[Feature]Add SAM Application

modelcenter/SAM/APP/app.py

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+import gradio as gr
+import numpy as np
+import cv2
+
+import utils
+from predict import build_predictor
+
+ID_PHOTO_IMAGE_DEMO = "./images/cityscapes_demo.png"
+
+generator = build_predictor()
+
+
+def clear_image_all():
+    utils.delete_result()
+    return None, None, None, None
+
+
+def get_id_photo_output(img):
+    """
+    Get the special size and background photo.
+
+    Args:
+        img(numpy:ndarray): The image array.
+        size(str): The size user specified.
+        bg(str): The background color user specified.
+        download_size(str): The size for image saving.
+
+    """
+    predictor = generator
+    masks = predictor.generate(img)
+    pred_result, pseudo_map = utils.masks2pseudomap(masks)  # PIL Image
+    added_pseudo_map = utils.visualize(
+        img, pred_result, color_map=utils.get_color_map_list(256))
+    res_download = utils.download(pseudo_map)
+
+    return pseudo_map, added_pseudo_map, res_download
+
+
+with gr.Blocks() as demo:
+    gr.Markdown("""# Segment Anything (PaddleSeg) """)
+    with gr.Tab("InputImage"):
+        image_in = gr.Image(value=ID_PHOTO_IMAGE_DEMO, label="Input image")
+
+        with gr.Row():
+            image_clear_btn = gr.Button("Clear")
+            image_submit_btn = gr.Button("Submit")
+
+        with gr.Row():
+            img_out1 = gr.Image(
+                label="Output image", interactive=False).style(height=300)
+            img_out2 = gr.Image(
+                label="Output image with mask",
+                interactive=False).style(height=300)
+        downloaded_img = gr.File(label='Image download').style(height=50)
+
+    image_clear_btn.click(
+        fn=clear_image_all,
+        inputs=None,
+        outputs=[image_in, img_out1, img_out2, downloaded_img])
+
+    image_submit_btn.click(
+        fn=get_id_photo_output,
+        inputs=[image_in, ],
+        outputs=[img_out1, img_out2, downloaded_img])
+
+    gr.Markdown(
+        """<font color=Gray>Tips: You can try segment the default image OR upload any images you want to segment by click on the clear button first.</font>"""
+    )
+
+    gr.Markdown(
+        """<font color=Gray>This is Segment Anything build with PaddlePaddle. 
+        We refer to the [SAM](https://github.com/facebookresearch/segment-anything) for code strucure and model architecture.
+        If you have any question or feature request, welcome to raise issues on [GitHub](https://github.com/PaddlePaddle/PaddleSeg/issues). </font>"""
+    )
+
+    gr.Button.style(1)
+
+demo.launch(server_name="0.0.0.0", server_port=8021, share=True)

modelcenter/SAM/APP/app.yml

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+【Segment-Anything-App-YAML】
+
+APP_Info:
+    title: Segment-Anything-App
+    colorFrom: blue
+    colorTo: yellow
+    sdk: gradio
+    sdk_version: 3.4.1
+    app_file: app.py
+    license: apache-2.0
+    device: cpu

modelcenter/SAM/APP/download.py

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+import os
+import sys
+import time
+
+import requests
+import zipfile
+
+FLUSH_INTERVAL = 0.1
+lasttime = time.time()
+
+
+def progress(str, end=False):
+    global lasttime
+    if end:
+        str += "\n"
+        lasttime = 0
+    if time.time() - lasttime >= FLUSH_INTERVAL:
+        sys.stdout.write("\r%s" % str)
+        lasttime = time.time()
+        sys.stdout.flush()
+
+
+def download_file(url, savepath, print_progress=True):
+    if print_progress:
+        print("Connecting to {}".format(url))
+    r = requests.get(url, stream=True, timeout=15)
+    total_length = r.headers.get('content-length')
+
+    if total_length is None:
+        with open(savepath, 'wb') as f:
+            shutil.copyfileobj(r.raw, f)
+    else:
+        with open(savepath, 'wb') as f:
+            dl = 0
+            total_length = int(total_length)
+            starttime = time.time()
+            if print_progress:
+                print("Downloading %s" % os.path.basename(savepath))
+            for data in r.iter_content(chunk_size=4096):
+                dl += len(data)
+                f.write(data)
+                if print_progress:
+                    done = int(50 * dl / total_length)
+                    progress("[%-50s] %.2f%%" %
+                             ('=' * done, float(100 * dl) / total_length))
+        if print_progress:
+            progress("[%-50s] %.2f%%" % ('=' * 50, 100), end=True)
+
+
+def uncompress(path):
+    files = zipfile.ZipFile(path, 'r')
+    filelist = files.namelist()
+    rootpath = filelist[0]
+    for file in filelist:
+        files.extract(file, './')

modelcenter/SAM/APP/predict.py

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+import paddle
+from segment_anything import SamAutomaticMaskGenerator, sam_model_registry
+
+model_link = {
+    'vit_h':
+    "https://bj.bcebos.com/paddleseg/dygraph/paddlesegAnything/vit_h/model.pdparams",
+    'vit_l':
+    "https://bj.bcebos.com/paddleseg/dygraph/paddlesegAnything/vit_l/model.pdparams",
+    'vit_b':
+    "https://bj.bcebos.com/paddleseg/dygraph/paddlesegAnything/vit_b/model.pdparams"
+}
+
+
+def build_predictor():
+    print("Loading model...")
+
+    if paddle.is_compiled_with_cuda():
+        paddle.set_device("gpu")
+    else:
+        paddle.set_device("cpu")
+
+    sam = sam_model_registry["vit_b"](checkpoint=model_link["vit_b"])
+    generator = SamAutomaticMaskGenerator(sam, output_mode="binary_mask")
+
+    return generator

modelcenter/SAM/APP/requirement.txt

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+gradio
+paddlepaddle
+opencv-python
+pyyaml >= 5.1
+PIL
+numpy
+time

modelcenter/SAM/APP/segment_anything/__init__.py

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+# Copyright (c) 2023 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# This implementation refers to: https://github.com/facebookresearch/segment-anything
+
+from .build_sam import (
+    build_sam,
+    build_sam_vit_h,
+    build_sam_vit_l,
+    build_sam_vit_b,
+    sam_model_registry, )
+from .predictor import SamPredictor
+from .automatic_mask_generator import SamAutomaticMaskGenerator