First push

app.py

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+import gradio as gr
+import numpy as np
+from PIL import Image
+from ultralytics import YOLO
+import cv2
+
+# Model configurations
+MODEL_CONFIGS = {
+    "Dry Season Form": {
+        "path": r"models\DSF_Mleda_250.pt",
+        "labels": [
+            "F1", "F2", "F3", "F4", "F5", "F6", "F7", "F8", "F9", "F10", "F11", "F12", "F13", "F14", "F15", "F16",
+            "H1", "H2", "H3", "H4", "H5", "H6", "H7", "H8", "H9", "H10", "H11", "H12", "H13", "H14", "H15", "H16", "H17",
+            "fs1", "fs2", "fs3", "fs4", "fs5",
+            "hs1", "hs2", "hs3", "hs4", "hs5", "hs6",
+            "sc1", "sc2",
+            "sex", "right", "left", "grey", "black", "white"
+        ],
+        "imgsz": 1280
+    },
+    "Wet Season Form": {
+        "path": r"models\WSF_Mleda_200.pt",
+        "labels": [
+            'F12', 'F14', 'fs1', 'fs2', 'fs3', 'fs4', 'fs5',
+            'H12', 'H14', 'hs1', 'hs2', 'hs3', 'hs4', 'hs5', 'hs6',
+            'white', 'black', 'grey', 'sex', 'blue', 'green', 'red', 'sc1', 'sc2'
+        ],
+        "imgsz": 1280
+    },
+    "All Season Form": {
+        "path": r"models\DSF_WSF_Mleda_450.pt",
+        "labels": [
+            "F1", "F2", "F3", "F4", "F5", "F6", "F7", "F8", "F9", "F10", "F11", "F12", "F13", "F14", "F15", "F16",
+            "H1", "H2", "H3", "H4", "H5", "H6", "H7", "H8", "H9", "H10", "H11", "H12", "H13", "H14", "H15", "H16", "H17",
+            "fs1", "fs2", "fs3", "fs4", "fs5",
+            "hs1", "hs2", "hs3", "hs4", "hs5", "hs6",
+            "sc1", "sc2",
+            "sex", "right", "left", "grey", "black", "white"
+        ],
+        "imgsz": 1280
+    }
+}
+
+def hex_to_bgr(hex_color: str) -> tuple:
+    """Convert #RRGGBB hex color to BGR tuple."""
+    hex_color = hex_color.lstrip("#")
+    if len(hex_color) != 6:
+        return (0, 255, 0)  # Default to green if invalid
+    r = int(hex_color[0:2], 16)
+    g = int(hex_color[2:4], 16)
+    b = int(hex_color[4:6], 16)
+    return (b, g, r)
+
+def load_model(path):
+    """Load YOLO model from the given path."""
+    return YOLO(path)
+
+def draw_detections(image: np.ndarray, results, labels, keypoint_threshold: float, show_labels: bool, point_size: int, point_color: str, label_size: float) -> np.ndarray:
+    img = image.copy()
+    color_bgr = hex_to_bgr(point_color)
+
+    for result in results:
+        boxes = result.boxes.xywh.cpu().numpy()
+        cls_ids = result.boxes.cls.int().cpu().numpy()
+        confs = result.boxes.conf.cpu().numpy()
+        kpts_all = result.keypoints.data.cpu().numpy()
+
+        for (x_c, y_c, w, h), cls_id, conf, kpts in zip(boxes, cls_ids, confs, kpts_all):
+            x1 = int(x_c - w/2); y1 = int(y_c - h/2)
+            x2 = int(x_c + w/2); y2 = int(y_c + h/2)
+
+            cv2.rectangle(img, (x1, y1), (x2, y2), color=(255,255,0), thickness=2)
+
+            class_name = result.names[int(cls_id)]
+            text = f"{class_name} {conf:.2f}"
+            (tw, th), _ = cv2.getTextSize(text, cv2.FONT_HERSHEY_SIMPLEX, 0.6, 1)
+            cv2.rectangle(img, (x1, y1 - th - 4), (x1 + tw, y1), (255,255,0), cv2.FILLED)
+            cv2.putText(img, text, (x1, y1 - 4), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0,0,0), 1, cv2.LINE_AA)
+
+            for i, (x, y, v) in enumerate(kpts):
+                if v > keypoint_threshold and i < len(labels):
+                    xi, yi = int(x), int(y)
+                    cv2.circle(img, (xi, yi), int(point_size), color_bgr, -1, cv2.LINE_AA)
+                    if show_labels:
+                        cv2.putText(img, labels[i], (xi + 3, yi + 3), cv2.FONT_HERSHEY_SIMPLEX, label_size, (255,0,0), 2, cv2.LINE_AA)
+    return img
+
+
+def predict_and_annotate(input_image: Image.Image, conf_threshold: float, keypoint_threshold: float, model_choice: str, show_labels: bool, point_size: int, point_color: str, label_size: float):
+    config = MODEL_CONFIGS[model_choice]
+    model = load_model(config["path"])
+    labels = config["labels"]
+    imgsz = config["imgsz"]
+
+    img_rgb = np.array(input_image.convert("RGB"))
+    img_bgr = cv2.cvtColor(img_rgb, cv2.COLOR_RGB2BGR)
+
+    results = model(img_bgr, conf=conf_threshold, imgsz=imgsz)
+    annotated_bgr = draw_detections(img_bgr, results, labels, keypoint_threshold, show_labels, point_size, point_color, label_size)
+    annotated_rgb = cv2.cvtColor(annotated_bgr, cv2.COLOR_BGR2RGB)
+    return Image.fromarray(annotated_rgb)
+
+
+# Gradio Interface
+app = gr.Interface(
+    fn=predict_and_annotate,
+    inputs=[
+        gr.Image(type="pil", label="Upload Image"),
+        gr.Slider(minimum=0.0, maximum=1.0, value=0.25, step=0.01, label="Confidence Threshold"),
+        gr.Slider(minimum=0.0, maximum=1.0, value=0.5, step=0.01, label="Keypoint Visibility Threshold"),
+        gr.Radio(choices=list(MODEL_CONFIGS.keys()), label="Select Model", value="Dry Season Form"),
+        gr.Checkbox(label="Show Keypoint Labels", value=True),
+        gr.Slider(minimum=1, maximum=20, value=8, step=0.1, label="Keypoint Size"),
+        gr.ColorPicker(label="Keypoint Color", value="#00FF00"),
+        gr.Slider(minimum=0.3, maximum=3.0, value=1.0, step=0.1, label="Keypoint Label Font Size")
+    ],
+    outputs=gr.Image(type="pil", label="Detection Result", format="png"),
+    title="🦋 Melanitis leda Landmark Identification",
+    description="Upload an image and select the model. Customize detection and keypoint display settings.",
+    flagging_mode="never"
+)
+
+
+if __name__ == "__main__":
+    app.launch()

models/DSF_Mleda_250.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:5b51ea730cb1c03399bc7d1de9fe51ca6f531af0d29c3064de9638a4a8ec701c
+size 56864045

models/DSF_WSF_Mleda_450.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:59cd20c486514ff18080413657d784f76c95937db5d5f6fa8cfae1f95b24951a
+size 56674033

models/WSF_Mleda_200.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:33b8298a9121ae3ccf5b5d5aff000d6dddda6f6e1c3e35a09f1215b8f53dd47c
+size 53621489

requirements.txt

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+ultralytics==8.3.88
+torch==2.6.0
+torchvision==0.21.0
+pycocotools==2.0.7
+PyYAML==6.0.1
+scipy==1.15.2
+gradio==5.29.0
+numpy==1.26.4
+opencv-python==4.9.0.80
+psutil==5.9.8
+py-cpuinfo==9.0.0
+safetensors==0.4.3
+pillow==11.1.0
+opencv-python==4.11.0.86
+opencv-python-headless==4.7.0.72