Update vis_coco

docs/doc_whl.md

@@ -1,23 +1 @@
-<div align="center">
-  <img src="https://github.com/RapidAI/LabelConvert/releases/download/v0/LabelConvertv3.png" width="55%" height="55%"/>
-</div>
-
-
-## Label Convert
-
-<p align="left">
-    <a href=""><img src="https://img.shields.io/badge/Python->=3.6,<3.12-aff.svg"></a>
-    <a href=""><img src="https://img.shields.io/badge/OS-Linux%2C%20Win%2C%20Mac-pink.svg"></a>
-    <a href="https://github.com/RapidAI/LabelConvert/graphs/contributors"><img src="https://img.shields.io/github/contributors/RapidAI/LabelConvert?color=9ea"></a>
-    <a href="https://github.com/RapidAI/LabelConvert/stargazers"><img src="https://img.shields.io/github/stars/RapidAI/LabelConvert?color=ccf" ></a>
-    <a href="https://pepy.tech/project/label_convert"><img src="https://static.pepy.tech/badge/label_convert?period=total&units=abbreviation&left_color=grey&right_color=blue&left_text=Downloads"></a>
-    <a href="https://pypi.org/project/label_convert/"><img alt="PyPI" src="https://img.shields.io/pypi/v/label_convert"></a>
-    <a href="https://choosealicense.com/licenses/apache-2.0/"><img src="https://img.shields.io/badge/License-Apache%202-dfd.svg"></a>
-    <a href="https://semver.org/"><img alt="SemVer2.0" src="https://img.shields.io/badge/SemVer-2.0-brightgreen"></a>
-    <a href="https://github.com/psf/black"><img src="https://img.shields.io/badge/code%20style-black-000000.svg"></a>
-</p>
-
-A dataset format conversion tool for object detection and image segmentation, which supports mutual conversion between **labelme, labelImg tools and YOLO, VOC, and COCO** dataset formats.
-
-
 ### See details for [LabelConvert](https://github.com/RapidAI/LabelConvert)

label_convert/vis_coco.py

@@ -0,0 +1,116 @@
+# -*- encoding: utf-8 -*-
+# @Author: SWHL
+# @Contact: liekkaskono@163.com
+import argparse
+import json
+import platform
+from pathlib import Path
+import random
+
+import cv2
+
+
+class VisCOCO:
+    def __init__(
+        self,
+    ):
+        self.font_size = 0.7
+
+    def __call__(self, img_id: int, json_path, img_path):
+        with open(json_path, "r", encoding="utf-8") as annos:
+            anno_dict = json.load(annos)
+
+        anno_imgs = anno_dict.get("images", None)
+        if anno_imgs is None:
+            raise ValueError(f"The images of {json_path} cannot be empty.")
+
+        print("The anno_dict num_key is:", len(anno_dict))
+        print("The anno_dict key is:", anno_dict.keys())
+        print("The anno_dict num_images is:", len(anno_imgs))
+
+        categories = anno_dict["categories"]
+        categories_dict = {c["id"]: c["name"] for c in categories}
+
+        class_nums = len(categories_dict.keys())
+        color = [
+            (random.randint(0, 255), random.randint(0, 255), random.randint(0, 255))
+            for _ in range(class_nums)
+        ]
+
+        img_info = anno_dict["images"][img_id - 1]
+        img_name = img_info.get("file_name")
+
+        img_full_path = Path(img_path) / img_name
+        image = cv2.imread(str(img_full_path))
+
+        annotations = anno_dict["annotations"]
+        num_bbox = 0
+        img_id = img_info.get("id")
+        for anno in annotations:
+            if anno["image_id"] != img_id:
+                continue
+
+            num_bbox += 1
+
+            class_id = anno["category_id"]
+            class_name = categories_dict[class_id]
+            class_color = color[class_id - 1]
+
+            x, y, w, h = list(map(int, anno["bbox"]))
+            cv2.rectangle(
+                image, (int(x), int(y)), (int(x + w), int(y + h)), class_color, 2
+            )
+
+            txt_size = cv2.getTextSize(
+                class_name, cv2.FONT_HERSHEY_SIMPLEX, self.font_size, 1
+            )[0]
+            cv2.rectangle(
+                image,
+                (x, y + 1),
+                (x + txt_size[0] + 5, y - int(1.5 * txt_size[1])),
+                class_color,
+                -1,
+            )
+            cv2.putText(
+                image,
+                class_name,
+                (x + 5, y - 5),
+                cv2.FONT_HERSHEY_SIMPLEX,
+                self.font_size,
+                (255, 255, 255),
+                1,
+            )
+
+        print("The unm_bbox of the display image is:", num_bbox)
+
+        cur_os = platform.system()
+        if cur_os == "Windows":
+            cv2.namedWindow(img_name, 0)
+            cv2.resizeWindow(img_name, 1000, 1000)
+            cv2.imshow(img_name, image)
+            cv2.waitKey(0)
+        else:
+            save_path = f"vis_{Path(img_name).stem}.jpg"
+            cv2.imwrite(save_path, image)
+            print(f"The {save_path} has been saved the current director.")
+
+
+def main():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--img_id", type=int, default=1, help="visual which one")
+    parser.add_argument(
+        "--json_path",
+        type=str,
+        default="tests/test_files/COCO_dataset/annotations/instances_train2017.json",
+    )
+    parser.add_argument(
+        "--img_dir", type=str, default="tests/test_files/COCO_dataset/train2017"
+    )
+    args = parser.parse_args()
+
+    viser = VisCOCO()
+    viser(args.img_id, args.json_path, args.img_dir)
+
+
+if __name__ == "__main__":
+    main()

setup.py

@@ -50,7 +50,7 @@ def get_readme() -> str:
     name=MODULE_NAME,
     version=VERSION_NUM,
     platforms="Any",
-    description="A dataset format conversion tool for object detection and image segmentation, which supports mutual conversion between labelme, labelImg tools and YOLO, VOC, and COCO dataset formats.",
+    description="A tool for object detection and image segmentation dataset format conversion. Supports conversion between labelme tool annotated data, labelImg tool annotated data, YOLO, PubLayNet and COCO data set formats.",
     long_description=get_readme(),
     long_description_content_type="text/markdown",
     author="SWHL",
@@ -75,7 +75,7 @@ def get_readme() -> str:
     entry_points={
         "console_scripts": [
             f"coco_to_labelImg={MODULE_NAME}.coco_to_labelImg:main",
-            f"coco_visual={MODULE_NAME}.coco_visual:main",
+            f"vis_coco={MODULE_NAME}.vis_coco:main",
             f"darknet_to_coco={MODULE_NAME}.darknet_to_coco:main",
             f"labelImg_to_yolov5={MODULE_NAME}.labelImg_to_yolov5:main",
             f"yolov5_to_coco={MODULE_NAME}.yolov5_to_coco:main",