update app

app.py

@@ -0,0 +1,92 @@
+
+import os
+os.system("pip install tensorflow")
+os.system("pip install modelscope")
+os.system("pip install thop")
+os.system("pip install easydict ")
+
+import gradio as gr
+import PIL.Image as Image
+import torch
+from modelscope.pipelines import pipeline
+from modelscope.utils.constant import Tasks
+import cv2
+import numpy as np
+import random
+
+import warnings
+
+warnings.filterwarnings("ignore")
+
+def object_detection(img_pil, confidence_threshold, device):
+    # 加载模型
+    p = pipeline(task='image-object-detection', model='damo/cv_tinynas_object-detection_damoyolo', device=device)
+
+    # 传入图片进行推理
+    result = p(img_pil)
+    # 读取图片
+    img_cv = cv2.cvtColor(np.asarray(img_pil), cv2.COLOR_RGB2BGR)
+    # 获取bbox和类别
+    scores = result['scores']
+    boxes = result['boxes']
+    labels = result['labels']
+    # 遍历每个bbox
+    for i in range(len(scores)):
+        # 只绘制置信度大于设定阈值的bbox
+        if scores[i] > confidence_threshold:
+            # 随机生成颜色
+            class_color = (random.randint(0, 255), random.randint(0, 255), random.randint(0, 255))
+            # 获取bbox坐标
+            x1, y1, x2, y2 = boxes[i]
+            x1, y1, x2, y2 = int(x1), int(y1), int(x2), int(y2)
+            # 绘制bbox
+            cv2.rectangle(img_cv, (x1, y1), (x2, y2), class_color, thickness=2)
+            # 绘制类别标签
+            label = f"{labels[i]}: {scores[i]:.2f}"
+            cv2.putText(img_cv, label, (x1, y1 - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, class_color, thickness=2)
+    img_pil = Image.fromarray(cv2.cvtColor(img_cv, cv2.COLOR_BGR2RGB))
+    return img_pil
+
+
+def download_test_image():
+    # 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')
+
+
+if __name__ == '__main__':
+    download_test_image()
+    # 定义输入和输出
+    input_image = gr.inputs.Image(type='pil')
+    input_slide = gr.inputs.Slider(minimum=0, maximum=1, step=0.05, default=0.5, label="Confidence Threshold")
+    input_device = gr.inputs.Radio(["cpu", "cuda", "gpu"], default="cpu")
+    output_image = gr.outputs.Image(type='pil')
+
+    examples = [['bus.jpg', 0.45, "cpu"],
+                ['dogs.jpg', 0.45, "cpu"],
+                ['zidane.jpg', 0.45, "cpu"]]
+    title = "DAMO-YOLO web demo"
+    description = "<div align='center'><img src='https://raw.githubusercontent.com/tinyvision/DAMO-YOLO/master/assets/logo.png' width='800''/><div>" \
+                  "<p style='text-align: center'><a href='https://github.com/tinyvision/DAMO-YOLO'>DAMO-YOLO</a> DAMO-YOLO DAMO-YOLO DAMO-YOLO：一种快速准确的目标检测方法，采用了一些新技术，包括 NAS 主干、高效的 RepGFPN、ZeroHead、AlignedOTA 和蒸馏增强。" \
+                  "DAMO-YOLO: a fast and accurate object detection method with some new techs, including NAS backbones, efficient RepGFPN, ZeroHead, AlignedOTA, and distillation enhancement..</p>"
+    article = "<p style='text-align: center'><a href='https://github.com/tinyvision/DAMO-YOLO'>DAMO-YOLO</a></p>" \
+              "<p style='text-align: center'><a href='https://github.com/isLinXu'>gradio build by gatilin</a></a></p>"
+
+    # 创建 Gradio 接口并运行
+    gr.Interface(
+        fn=object_detection,
+        inputs=[
+            input_image, input_slide, input_device
+        ],
+        outputs=output_image,
+        title=title,
+        examples=examples,
+        description=description, article=article
+    ).launch()

requirements.txt

@@ -0,0 +1,19 @@
+git+https://github.com/facebookresearch/detectron2.git@v0.6#egg=detectron2
+ultralytics~=8.0.169
+wget~=3.2
+opencv-python~=4.6.0.66
+numpy~=1.23.0
+pillow~=9.4.0
+gradio~=3.42.0
+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