Update README.md

README.md

@@ -7,4 +7,319 @@ language:
 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)
+
+```
+
+![sample1](./images/000000000009.jpg)
+![sample2](./images/000000000025.jpg)
+
+
+## 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)
+
+```
+
+![sample1](./images/panoptic_000000000009.jpg)
+![sample2](./images/panoptic_000000000025.jpg)
+
+
+