utils.py

from typing import Tuple
import os.path as osp
import PIL
import mmcv
import numpy as np
from detectron2.utils.colormap import colormap
from detectron2.utils.visualizer import VisImage, Visualizer
from mmdet.datasets.coco_panoptic import INSTANCE_OFFSET

CLASSES = [
    'person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train',
    'truck', 'boat', 'traffic light', 'fire hydrant', 'stop sign',
    'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow',
    'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag',
    'tie', 'suitcase', 'frisbee', 'skis', 'snowboard', 'sports ball', 'kite',
    'baseball bat', 'baseball glove', 'skateboard', 'surfboard',
    'tennis racket', 'bottle', 'wine glass', 'cup', 'fork', 'knife', 'spoon',
    'bowl', 'banana', 'apple', 'sandwich', 'orange', 'broccoli', 'carrot',
    'hot dog', 'pizza', 'donut', 'cake', 'chair', 'couch', 'potted plant',
    'bed', 'dining table', 'toilet', 'tv', 'laptop', 'mouse', 'remote',
    'keyboard', 'cell phone', 'microwave', 'oven', 'toaster', 'sink',
    'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddy bear',
    'hair drier', 'toothbrush', 'banner', 'blanket', 'bridge', 'cardboard',
    'counter', 'curtain', 'door-stuff', 'floor-wood', 'flower', 'fruit',
    'gravel', 'house', 'light', 'mirror-stuff', 'net', 'pillow', 'platform',
    'playingfield', 'railroad', 'river', 'road', 'roof', 'sand', 'sea',
    'shelf', 'snow', 'stairs', 'tent', 'towel', 'wall-brick', 'wall-stone',
    'wall-tile', 'wall-wood', 'water-other', 'window-blind', 'window-other',
    'tree-merged', 'fence-merged', 'ceiling-merged', 'sky-other-merged',
    'cabinet-merged', 'table-merged', 'floor-other-merged', 'pavement-merged',
    'mountain-merged', 'grass-merged', 'dirt-merged', 'paper-merged',
    'food-other-merged', 'building-other-merged', 'rock-merged',
    'wall-other-merged', 'rug-merged', 'background'
]

PREDICATES = [
    'over',
    'in front of',
    'beside',
    'on',
    'in',
    'attached to',
    'hanging from',
    'on back of',
    'falling off',
    'going down',
    'painted on',
    'walking on',
    'running on',
    'crossing',
    'standing on',
    'lying on',
    'sitting on',
    'flying over',
    'jumping over',
    'jumping from',
    'wearing',
    'holding',
    'carrying',
    'looking at',
    'guiding',
    'kissing',
    'eating',
    'drinking',
    'feeding',
    'biting',
    'catching',
    'picking',
    'playing with',
    'chasing',
    'climbing',
    'cleaning',
    'playing',
    'touching',
    'pushing',
    'pulling',
    'opening',
    'cooking',
    'talking to',
    'throwing',
    'slicing',
    'driving',
    'riding',
    'parked on',
    'driving on',
    'about to hit',
    'kicking',
    'swinging',
    'entering',
    'exiting',
    'enclosing',
    'leaning on',
]


def get_colormap(num_colors: int):
    return (np.resize(colormap(), (num_colors, 3))).tolist()


def draw_text(
    viz_img: VisImage = None,
    text: str = None,
    x: float = None,
    y: float = None,
    color: Tuple[float, float, float] = [0, 0, 0],
    size: float = 10,
    padding: float = 5,
    box_color: str = 'black',
    font: str = None,
) -> float:
    text_obj = viz_img.ax.text(
        x,
        y,
        text,
        size=size,
        # family="sans-serif",
        bbox={
            'facecolor': box_color,
            'alpha': 0.8,
            'pad': padding,
            'edgecolor': 'none',
        },
        verticalalignment='top',
        horizontalalignment='left',
        color=color,
        zorder=10,
        rotation=0,
    )
    viz_img.get_image()
    text_dims = text_obj.get_bbox_patch().get_extents()

    return text_dims.width


def show_result(img,
                result,
                is_one_stage,
                num_rel=20,
                show=False,
                out_dir=None,
                out_file=None):
    # Load image
    img = mmcv.imread(img)
    img = img.copy()  # (H, W, 3)
    img_h, img_w = img.shape[:-1]
    
    # Decrease contrast
    img = PIL.Image.fromarray(img)
    converter = PIL.ImageEnhance.Color(img)
    img = converter.enhance(0.01)
    if out_file is not None:
        mmcv.imwrite(np.asarray(img), 'bw'+out_file)

    # Draw masks
    pan_results = result.pan_results

    ids = np.unique(pan_results)[::-1]
    num_classes = 133
    legal_indices = (ids != num_classes)  # for VOID label
    ids = ids[legal_indices]

    # Get predicted labels
    labels = np.array([id % INSTANCE_OFFSET for id in ids], dtype=np.int64)
    labels = [CLASSES[l] for l in labels]

    #For psgtr
    rel_obj_labels = result.labels
    rel_obj_labels = [CLASSES[l - 1] for l in rel_obj_labels]

    # (N_m, H, W)
    segms = pan_results[None] == ids[:, None, None]
    # Resize predicted masks
    segms = [
        mmcv.image.imresize(m.astype(float), (img_w, img_h)) for m in segms
    ]
    # One stage segmentation
    masks = result.masks

    # Choose colors for each instance in coco
    colormap_coco = get_colormap(len(masks)) if is_one_stage else get_colormap(len(segms))
    colormap_coco = (np.array(colormap_coco) / 255).tolist()

    # Viualize masks
    viz = Visualizer(img)
    viz.overlay_instances(
        labels=rel_obj_labels if is_one_stage else labels,
        masks=masks if is_one_stage else segms,
        assigned_colors=colormap_coco,
    )
    viz_img = viz.get_output().get_image()
    if out_file is not None:
        mmcv.imwrite(viz_img, out_file)

    # Draw relations

    # Filter out relations
    n_rel_topk = num_rel
    # Exclude background class
    rel_dists = result.rel_dists[:, 1:]
    # rel_dists = result.rel_dists
    rel_scores = rel_dists.max(1)
    # rel_scores = result.triplet_scores
    # Extract relations with top scores
    rel_topk_idx = np.argpartition(rel_scores, -n_rel_topk)[-n_rel_topk:]
    rel_labels_topk = rel_dists[rel_topk_idx].argmax(1)
    rel_pair_idxes_topk = result.rel_pair_idxes[rel_topk_idx]
    relations = np.concatenate(
        [rel_pair_idxes_topk, rel_labels_topk[..., None]], axis=1)
    n_rels = len(relations)
    
    top_padding = 20
    bottom_padding = 20
    left_padding = 20
    text_size = 10
    text_padding = 5
    text_height = text_size + 2 * text_padding
    row_padding = 10
    height = (top_padding + bottom_padding + n_rels *
              (text_height + row_padding) - row_padding)
    width = img_w
    curr_x = left_padding
    curr_y = top_padding
    
    # # Adjust colormaps
    # colormap_coco = [adjust_text_color(c, viz) for c in colormap_coco]
    viz_graph = VisImage(np.full((height, width, 3), 255))
    
    all_rel_vis = []
    
    for i, r in enumerate(relations):
        s_idx, o_idx, rel_id = r
        s_label = rel_obj_labels[s_idx]
        o_label = rel_obj_labels[o_idx]
        rel_label = PREDICATES[rel_id]
        viz = Visualizer(img)
        viz.overlay_instances(
            labels=[s_label, o_label],
            masks=[masks[s_idx], masks[o_idx]],
            assigned_colors=[colormap_coco[s_idx], colormap_coco[o_idx]],
        )
        viz_masked_img = viz.get_output().get_image()

        viz_graph = VisImage(np.full((40, width, 3), 255))
        curr_x = 2
        curr_y = 2
        text_size = 25
        text_padding = 20
        font = 36
        text_width = draw_text(
            viz_img=viz_graph,
            text=s_label,
            x=curr_x,
            y=curr_y,
            color=colormap_coco[s_idx],
            size=text_size,
            padding=text_padding,
            font=font,
        )
        curr_x += text_width
        # Draw relation text
        text_width = draw_text(
            viz_img=viz_graph,
            text=rel_label,
            x=curr_x,
            y=curr_y,
            size=text_size,
            padding=text_padding,
            box_color='gainsboro',
            font=font,
        )
        curr_x += text_width

        # Draw object text
        text_width = draw_text(
            viz_img=viz_graph,
            text=o_label,
            x=curr_x,
            y=curr_y,
            color=colormap_coco[o_idx],
            size=text_size,
            padding=text_padding,
            font=font,
        )
        output_viz_graph = np.vstack([viz_masked_img, viz_graph.get_image()])
        if show:
           all_rel_vis.append(output_viz_graph)

    return all_rel_vis