add detr-resnet-101-panoptic

detr-resnet-101-panoptic/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 apt-get update && \
+    apt-get upgrade -y && \
+    apt-get install -y git
+
+RUN apt-get install -y tk
+
+RUN python -m pip install git+https://github.com/cocodataset/panopticapi.git
+
+RUN pip3 install --trusted-host pypi.python.org -r requirements.txt 
+
+COPY . /app
+
+CMD ["python", "detr_panoptic.py"]

detr-resnet-101-panoptic/detr_panoptic.py

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+#全景分割
+from PIL import Image
+import io
+import matplotlib.pyplot as plt
+import torch
+import torchvision.transforms as T
+import numpy
+import gradio as gr
+import itertools
+import seaborn as sns
+from panopticapi.utils import rgb2id
+
+
+# These are the COCO classes
+CLASSES = [
+    'N/A', 'person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus',
+    'train', 'truck', 'boat', 'traffic light', 'fire hydrant', 'N/A',
+    'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse',
+    'sheep', 'cow', 'elephant', 'bear', 'zebra', 'giraffe', 'N/A', 'backpack',
+    'umbrella', 'N/A', 'N/A', 'handbag', 'tie', 'suitcase', 'frisbee', 'skis',
+    'snowboard', 'sports ball', 'kite', 'baseball bat', 'baseball glove',
+    'skateboard', 'surfboard', 'tennis racket', 'bottle', 'N/A', 'wine glass',
+    'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple', 'sandwich',
+    'orange', 'broccoli', 'carrot', 'hot dog', 'pizza', 'donut', 'cake',
+    'chair', 'couch', 'potted plant', 'bed', 'N/A', 'dining table', 'N/A',
+    'N/A', 'toilet', 'N/A', 'tv', 'laptop', 'mouse', 'remote', 'keyboard',
+    'cell phone', 'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'N/A',
+    'book', 'clock', 'vase', 'scissors', 'teddy bear', 'hair drier',
+    'toothbrush'
+]
+
+# Detectron2 uses a different numbering scheme, we build a conversion table
+coco2d2 = {}
+count = 0
+for i, c in enumerate(CLASSES):
+  if c != "N/A":
+    coco2d2[i] = count
+    count+=1
+
+
+# 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(result):
+    palette = itertools.cycle(sns.color_palette())
+
+    # The segmentation is stored in a special-format png
+    panoptic_seg = Image.open(io.BytesIO(result['png_string']))
+    panoptic_seg = numpy.array(panoptic_seg, dtype=numpy.uint8).copy()
+    # We retrieve the ids corresponding to each mask
+    panoptic_seg_id = rgb2id(panoptic_seg)
+
+    # Finally we color each mask individually
+    panoptic_seg[:, :, :] = 0
+    for id in range(panoptic_seg_id.max() + 1):
+        panoptic_seg[panoptic_seg_id == id] = numpy.asarray(next(palette)) * 255
+    plt.figure(figsize=(15,15))
+    plt.imshow(panoptic_seg)
+    plt.axis('off')
+    return fig2img(plt.gcf())
+
+model, postprocessor = torch.hub.load('facebookresearch/detr', 
+    'detr_resnet101_panoptic', 
+    pretrained=True, 
+    return_postprocessor=True, 
+    num_classes=250)
+
+transform = T.Compose([
+    T.Resize(800),
+    T.ToTensor(),
+    T.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
+])
+model.eval();
+
+
+def detect_objects(image_input):
+    model.eval();
+
+    # if image comes from upload
+    if image_input:
+        image = image_input
+
+    # mean-std normalize the input image (batch-size: 1)
+    img = transform(image).unsqueeze(0)
+    out = model(img)
+
+    # the post-processor expects as input the target size of the predictions (which we set here to the image size)
+    result = postprocessor(out, torch.as_tensor(img.shape[-2:]).unsqueeze(0))[0]
+    
+    #Visualize prediction
+    viz_img = visualize_prediction(result)
+    
+    return viz_img
+        
+    
+demo = gr.Interface(detect_objects, 
+                    inputs = gr.Image(type='pil'), 
+                    outputs = gr.Image(shape=(650,650)),
+                    title = "全景分割",
+                    allow_flagging="never",
+                    examples = ['1.jpg', '2.jpg'])
+
+
+if __name__ == '__main__':
+    demo.queue().launch(server_name = "0.0.0.0", server_port = 7003, max_threads=40)

detr-resnet-101-panoptic/requirements.txt

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+gradio
+huggingface
+torch
+transformers
+seaborn
+matplotlib
+pillow
+requests
+torchvision
+numpy
+scipy