Update kvasir-points_datasets_script.py

kvasir-points_datasets_script.py

@@ -15,6 +15,7 @@
 """Dataset for filtered Kvasir-instrument and Hyper-Kvasir with bounding boxes."""
 
 import os
+import io
 import json
 from PIL import Image
 import datasets
@@ -22,17 +23,14 @@ import datasets
 import os
 import json
 import pandas as pd
-import hashlib
+import hashlib  
 from collections import defaultdict
 import numpy as np
 
 
-def cal_mid(bx): return [[[float(box['xmin'] + box['xmax']) / 2,
-                           float(box['ymin'] + box['ymax']) / 2] for box in bx]]
 
-
-def cal_mid_xy(bx): return [{"x": float(box['xmin'] + box['xmax']) / 2,
-                             "y": float(box['ymin'] + box['ymax']) / 2} for box in bx]
+# def cal_mid_xy(bx): return [{"x": float(box['xmin'] + box['xmax']) / 2,
+#                              "y": float(box['ymin'] + box['ymax']) / 2} for box in bx]
 
 
 def cal_sha256(file_path): return hashlib.sha256(
@@ -51,6 +49,23 @@ def convert_to_json_format(file_path, image_width, image_height):
             }
             for line in file.readlines()
         ]
+    
+def get_image_bytes(img_path, max_width=700):
+    img = Image.open(img_path)
+    if img.width <= max_width:
+        return open(img_path, "rb").read(), img.width, img.height, 1.0
+    with io.BytesIO() as b:
+        img.resize((max_width, int(img.height * max_width / img.width))).save(b, "PNG")
+        return b.getvalue(), max_width, int(img.height * max_width / img.width), float(max_width) / img.width
+
+
+def get_bboxes(bx, ratio):
+    return [[box[k] * ratio for k in ('xmin', 'ymin', 'xmax', 'ymax')] for box in bx]
+
+
+def cal_mid(bx, ratio): 
+    return [[((box['xmin'] + box['xmax']) / 2) * ratio,
+             ((box['ymin'] + box['ymax']) / 2) * ratio] for box in bx]
 
 
 class_map = {"0": "normal",  "1": "cluster", "2": "pinhead"}
@@ -111,7 +126,9 @@ class KvasirHyperBBox(datasets.GeneratorBasedBuilder):
         features = datasets.Features({
             "image_data": datasets.Image(),
             "image_sha256": datasets.Value("string"),
-            "points": datasets.Sequence(datasets.Sequence(datasets.Sequence(datasets.Value("float32")))),
+            "img_size": datasets.Sequence(datasets.Value("float32")),
+            "points": datasets.Sequence(datasets.Sequence(datasets.Value("float32"))),
+            "bbox": datasets.Sequence(datasets.Sequence(datasets.Value("float32"))),
             "count": datasets.Value("int64"),
             "label": datasets.Value("string"),
             "collection_method": datasets.Value("string"),
@@ -138,11 +155,14 @@ class KvasirHyperBBox(datasets.GeneratorBasedBuilder):
     def _generate_examples(self):
         for key, entry in hyper_seg_imgs.items():
             img_path = os.path.join(hyper_seg_img_base_path, f"{key}.jpg")
+            img, width, height, ratio = get_image_bytes(img_path)
             hyper_entry = hyper_df.loc[hyper_df['Video file'] == key].iloc[0]
             yield key, {
-                "image_data": open(img_path, 'rb').read(),
+                "image_data": img,
                 "image_sha256": cal_sha256(img_path),
-                "points":  cal_mid(entry['bbox']),
+                "img_size": [width, height],
+                "points":  cal_mid(entry['bbox'], ratio),
+                "bbox": get_bboxes(entry['bbox'], ratio),
                 "count": len(entry['bbox']),
                 "label": hyper_entry.Finding,
                 "collection_method": 'counting',
@@ -152,10 +172,13 @@ class KvasirHyperBBox(datasets.GeneratorBasedBuilder):
 
         for key, entry in instr_seg_imgs.items():
             img_path = os.path.join(instr_seg_img_base_path, f"{key}.jpg")
+            img, width, height, ratio = get_image_bytes(img_path)
             yield key, {
-                "image_data": open(img_path, 'rb').read(),
+                "image_data": img,
                 "image_sha256": cal_sha256(img_path),
-                "points":  cal_mid(entry['bbox']),
+                "img_size": [width, height],
+                "points":  cal_mid(entry['bbox'], ratio),
+                "bbox": get_bboxes(entry['bbox'], ratio),
                 "count": len(entry['bbox']),
                 "label": "instrument",
                 "collection_method": "counting",
@@ -166,24 +189,24 @@ class KvasirHyperBBox(datasets.GeneratorBasedBuilder):
         for folder in os.listdir(visem_root):
             folder_path = os.path.join(visem_root, folder)
             labels_all = os.listdir(folder_path+"/labels")
-            images = os.listdir(folder_path+"/images")
-            height, width = Image.open(os.path.join(
-                folder_path, "images", images[0])).size
             labels = [labels_all[i] for i in np.linspace(
                 0, len(labels_all)-1, 250).astype(int)]
             for label in labels:
                 label_path = os.path.join(folder_path, "labels", label)
                 image_path = label_path.replace(
                     "/labels/", "/images/").replace(".txt", ".jpg")
+                img, width, height, ratio = get_image_bytes(image_path)
                 entry_bbox = convert_to_json_format(label_path, width, height)
                 label_dict = defaultdict(list)
                 for entry in entry_bbox:
                     label_dict[entry['label']].append(entry)
                 for label in label_dict:
                     yield cal_sha256(image_path)+label, {
-                        "image_data": open(image_path, 'rb').read(),
+                        "image_data": img,
                         "image_sha256": cal_sha256(image_path),
-                        "points": cal_mid(label_dict[label]),
+                        "img_size": [width, height],
+                        "points": cal_mid(label_dict[label], ratio),
+                        "bbox": get_bboxes(label_dict[label], ratio),
                         "count": len(label_dict[label]),
                         "label": class_map[label],
                         "collection_method": "counting",
@@ -193,5 +216,5 @@ class KvasirHyperBBox(datasets.GeneratorBasedBuilder):
 
 
 # rm -rf /home/sushant/.cache/huggingface/modules/datasets_modules/datasets/kvasir-points_datasets_script/ /home/sushant/.cache/huggingface/datasets/kvasir-points_datasets_script 
-# datasets-cli test  /global/D1/projects/HOST/Datasets/hyper-kvasir/sushant-experiments/kvasir-points_datasets_script.py --save_info  --all_configs --trust_remote_cod
+# datasets-cli test  /global/D1/projects/HOST/Datasets/hyper-kvasir/sushant-experiments/kvasir-points_datasets_script.py --save_info  --all_configs --trust_remote_code
 # huggingface-cli upload kvasir-points . . --repo-type dataset