add detr-resnet-50

detr-resnet-50/Dockerfile

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+FROM python:3.7.4-slim
+
+WORKDIR /app
+
+COPY requirements.txt /app
+
+RUN pip config set global.index-url https://pypi.mirrors.ustc.edu.cn/simple/ 
+
+RUN pip3 install --trusted-host pypi.python.org -r requirements.txt 
+
+COPY . /app
+
+CMD ["python", "detr_object_detection.py"]

detr-resnet-50/detr_object_detection.py

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+#目标检测
+import io
+import gradio as gr
+import matplotlib.pyplot as plt
+import torch
+from PIL import Image
+from transformers import AutoFeatureExtractor, DetrForObjectDetection
+
+
+def make_prediction(img, feature_extractor, model):
+    inputs = feature_extractor(img, return_tensors="pt")
+    outputs = model(**inputs)
+    img_size = torch.tensor([tuple(reversed(img.size))])
+    processed_outputs = feature_extractor.post_process(outputs, img_size)
+    return processed_outputs[0]
+
+def detect_objects(image_input):
+    #Extract model and feature extractor
+    feature_extractor = AutoFeatureExtractor.from_pretrained("facebook/detr-resnet-50")
+    model = DetrForObjectDetection.from_pretrained("facebook/detr-resnet-50")
+    
+    # if image comes from upload
+    if image_input:
+        image = image_input
+    
+    #Make prediction
+    processed_outputs = make_prediction(image, feature_extractor, model)
+    
+    #Visualize prediction
+    viz_img = visualize_prediction(image, processed_outputs, 0.7, model.config.id2label)
+    
+    return viz_img   
+
+# visualization
+COLORS = [
+    [0.000, 0.447, 0.741],
+    [0.850, 0.325, 0.098],
+    [0.929, 0.694, 0.125],
+    [0.494, 0.184, 0.556],
+    [0.466, 0.674, 0.188],
+    [0.301, 0.745, 0.933]
+]
+
+# Draw the bounding boxes on image.
+def fig2img(fig):
+    buf = io.BytesIO()
+    fig.savefig(buf)
+    buf.seek(0)
+    img = Image.open(buf)
+    return img
+
+# Draw the bounding boxes.
+def visualize_prediction(pil_img, output_dict, threshold=0.7, id2label=None):
+    keep = output_dict["scores"] > threshold
+    boxes = output_dict["boxes"][keep].tolist()
+    scores = output_dict["scores"][keep].tolist()
+    labels = output_dict["labels"][keep].tolist()
+    if id2label is not None:
+        labels = [id2label[x] for x in labels]
+
+    plt.figure(figsize=(16, 10))
+    plt.imshow(pil_img)
+    ax = plt.gca()
+    colors = COLORS * 100
+    for score, (xmin, ymin, xmax, ymax), label, color in zip(scores, boxes, labels, colors):
+        ax.add_patch(plt.Rectangle((xmin, ymin), xmax - xmin, ymax - ymin, fill=False, color=color, linewidth=3))
+        ax.text(xmin, ymin, f"{label}: {score:0.2f}", fontsize=15, bbox=dict(facecolor="yellow", alpha=0.5))
+    plt.axis("off")
+    return fig2img(plt.gcf())
+        
+
+demo = gr.Interface(detect_objects,
+                    inputs = gr.Image(type = 'pil'),
+                    outputs = gr.Image(shape = (650,650)),
+                    title = "目标检测",
+                    allow_flagging="never",
+                    examples = ['1.jpg', '2.jpg', '3.jpg'])
+
+if __name__ == '__main__':
+    demo.queue().launch(server_name="0.0.0.0", server_port=7002, max_threads=40)

detr-resnet-50/requirements.txt

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+gradio
+huggingface_hub
+torch
+transformers
+timm
+matplotlib
+pillow
+requests
+