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