update files

app.py

@@ -0,0 +1,103 @@
+
+import os
+os.system("pip install super-gradients~=3.2.0")
+
+import cv2
+import numpy as np
+import gradio as gr
+
+from super_gradients.training import models
+import warnings
+
+warnings.filterwarnings("ignore")
+class YOLOX_WebUI:
+    def __init__(self):
+        self.download_test_img()
+
+    def download_test_img(self):
+        # Images
+        torch.hub.download_url_to_file(
+            'https://user-images.githubusercontent.com/59380685/266264420-21575a83-4057-41cf-8a4a-b3ea6f332d79.jpg',
+            'bus.jpg')
+        torch.hub.download_url_to_file(
+            'https://user-images.githubusercontent.com/59380685/266264536-82afdf58-6b9a-4568-b9df-551ee72cb6d9.jpg',
+            'dogs.jpg')
+        torch.hub.download_url_to_file(
+            'https://user-images.githubusercontent.com/59380685/266264600-9d0c26ca-8ba6-45f2-b53b-4dc98460c43e.jpg',
+            'zidane.jpg')
+
+    def predict(self, image_path,conf, iou, line_width, device, model_type, model_path):
+        self.device = device
+        if model_type == "yolox_n":
+            self.model = models.get("yolox_n", pretrained_weights="coco").to(self.device)
+        elif model_type == "yolox_s":
+            self.model = models.get("yolox_s", pretrained_weights="coco").to(self.device)
+        elif model_type == "yolox_m":
+            self.model = models.get("yolox_m", pretrained_weights="coco").to(self.device)
+        elif model_type == "yolox_t":
+            self.model = models.get("yolox_t", pretrained_weights="coco").to(self.device)
+        elif model_type == "yolox_l":
+            self.model = models.get("yolox_l", pretrained_weights="coco").to(self.device)
+        else:
+            self.model = models.get(model_path, pretrained_weights="coco").to(self.device)
+        if model_type not in ["yolox_n", "yolox_s", "yolox_m", "yolox_l","yolox_t"]:
+            self.model = models.get(model_path, pretrained_weights="coco").to(self.device)
+
+        results = self.model.predict(image_path)
+
+        # get image data and bbox information
+        image = results._images_prediction_lst[0].image
+        class_names = results._images_prediction_lst[0].class_names
+        prediction = results._images_prediction_lst[0].prediction
+        bboxes_xyxy = prediction.bboxes_xyxy
+        labels = prediction.labels
+        confidences = prediction.confidence
+        # draw rectangles and label names
+        for bbox, label, confidence in zip(bboxes_xyxy, labels, confidences):
+            color = tuple(np.random.randint(0, 255, 3).tolist())
+            x1, y1, x2, y2 = bbox.astype(int)
+            if confidence > conf:
+                cv2.rectangle(image, (x1, y1), (x2, y2), color, 2)
+                cla_name = class_names[int(label)]
+                label_name = f"{cla_name}: {confidence:.2f}"
+                cv2.putText(image, label_name, (x1, y1 - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 2)
+
+        return image
+
+if __name__ == '__main__':
+    # Instantiate YOLO_NAS_WebUI class
+    detector = YOLOX_WebUI()
+    examples = [
+        ['bus.jpg', 0.25, 0.45, 2, "cpu", "yolox_n", "yolox_n.pt"],
+        ['dogs.jpg', 0.25, 0.45, 2, "cpu", "yolox_n", "yolox_n.pt"],
+        ['zidane.jpg', 0.25, 0.45, 2, "cpu", "yolox_n", "yolox_n.pt"]
+    ]
+    # Define Gradio interface
+    iface = gr.Interface(
+        fn=detector.predict,
+        inputs=["image",
+                gr.inputs.Slider(minimum=0, maximum=1, step=0.01, default=0.25,
+                                 label="Confidence Threshold"),
+                gr.inputs.Slider(minimum=0, maximum=1, step=0.01, default=0.45,
+                                 label="IoU Threshold"),
+                gr.inputs.Number(default=2, label="Line Width"),
+                gr.inputs.Radio(["cpu", "cuda"], label="Device", default="cpu"),
+                gr.inputs.Radio(["yolox_n", "yolox_s", "yolox_m", "yolox_l","yolox_t"],
+                                label="Model Type", default="yolo_nas_s"),
+                gr.inputs.Textbox(default="yolox_n.pt", label="Model Path")],
+        outputs="image",
+        title="YOLOX_WebUI Object Detector",
+        description="Detect objects in an image using YOLOX model.",
+        theme="default", examples=examples,
+        layout="vertical",
+        allow_flagging=False,
+        analytics_enabled=True,
+        server_port=None,
+        server_name=None,
+        server_protocol=None,
+    )
+
+    # Run Gradio interface
+    iface.launch(share=True)
+
+

requirements.txt

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+wget~=3.2
+opencv-python~=4.6.0.66
+numpy~=1.23.0
+torch~=1.13.1
+torchvision~=0.14.1
+pillow~=9.4.0
+gradio~=3.42.0
+ultralytics~=8.0.169
+pyyaml~=6.0
+wandb~=0.13.11
+tqdm~=4.65.0
+matplotlib~=3.7.1
+pandas~=2.0.0
+seaborn~=0.12.2
+requests~=2.31.0
+psutil~=5.9.4
+thop~=0.1.1-2209072238
+timm~=0.9.2
+super-gradients~=3.2.0
+openmim