init space

.gitignore

@@ -0,0 +1 @@
+.idea

README.md

@@ -11,4 +11,15 @@ license: mit
 short_description: yolo human parser
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+## Yolo human parser
+This repository is the result of a deep investigation across multiple sources.
+Opaque or redundant code has been removed to make the implementation easier to study and extend.
+## Acknowledgments
+This work integrates code and concepts from several repositories.  
+For proper attribution, please refer to the following sources (or notify us if any are missing):
+- [Yolo](https://github.com/ultralytics/ultralytics)
+- [MnLgt](https://huggingface.co/MnLgt/yolo-human-parse)
+- [Self Space](https://huggingface.co/spaces/leonelhs/human-parser)
+## Contact
+For questions, comments, or feedback, please contact:  
+📧 **leonelhs@gmail.com**

app.py

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+#######################################################################################
+#
+# MIT License
+#
+# Copyright (c) [2025] [leonelhs@gmail.com]
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy
+# of this software and associated documentation files (the "Software"), to deal
+# in the Software without restriction, including without limitation the rights
+# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+# copies of the Software, and to permit persons to whom the Software is
+# furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all
+# copies or substantial portions of the Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+#
+#######################################################################################
+#
+# This project is one of several repositories exploring image segmentation techniques.
+# All related projects and interactive demos can be found at:
+# https://huggingface.co/spaces/leonelhs/removators
+# huggingface: https://huggingface.co/spaces/leonelhs/human-parser
+#
+from itertools import islice
+
+import gradio as gr
+import numpy as np
+from PIL import Image
+from ultralytics import YOLO
+from huggingface_hub import hf_hub_download
+
+# model = YOLO("yolo11x-seg.pt")  # only to test official model
+
+REPO_ID = "MnLgt/yolo-human-parse"
+model_path = hf_hub_download(repo_id=REPO_ID, filename="yolo-human-parse-epoch-125.pt")
+model = YOLO(model_path)
+
+# use for show bounding boxes
+def predict_box(image):
+    sections = []
+    results = model(image)[0]  # predict on an image
+    for result in results.boxes:
+        box = np.asarray(result.xyxy)[0]
+        label = results.names[int(result.cls)]
+        sections.append(((int(box[0]), int(box[1]), int(box[2]), int(box[3])), label))
+    return image, sections
+
+
+def predict(image):
+    sections = []
+    results = model(image)[0]  # predict on an image
+    for box, mask in zip(results.boxes, results.masks):
+        label = results.names[int(box.cls)]
+        data = np.asarray(mask.data)
+        sections.append((data, label))
+
+    width = results.masks.shape[1]
+    height = results.masks.shape[2]
+    image = image.resize((height, width), Image.Resampling.BILINEAR)
+    return image, sections
+
+with gr.Blocks(title="Yolo human parser") as app:
+    navbar = gr.Navbar(visible=True, main_page_name="Workspace")
+    gr.Markdown("## Yolo human parser")
+    with gr.Row():
+        with gr.Column(scale=1):
+            inp = gr.Image(type="pil", label="Upload Image")
+            btn_predict = gr.Button("Segment")
+        with gr.Column(scale=2):
+            out = gr.AnnotatedImage(label="Segments annotated")
+
+    btn_predict.click(predict, inputs=[inp], outputs=[out])
+
+with app.route("Readme", "/readme"):
+    with open("README.md") as f:
+        for line in islice(f, 12, None):
+            gr.Markdown(line.strip())
+
+app.launch(share=False, debug=True, show_error=True, mcp_server=True, pwa=True)
+app.queue()

requirements.txt

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+torch>=2.8.0
+ultralytics>=8.3.203
+pillow~=11.0.0