Update kvasir-points_datasets_script.py

kvasir-points_datasets_script.py

@@ -23,15 +23,38 @@ import os
 import json
 import pandas as pd
 import hashlib
+from collections import defaultdict
+import numpy as np
 
 
-cal_mid = lambda bx: [[[float(box['xmin'] + box['xmax']) / 2, float(box['ymin'] + box['ymax']) / 2] for box in bx]]
+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_sha256(file_path): return hashlib.sha256(
     open(file_path, 'rb').read()).hexdigest()
 
 
+def convert_to_json_format(file_path, image_width, image_height):
+    with open(file_path, 'r') as file:
+        return [
+            {
+                "label": line.split()[0],
+                "xmin": int((float(line.split()[1]) - float(line.split()[3]) / 2) * image_width),
+                "ymin": int((float(line.split()[2]) - float(line.split()[4]) / 2) * image_height),
+                "xmax": int((float(line.split()[1]) + float(line.split()[3]) / 2) * image_width),
+                "ymax": int((float(line.split()[2]) + float(line.split()[4]) / 2) * image_height),
+            }
+            for line in file.readlines()
+        ]
+
+
+class_map = {"0": "normal",  "1": "cluster", "2": "pinhead"}
+
 hyper_label_img_path = '/global/D1/projects/HOST/Datasets/hyper-kvasir/labeled-images/image-labels.csv'
 
 hyper_df = pd.read_csv(hyper_label_img_path)
@@ -45,6 +68,8 @@ instr_seg_img_base_path = '/global/D1/projects/HOST/Datasets/kvasir-instrument/i
 hyper_seg_imgs = json.load(open(hyper_seg_img_path))
 instr_seg_imgs = json.load(open(instr_seg_img_path))
 
+visem_root = "/global/D1/projects/HOST/Datasets/visem-tracking"
+
 _CITATION = """\
 @article{kvasir,
     title={Kvasir-instrument and Hyper-Kvasir datasets for bounding box annotations},
@@ -122,12 +147,11 @@ class KvasirHyperBBox(datasets.GeneratorBasedBuilder):
                 "label": hyper_entry.Finding,
                 "collection_method": 'counting',
                 "classification": hyper_entry.Classification,
-                "organ": hyper_entry.Organ 
+                "organ": hyper_entry.Organ
             }
 
         for key, entry in instr_seg_imgs.items():
             img_path = os.path.join(instr_seg_img_base_path, f"{key}.jpg")
-            assert len(cal_mid(entry['bbox'])) > 0
             yield key, {
                 "image_data": open(img_path, 'rb').read(),
                 "image_sha256": cal_sha256(img_path),
@@ -139,5 +163,35 @@ class KvasirHyperBBox(datasets.GeneratorBasedBuilder):
                 "organ": "instrument"
             }
 
-#datasets-cli test  /global/D1/projects/HOST/Datasets/hyper-kvasir/sushant-experiments/kvasir-points_datasets_script.py --save_info --all_configs --trust_remote_code
+        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")
+                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_sha256": cal_sha256(image_path),
+                        "points": cal_mid(label_dict[label]),
+                        "count": len(label_dict[label]),
+                        "label": class_map[label],
+                        "collection_method": "counting",
+                        "classification": "sperm",
+                        "organ": "visem dataset"
+                    }
+
+
+# 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
 # huggingface-cli upload kvasir-points . . --repo-type dataset