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"""
Dataset object for Panoptic Narrative Grounding.
Paper: https://openaccess.thecvf.com/content/ICCV2021/papers/Gonzalez_Panoptic_Narrative_Grounding_ICCV_2021_paper.pdf
"""
import os
from os.path import join, isdir, exists
import torch
from torch.utils.data import Dataset
import cv2
from PIL import Image
from skimage import io
import numpy as np
import textwrap
import matplotlib.pyplot as plt
from matplotlib import transforms
from imgaug.augmentables.segmaps import SegmentationMapsOnImage
import matplotlib.colors as mc
from clip_grounding.utils.io import load_json
from clip_grounding.datasets.png_utils import show_image_and_caption
class PNG(Dataset):
"""Panoptic Narrative Grounding."""
def __init__(self, dataset_root, split) -> None:
"""
Initializer.
Args:
dataset_root (str): path to the folder containing PNG dataset
split (str): MS-COCO split such as train2017/val2017
"""
super().__init__()
assert isdir(dataset_root)
self.dataset_root = dataset_root
assert split in ["val2017"], f"Split {split} not supported. "\
"Currently, only supports split `val2017`."
self.split = split
self.ann_dir = join(self.dataset_root, "annotations")
# feat_dir = join(self.dataset_root, "features")
panoptic = load_json(join(self.ann_dir, "panoptic_{:s}.json".format(split)))
images = panoptic["images"]
self.images_info = {i["id"]: i for i in images}
panoptic_anns = panoptic["annotations"]
self.panoptic_anns = {int(a["image_id"]): a for a in panoptic_anns}
# self.panoptic_pred_path = join(
# feat_dir, split, "panoptic_seg_predictions"
# )
# assert isdir(self.panoptic_pred_path)
panoptic_narratives_path = join(self.dataset_root, "annotations", f"png_coco_{split}.json")
self.panoptic_narratives = load_json(panoptic_narratives_path)
def __len__(self):
return len(self.panoptic_narratives)
def get_image_path(self, image_id: str):
image_path = join(self.dataset_root, "images", self.split, f"{image_id.zfill(12)}.jpg")
return image_path
def __getitem__(self, idx: int):
narr = self.panoptic_narratives[idx]
image_id = narr["image_id"]
image_path = self.get_image_path(image_id)
assert exists(image_path)
image = Image.open(image_path)
caption = narr["caption"]
# show_single_image(image, title=caption, titlesize=12)
segments = narr["segments"]
image_id = int(narr["image_id"])
panoptic_ann = self.panoptic_anns[image_id]
panoptic_ann = self.panoptic_anns[image_id]
segment_infos = {}
for s in panoptic_ann["segments_info"]:
idi = s["id"]
segment_infos[idi] = s
image_info = self.images_info[image_id]
panoptic_segm = io.imread(
join(
self.ann_dir,
"panoptic_segmentation",
self.split,
"{:012d}.png".format(image_id),
)
)
panoptic_segm = (
panoptic_segm[:, :, 0]
+ panoptic_segm[:, :, 1] * 256
+ panoptic_segm[:, :, 2] * 256 ** 2
)
panoptic_ann = self.panoptic_anns[image_id]
# panoptic_pred = io.imread(
# join(self.panoptic_pred_path, "{:012d}.png".format(image_id))
# )[:, :, 0]
# # select a single utterance to visualize
# segment = segments[7]
# segment_ids = segment["segment_ids"]
# segment_mask = np.zeros((image_info["height"], image_info["width"]))
# for segment_id in segment_ids:
# segment_id = int(segment_id)
# segment_mask[panoptic_segm == segment_id] = 1.
utterances = [s["utterance"] for s in segments]
outputs = []
for i, segment in enumerate(segments):
# create segmentation mask on image
segment_ids = segment["segment_ids"]
# if no annotation for this word, skip
if not len(segment_ids):
continue
segment_mask = np.zeros((image_info["height"], image_info["width"]))
for segment_id in segment_ids:
segment_id = int(segment_id)
segment_mask[panoptic_segm == segment_id] = 1.
# store the outputs
text_mask = np.zeros(len(utterances))
text_mask[i] = 1.
segment_data = dict(
image=image,
text=utterances,
image_mask=segment_mask,
text_mask=text_mask,
full_caption=caption,
)
outputs.append(segment_data)
# # visualize segmentation mask with associated text
# segment_color = "red"
# segmap = SegmentationMapsOnImage(
# segment_mask.astype(np.uint8), shape=segment_mask.shape,
# )
# image_with_segmap = segmap.draw_on_image(np.asarray(image), colors=[0, COLORS[segment_color]])[0]
# image_with_segmap = Image.fromarray(image_with_segmap)
# colors = ["black" for _ in range(len(utterances))]
# colors[i] = segment_color
# show_image_and_caption(image_with_segmap, utterances, colors)
return outputs
def overlay_segmask_on_image(image, image_mask, segment_color="red"):
segmap = SegmentationMapsOnImage(
image_mask.astype(np.uint8), shape=image_mask.shape,
)
rgb_color = mc.to_rgb(segment_color)
rgb_color = 255 * np.array(rgb_color)
image_with_segmap = segmap.draw_on_image(np.asarray(image), colors=[0, rgb_color])[0]
image_with_segmap = Image.fromarray(image_with_segmap)
return image_with_segmap
def get_text_colors(text, text_mask, segment_color="red"):
colors = ["black" for _ in range(len(text))]
colors[text_mask.nonzero()[0][0]] = segment_color
return colors
def overlay_relevance_map_on_image(image, heatmap):
width, height = image.size
# resize the heatmap to image size
heatmap = cv2.resize(heatmap, (width, height))
heatmap = np.uint8(255 * heatmap)
heatmap = cv2.applyColorMap(heatmap, cv2.COLORMAP_JET)
heatmap = cv2.cvtColor(heatmap, cv2.COLOR_BGR2RGB)
# create overlapped super image
img = np.asarray(image)
super_img = heatmap * 0.4 + img * 0.6
super_img = np.uint8(super_img)
super_img = Image.fromarray(super_img)
return super_img
def visualize_item(image, text, image_mask, text_mask, segment_color="red"):
segmap = SegmentationMapsOnImage(
image_mask.astype(np.uint8), shape=image_mask.shape,
)
rgb_color = mc.to_rgb(segment_color)
rgb_color = 255 * np.array(rgb_color)
image_with_segmap = segmap.draw_on_image(np.asarray(image), colors=[0, rgb_color])[0]
image_with_segmap = Image.fromarray(image_with_segmap)
colors = ["black" for _ in range(len(text))]
text_idx = text_mask.argmax()
colors[text_idx] = segment_color
show_image_and_caption(image_with_segmap, text, colors)
if __name__ == "__main__":
from clip_grounding.utils.paths import REPO_PATH, DATASET_ROOTS
PNG_ROOT = DATASET_ROOTS["PNG"]
dataset = PNG(dataset_root=PNG_ROOT, split="val2017")
item = dataset[0]
sub_item = item[1]
visualize_item(
image=sub_item["image"],
text=sub_item["text"],
image_mask=sub_item["image_mask"],
text_mask=sub_item["text_mask"],
segment_color="red",
)
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