upload space files

.gitattributes

@@ -32,3 +32,6 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+test_images/ss.png filter=lfs diff=lfs merge=lfs -text
+test_images/ss2.png filter=lfs diff=lfs merge=lfs -text
+test_images/ss3.png filter=lfs diff=lfs merge=lfs -text

README.md

@@ -1,8 +1,8 @@
 ---
 title: Valorant Object Detection
-emoji: ⚡
-colorFrom: green
-colorTo: red
+emoji: 🎮
+colorFrom: red
+colorTo: gray
 sdk: gradio
 sdk_version: 3.15.0
 app_file: app.py

app.py

@@ -0,0 +1,61 @@
+from pathlib import Path
+import os
+import json
+import gradio as gr
+import yolov5
+from PIL import Image
+from huggingface_hub import hf_hub_download
+
+app_title = "Valorant Object Detection"
+models_ids = ['keremberke/yolov5n-valorant', 'keremberke/yolov5s-valorant', 'keremberke/yolov5m-valorant']
+article = f"<p style='text-align: center'> <a href='https://huggingface.co/{models_ids[-1]}'>huggingface.co/{models_ids[-1]}</a> | <a href='https://huggingface.co/keremberke/valorant-object-detection'>huggingface.co/keremberke/valorant-object-detection</a> | <a href='https://github.com/keremberke/awesome-yolov5-models'>awesome-yolov5-models</a> </p>"
+
+current_model_id = models_ids[-1]
+model = yolov5.load(current_model_id)
+
+if space_content/test_images:
+    image_filenames = os.listdir(space_content/test_images)
+    examples = [
+        [Path(space_content/test_images) / image_filename, 0.25, models_ids[-1]]
+        for image_filename in image_filenames
+    ]
+else:
+    examples = None
+
+
+def predict(image, threshold=0.25, model_id=None):
+    # update model if required
+    global current_model_id
+    global model
+    if model_id != current_model_id:
+        model = yolov5.load(model_id)
+        current_model_id = model_id
+
+    # get model input size
+    config_path = hf_hub_download(repo_id=model_id, filename="config.json")
+    with open(config_path, "r") as f:
+        config = json.load(f)
+    input_size = config["input_size"]
+
+    # perform inference
+    model.conf = threshold
+    results = model(image, size=input_size)
+    numpy_image = results.render()[0]
+    output_image = Image.fromarray(numpy_image)
+    return output_image
+
+
+gr.Interface(
+    title=app_title,
+    description="Created by 'keremberke'",
+    article=article,
+    fn=predict,
+    inputs=[
+        gr.Image(type="pil"),
+        gr.Slider(maximum=1, step=0.01, value=0.25),
+        gr.Dropdown(models_ids, value=models_ids[-1]),
+    ],
+    outputs=gr.Image(type="pil"),
+    examples=examples,
+    cache_examples=True if examples else False,
+).launch(enable_queue=True)

requirements.txt

@@ -0,0 +1,5 @@
+
+yolov5==7.0.5
+gradio==3.15.0
+torch
+huggingface-hub