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---
license: apache-2.0
task_categories:
- object-detection
language:
- en
size_categories:
- 100K<n<1M
pretty_name: Coco
---
# Coco dataset loader based on tensorflow dataset coco
## Object Detection
```python
import os
from datasets import load_dataset
from PIL import Image, ImageFont, ImageDraw, ImageColor
def calc_lum(rgb):
return (0.2126*rgb[0] + 0.7152*rgb[1] + 0.0722*rgb[2])
COLOR_MAP = [ImageColor.getrgb(code) for name, code in ImageColor.colormap.items()]
def get_text_bbox(bb, tbb, margin, im_w, im_h, anchor="leftBottom"):
m = margin
l, t, r, b = bb
tl, tt, tr, tb = tbb
bbw, bbh = r - l, b - t
tbbw, tbbh = tr - tl, tb - tt
# bbox (left-top)
if anchor == "leftTop":
ax, ay = l, t
if tbbw*3 > bbw or tbbh*4 > bbh:
# align (text box: left-bottom)
x1, y1 = max(ax, 0), max(ay - tb - 2*m, 0)
x2, y2 = min(x1 + tr + 2*m, im_w), min(y1 + tb + 2*m, im_h)
return ((x1, y1, x2, y2), (max(x1+m, 0), max(y1+m, 0)))
else:
# align (text box: left-top)
x1, y1 = max(ax, 0), max(ay, 0)
x2, y2 = min(x1 + tr + 2*m, im_w), min(y1 + tb + 2*m, im_h)
return (( x1, y1, x2, y2), (max(x1+m, 0), max(y1+m, 0)))
elif anchor == "rightTop":
ax, ay = r, t
if tbbw*3 > bbw or tbbh*4 > bbh:
# align (text box: left-bottom)
x2, y1 = max(ax, 0), max(ay - tb - 2*m, 0)
x1, y2 = max(x2 - tr - 2*m, 0), min(y1 + tb + 2*m, im_h)
return ((x1, y1, x2, y2), (max(x1+m, 0), max(y1+m, 0)))
else:
# align (text box: left-top)
x2, y1 = max(ax, 0), max(ay, 0)
x1, y2 = max(x2 - tr - 2*m, 0), min(y1 + tb + 2*m, im_h)
return ((x1, y1, x2, y2), (max(x1+m, 0), max(y1+m, 0)))
elif anchor == "rightBottom":
ax, ay = r, b
if tbbw*3 > bbw or tbbh*4 > bbh:
# align (text box: left-top)
x2, y2 = min(ax, im_w), min(ay + tb + 2*m, im_h)
x1, y1 = max(x2 - tr - 2*m, 0), max(y2 - tb - 2*m, 0)
return ((x1, y1, x2, y2), (max(x1+m, 0), max(y1+m, 0)))
else:
# align (text box: left-bottom)
x2, y2 = min(ax, im_w), max(ay, 0)
x1, y1 = max(x2 - tr - 2*m, 0), max(y2 - tb - 2*m, 0)
return ((x1, y1, x2, y2), (max(x1+m, 0), max(y1+m, 0)))
elif anchor == "leftBottom":
ax, ay = l, b
if tbbw*3 > bbw or tbbh*4 > bbh:
# align (text box: left-top)
x1, y2 = min(ax, im_w), min(ay + tb + 2*m, im_h)
x2, y1 = min(x1 + tr + 2*m, im_w), max(y2 - tb - 2*m, 0)
return ((x1, y1, x2, y2), (max(x1+m, 0), max(y1+m, 0)))
else:
# align (text box: left-bottom)
x1, y2 = min(ax, im_w), max(ay, 0)
x2, y1 = min(x1 + tr + 2*m, im_w), max(y2 - tb - 2*m, 0)
return ((x1, y1, x2, y2), (max(x1+m, 0), max(y1+m, 0)))
elif anchor == "centerBottom":
ax, ay = (l+r)//2, b
if tbbw*3 > bbw or tbbh*4 > bbh:
# align (text box: left-top)
x1, y2 = min(ax - tr//2 - m, im_w), min(ay + tb + 2*m, im_h)
x2, y1 = min(x1 + tr + 2*m, im_w), max(y2 - tb - 2*m, 0)
return ((x1, y1, x2, y2), (max(x1+m, 0), max(y1+m, 0)))
else:
# align (text box: left-bottom)
x1, y2 = min(ax - tr//2 - m, im_w), max(ay, 0)
x2, y1 = min(x1 + tr + 2*m, im_w), max(y2 - tb - 2*m, 0)
return ((x1, y1, x2, y2), (max(x1+m, 0), max(y1+m, 0)))
def draw_bbox(image, objects, out_path, label_names=None, font="Roboto-Bold.ttf", fontsize=15, fill=True, opacity=60, width=2, margin=3, anchor="leftBottom"):
fnt = ImageFont.truetype(font, fontsize)
im_w, im_h = image.size
img = image.convert("RGBA")
overlay = Image.new('RGBA', img.size, (0, 0, 0, 0))
draw = ImageDraw.Draw(overlay)
for bb, lbl_id in zip(objects["bbox"], objects["label"]):
c = COLOR_MAP[min(lbl_id, len(COLOR_MAP)-1)]
fill_c = c + (opacity, ) if fill else None
draw.rectangle((bb[0], bb[1], bb[2], bb[3]), outline=c, fill=fill_c, width=width)
text = ""
if label_names is not None:
text = label_names[lbl_id]
tbb = fnt.getbbox(text)
btn_bbox, text_pos = get_text_bbox(bb, tbb, margin, im_w, im_h, anchor)
fc = (0, 0, 0) if calc_lum(c) > 150 else (255, 255, 255)
draw.rectangle(btn_bbox, outline=c, fill=c + (255, ))
draw.text(text_pos, text, font=fnt, fill=fc + (255, ))
img = Image.alpha_composite(img, overlay)
overlay = Image.new('RGBA', img.size, (0, 0, 0, 0))
draw = ImageDraw.Draw(overlay)
img = img.convert("RGB")
img.save(out_path)
raw_datasets = load_dataset(
"coco.py",
"2017",
cache_dir="./huggingface_datasets",
)
train_dataset = raw_datasets["train"]
label_list = raw_datasets["train"].features["objects"].feature['label'].names
for idx, item in zip(range(10), train_dataset):
draw_bbox(item["image"], item["objects"], item["image/filename"], label_list)
```
## Panoptic segmentation
```python
import numpy as np
from datasets import load_dataset
from PIL import Image, ImageFont, ImageDraw, ImageColor
from transformers.image_transforms import (
rgb_to_id,
)
def calc_lum(rgb):
return (0.2126*rgb[0] + 0.7152*rgb[1] + 0.0722*rgb[2])
COLOR_MAP = [ImageColor.getrgb(code) for name, code in ImageColor.colormap.items()]
def get_text_bbox(bb, tbb, margin, im_w, im_h, anchor="leftBottom"):
m = margin
l, t, r, b = bb
tl, tt, tr, tb = tbb
bbw, bbh = r - l, b - t
tbbw, tbbh = tr - tl, tb - tt
# bbox (left-top)
if anchor == "leftTop":
ax, ay = l, t
if tbbw*3 > bbw or tbbh*4 > bbh:
# align (text box: left-bottom)
x1, y1 = max(ax, 0), max(ay - tb - 2*m, 0)
x2, y2 = min(x1 + tr + 2*m, im_w), min(y1 + tb + 2*m, im_h)
return ((x1, y1, x2, y2), (max(x1+m, 0), max(y1+m, 0)))
else:
# align (text box: left-top)
x1, y1 = max(ax, 0), max(ay, 0)
x2, y2 = min(x1 + tr + 2*m, im_w), min(y1 + tb + 2*m, im_h)
return (( x1, y1, x2, y2), (max(x1+m, 0), max(y1+m, 0)))
elif anchor == "rightTop":
ax, ay = r, t
if tbbw*3 > bbw or tbbh*4 > bbh:
# align (text box: left-bottom)
x2, y1 = max(ax, 0), max(ay - tb - 2*m, 0)
x1, y2 = max(x2 - tr - 2*m, 0), min(y1 + tb + 2*m, im_h)
return ((x1, y1, x2, y2), (max(x1+m, 0), max(y1+m, 0)))
else:
# align (text box: left-top)
x2, y1 = max(ax, 0), max(ay, 0)
x1, y2 = max(x2 - tr - 2*m, 0), min(y1 + tb + 2*m, im_h)
return ((x1, y1, x2, y2), (max(x1+m, 0), max(y1+m, 0)))
elif anchor == "rightBottom":
ax, ay = r, b
if tbbw*3 > bbw or tbbh*4 > bbh:
# align (text box: left-top)
x2, y2 = min(ax, im_w), min(ay + tb + 2*m, im_h)
x1, y1 = max(x2 - tr - 2*m, 0), max(y2 - tb - 2*m, 0)
return ((x1, y1, x2, y2), (max(x1+m, 0), max(y1+m, 0)))
else:
# align (text box: left-bottom)
x2, y2 = min(ax, im_w), max(ay, 0)
x1, y1 = max(x2 - tr - 2*m, 0), max(y2 - tb - 2*m, 0)
return ((x1, y1, x2, y2), (max(x1+m, 0), max(y1+m, 0)))
elif anchor == "leftBottom":
ax, ay = l, b
if tbbw*3 > bbw or tbbh*4 > bbh:
# align (text box: left-top)
x1, y2 = min(ax, im_w), min(ay + tb + 2*m, im_h)
x2, y1 = min(x1 + tr + 2*m, im_w), max(y2 - tb - 2*m, 0)
return ((x1, y1, x2, y2), (max(x1+m, 0), max(y1+m, 0)))
else:
# align (text box: left-bottom)
x1, y2 = min(ax, im_w), max(ay, 0)
x2, y1 = min(x1 + tr + 2*m, im_w), max(y2 - tb - 2*m, 0)
return ((x1, y1, x2, y2), (max(x1+m, 0), max(y1+m, 0)))
elif anchor == "centerBottom":
ax, ay = (l+r)//2, b
if tbbw*3 > bbw or tbbh*4 > bbh:
# align (text box: left-top)
x1, y2 = min(ax - tr//2 - m, im_w), min(ay + tb + 2*m, im_h)
x2, y1 = min(x1 + tr + 2*m, im_w), max(y2 - tb - 2*m, 0)
return ((x1, y1, x2, y2), (max(x1+m, 0), max(y1+m, 0)))
else:
# align (text box: left-bottom)
x1, y2 = min(ax - tr//2 - m, im_w), max(ay, 0)
x2, y1 = min(x1 + tr + 2*m, im_w), max(y2 - tb - 2*m, 0)
return ((x1, y1, x2, y2), (max(x1+m, 0), max(y1+m, 0)))
# Copied from transformers.models.detr.image_processing_detr.masks_to_boxes
def masks_to_boxes(masks: np.ndarray) -> np.ndarray:
"""
Compute the bounding boxes around the provided panoptic segmentation masks.
Args:
masks: masks in format `[number_masks, height, width]` where N is the number of masks
Returns:
boxes: bounding boxes in format `[number_masks, 4]` in xyxy format
"""
if masks.size == 0:
return np.zeros((0, 4))
h, w = masks.shape[-2:]
y = np.arange(0, h, dtype=np.float32)
x = np.arange(0, w, dtype=np.float32)
# see https://github.com/pytorch/pytorch/issues/50276
y, x = np.meshgrid(y, x, indexing="ij")
x_mask = masks * np.expand_dims(x, axis=0)
x_max = x_mask.reshape(x_mask.shape[0], -1).max(-1)
x = np.ma.array(x_mask, mask=~(np.array(masks, dtype=bool)))
x_min = x.filled(fill_value=1e8)
x_min = x_min.reshape(x_min.shape[0], -1).min(-1)
y_mask = masks * np.expand_dims(y, axis=0)
y_max = y_mask.reshape(x_mask.shape[0], -1).max(-1)
y = np.ma.array(y_mask, mask=~(np.array(masks, dtype=bool)))
y_min = y.filled(fill_value=1e8)
y_min = y_min.reshape(y_min.shape[0], -1).min(-1)
return np.stack([x_min, y_min, x_max, y_max], 1)
def draw_seg(image, panoptic_image, oids, labels, out_path, label_names=None, font="Roboto-Bold.ttf", fontsize=15, opacity=160, anchor="leftBottom"):
fnt = ImageFont.truetype(font, fontsize)
im_w, im_h = image.size
masks = np.asarray(panoptic_image, dtype=np.uint32)
masks = rgb_to_id(masks)
oids = np.array(oids, dtype=np.uint32)
masks = masks == oids[:, None, None]
masks = masks.astype(np.uint8)
bboxes = masks_to_boxes(masks)
img = image.convert("RGBA")
for label, mask, bbox in zip(labels, masks, bboxes):
c = COLOR_MAP[min(label, len(COLOR_MAP)-1)]
cf = np.array(c + (opacity, )).astype(np.uint8)
cmask = mask[:, :, None] * cf[None, None, :]
cmask = Image.fromarray(cmask)
img = Image.alpha_composite(img, cmask)
if label_names is not None:
text = label_names[label]
tbb = fnt.getbbox(text)
btn_bbox, text_pos = get_text_bbox(bbox, tbb, 3, im_w, im_h, anchor=anchor)
overlay = Image.new('RGBA', img.size, (0, 0, 0, 0))
draw = ImageDraw.Draw(overlay)
fc = (0, 0, 0) if calc_lum(c) > 150 else (255, 255, 255)
draw.rectangle(btn_bbox, outline=c, fill=c + (255, ))
draw.text(text_pos, text, font=fnt, fill=fc + (255, ))
img = Image.alpha_composite(img, overlay)
img = img.convert("RGB")
img.save(out_path)
raw_datasets = load_dataset(
"coco.py",
"2017_panoptic",
cache_dir="./huggingface_datasets",
# data_dir="./data",
)
train_dataset = raw_datasets["train"]
label_list = raw_datasets["train"].features["panoptic_objects"].feature['label'].names
for idx, item in zip(range(10), train_dataset):
draw_seg(
item["image"],
item["panoptic_image"],
item["panoptic_objects"]["id"],
item["panoptic_objects"]["label"],
"panoptic_" + item["image/filename"],
label_list)
```
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