sync ms

.gitignore

@@ -1,7 +1,8 @@
-__pycache__/
+*__pycache__*
 _local/
 *.pyc
 local_models_*/
 rename.sh
 *.onnx
 simsun.ttc
+test.*

app.py

@@ -4,75 +4,85 @@ import numpy as np
 import gradio as gr
 from PIL import Image
 from insectid import InsectDetector, InsectIdentifier
-from utils import MODEL_DIR
+from utils import _L, MODEL_DIR, EN_US
 
 
 def infer(filename: str):
-    if not filename:
-        None, "Please upload a picture"
+    status = "Success"
+    result = outxt = None
+    try:
+        if not filename:
+            raise ValueError("请上传图片")
 
-    detector = InsectDetector()
-    identifier = InsectIdentifier()
-    image = khandy.imread(filename)
-    if image is None:
-        return None
+        detector = InsectDetector()
+        identifier = InsectIdentifier()
+        image = khandy.imread(filename)
+        if image is None:
+            raise ValueError("图片读取失败")
 
-    if max(image.shape[:2]) > 1280:
-        image = khandy.resize_image_long(image, 1280)
+        if max(image.shape[:2]) > 1280:
+            image = khandy.resize_image_long(image, 1280)
 
-    image_for_draw = image.copy()
-    image_height, image_width = image.shape[:2]
-    boxes, confs, classes = detector.detect(image)
-    text = "Unknown"
-    for box, _, _ in zip(boxes, confs, classes):
-        box = box.astype(np.int32)
-        box_width = box[2] - box[0] + 1
-        box_height = box[3] - box[1] + 1
-        if box_width < 30 or box_height < 30:
-            continue
+        image_for_draw = image.copy()
+        image_height, image_width = image.shape[:2]
+        boxes, confs, classes = detector.detect(image)
+        text = _L("未知")
+        for box, _, _ in zip(boxes, confs, classes):
+            box = box.astype(np.int32)
+            box_width = box[2] - box[0] + 1
+            box_height = box[3] - box[1] + 1
+            if box_width < 30 or box_height < 30:
+                continue
 
-        cropped = khandy.crop_or_pad(image, box[0], box[1], box[2], box[3])
-        results = identifier.identify(cropped)
-        print(results[0])
-        prob = results[0]["probability"]
-        if prob >= 0.10:
-            text = "{}: {:.2f}%".format(
-                results[0]["latin_name"],
-                100.0 * results[0]["probability"],
+            cropped = khandy.crop_or_pad(image, box[0], box[1], box[2], box[3])
+            results = identifier.identify(cropped)
+            print(results[0])
+            prob = results[0]["probability"]
+            if prob >= 0.10:
+                text = "{} {}: {:.2f}%".format(
+                    results[0]["chinese_name"],
+                    results[0]["latin_name"],
+                    100.0 * results[0]["probability"],
+                )
+
+            position = [box[0] + 2, box[1] - 20]
+            position[0] = min(max(position[0], 0), image_width)
+            position[1] = min(max(position[1], 0), image_height)
+            cv2.rectangle(
+                image_for_draw,
+                (box[0], box[1]),
+                (box[2], box[3]),
+                (0, 255, 0),
+                2,
+            )
+            image_for_draw = khandy.draw_text(
+                image_for_draw,
+                text,
+                position,
+                font=None if EN_US else f"{MODEL_DIR}/simsun.ttc",
+                font_size=15,
             )
 
-        position = [box[0] + 2, box[1] - 20]
-        position[0] = min(max(position[0], 0), image_width)
-        position[1] = min(max(position[1], 0), image_height)
-        cv2.rectangle(
-            image_for_draw,
-            (box[0], box[1]),
-            (box[2], box[3]),
-            (0, 255, 0),
-            2,
-        )
-        image_for_draw = khandy.draw_text(
-            image_for_draw,
-            text,
-            position,
-            font=f"{MODEL_DIR}/simsun.ttc",
-            font_size=15,
-        )
+        outxt = text.split(":")[0] if ":" in text else text
+        result = Image.fromarray(image_for_draw[:, :, ::-1], mode="RGB")
+
+    except Exception as e:
+        status = f"{e}"
 
-    outxt = text.split(":")[0] if ":" in text else text
-    return Image.fromarray(image_for_draw[:, :, ::-1], mode="RGB"), outxt
+    return status, result, outxt
 
 
 if __name__ == "__main__":
     with gr.Blocks() as demo:
         gr.Interface(
             fn=infer,
-            inputs=gr.Image(label="Upload insect picture", type="filepath"),
+            inputs=gr.Image(label=_L("上传昆虫照片"), type="filepath"),
             outputs=[
-                gr.Image(label="Recognition result", show_share_button=False),
-                gr.Textbox(label="Best match", show_copy_button=True),
+                gr.Textbox(label=_L("状态栏"), show_copy_button=True),
+                gr.Image(label=_L("识别结果"), show_share_button=False),
+                gr.Textbox(label=_L("最可能的物种"), show_copy_button=True),
             ],
-            title="Image file format support PNG, JPG, JPEG and BMP, and the file size does not exceed 10M.",
+            title=_L("图像文件格式支持 PNG, JPG, JPEG 和 BMP, 且文件大小不超过 10M"),
             examples=[
                 f"{MODEL_DIR}/examples/butterfly.jpg",
                 f"{MODEL_DIR}/examples/beetle.jpg",

requirements.txt

@@ -3,4 +3,5 @@ Pillow
 requests
 onnxruntime
 numpy>=1.11.1
-opencv-python>=4.5
+opencv-python>=4.5
+modelscope[framework]==1.21

utils.py

@@ -1,3 +1,30 @@
-from huggingface_hub import snapshot_download
+import os
+import modelscope
+import huggingface_hub
 
-MODEL_DIR = snapshot_download("Genius-Society/insecta", cache_dir="./__pycache__")
+EN_US = os.getenv("LANG") != "zh_CN.UTF-8"
+
+MODEL_DIR = (
+    huggingface_hub.snapshot_download(
+        "Genius-Society/insecta",
+        cache_dir="./__pycache__",
+    )
+    if EN_US
+    else modelscope.snapshot_download(
+        "Genius-Society/insecta",
+        cache_dir="./__pycache__",
+    )
+)
+
+ZH2EN = {
+    "上传昆虫照片": "Upload insect picture",
+    "状态栏": "Status",
+    "识别结果": "Recognition result",
+    "最可能的物种": "Best match",
+    "图像文件格式支持 PNG, JPG, JPEG 和 BMP, 且文件大小不超过 10M": "Image file format support PNG, JPG, JPEG and BMP, and the file size does not exceed 10M.",
+    "未知": "Unknown",
+}
+
+
+def _L(zh_txt: str):
+    return ZH2EN[zh_txt] if EN_US else zh_txt