probs(roi_map.copy())
        w = roi_map.shape[2]
        for k in range(num_kes):
            pos = roi_map[k, :, :].argmax()
            x_int = pos % w
            y_int = (pos - x_int) // w
            assert (roi_map_probs[k, y_int, x_int] ==
                    roi_map_probs[k, :, :].max())
            x = (x_int + 0.5) * width_correction
            y = (y_int + 0.5) * height_correction
            xy_preds[i, 0, k] = x + offset_x[i]
            xy_preds[i, 1, k] = y + offset_y[i]
            xy_preds[i, 2, k] = roi_map[k, y_int, x_int]
            xy_preds[i, 3, k] = roi_map_probs[k, y_int, x_int]

    return xy_preds

class KEer(object):
    """
    Projects a set of kes in an image on the locations
    specified by the bounding boxes
    """

    def __init__(self, threshold=0.5, padding=1):
        self.threshold = threshold
        self.padding = padding

    def forward_single_image(self, kes, boxes):
        boxes = boxes.convert("xyxy")
        im_w, im_h = boxes.size
        # print("KEer kes.size()", kes.size(), kes[0].size(), kes[0][0].size())
        # assert(0)
        # res = [
        #     paste_ke_in_image(ke[0], box, im_h, im_w, self.threshold, self.padding)
        #     for ke, box in zip(kes, boxes.bbox)
        # ]
        res = heatmaps_to_kes(kes, boxes.bbox)

        if len(res) > 0:
            # res = torch.stack(res, dim=0)[:, None]
            res = torch.from_numpy(res)
        else:
            res = kes.new_empty((0, 1, kes.shape[-2], kes.shape[-1]))
        print("res inference.py", res.size())
        return res

    def __call__(self, kes, boxes):
        if isinstance(boxes, BoxList):
            boxes = [boxes]

        # Make some sanity check
        assert len(boxes) == len(kes), "kes and boxes should have the same length."

        # TODO:  Is this JIT compatible?
        # If not we should make it compatible.
        results = []
        for ke, box in zip(kes, boxes):
            assert ke.shape[0] == len(box), "Number of objects should be the same."
            # print("ke inference.py", ke.size())
            result = self.forward_single_image(ke, box)
            results.append(result)
        return results


def make_roi_ke_post_processor(cfg):
    if cfg.MODEL.ROI_KE_HEAD.POSTPROCESS_KES:
        ke_threshold = cfg.MODEL.ROI_KE_HEAD.POSTPROCESS_KES_THRESHOLD
        keer = KEer(threshold=ke_threshold, padding=1)
    else:
        keer = None
    ke_post_processor = KEPostProcessor(keer)
    return ke_post_processor