refine the sam nodes

.github/workflows/segment-anything-ci.yml

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+name: workflows-ci
+
+on:
+  workflow_dispatch:
+    inputs:
+      environment:
+        description: 'The environment to deploy the workflow to'
+        required: true
+        default: 'uat'
+      branch:
+        description: 'The branch to checkout code from'
+        required: true
+        default: 'master'
+  pull_request:
+    branches:
+      - master
+
+concurrency:
+  group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.event.inputs.environment }}
+  cancel-in-progress: true
+
+env:
+  test_script: test_sam.py
+  target_branch: ${{ github.base_ref || 'master' }}
+
+jobs:
+  check-modified-files:
+    runs-on: ubuntu-latest
+    outputs:
+      only_non_code_files: ${{ steps.check_files.outputs.only_non_code_files }}
+    steps:
+    - name: Checkout repository
+      uses: actions/checkout@v4
+      with:
+        fetch-depth: 0
+        ref: ${{ github.head_ref || github.event.inputs.branch }}
+
+    - name: Fetch target branch
+      run: |
+        git fetch origin ${{ env.target_branch }} --depth=1
+
+    - name: Check modified files
+      id: check_files
+      run: |
+        modified_files=$(git diff --name-only origin/${{ env.target_branch }}...HEAD)
+        only_non_code_files=true
+        for file in $modified_files; do
+          if [[ "$file" == *.py ]] || [[ "$file" == *.js ]] || [[ "$file" == *.json ]] || [[ "$file" == *.yml ]]; then
+            only_non_code_files=false
+            break
+          fi
+        done
+        echo "only_non_code_files=$only_non_code_files" >> $GITHUB_OUTPUT
+
+    - name: Debug only_non_code_files
+      run: echo "only_non_code_files=${{ steps.check_files.outputs.only_non_code_files }}"
+
+  run-examples-workflows:
+    needs: check-modified-files
+    if: ${{ needs.check-modified-files.outputs.only_non_code_files == 'false' }} || ${{ github.event_name == 'workflow_dispatch' }}
+    runs-on: ${{ matrix.os }}
+    strategy:
+      matrix:
+        os: [windows-latest, ubuntu-latest]
+    environment: ${{ github.event.inputs.environment || 'prod' }}
+
+    steps:
+    - name: Show Base
+      run: |
+        echo "Current directory: $(pwd)"
+
+    - name: Checkout ComfyUI
+      uses: actions/checkout@v4
+      with:
+        repository: comfyanonymous/ComfyUI
+        path: ComfyUI
+        ref: v0.3.7
+
+    - name: Checkout plugin repository
+      uses: actions/checkout@v4
+      with:
+        path: ComfyUI/custom_nodes/BizyAir
+
+    - name: Set up Python
+      uses: actions/setup-python@v5
+      with:
+        python-version: '3.10'
+
+    - name: Cache pip packages
+      uses: actions/cache@v4
+      with:
+        path: ~/.cache/pip
+        key: ${{ runner.os }}-pip-${{ hashFiles('**/requirements.txt') }}
+        restore-keys: |
+          ${{ runner.os }}-pip-
+
+    - name: Install dependencies
+      run: |
+        python3 -m pip install --upgrade pip
+        python3 -m pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cpu
+        python3 -m pip install -r ComfyUI/requirements.txt
+        python3 -m pip install -r ComfyUI/custom_nodes/BizyAir/requirements.txt
+        python3 -m pip show torch
+
+    - name: Run ComfyUI on Linux
+      if: matrix.os == 'ubuntu-latest'
+      run: |
+        cd ComfyUI
+        nohup python main.py --port 8188 --cpu > >(tee -a service.log) 2>&1 &
+
+        cd custom_nodes/BizyAir
+        echo "Current directory: $(pwd)"
+        python3 tests/write_api_ini_file.py
+        python3 tests/${{ env.test_script }}
+      env:
+        BIZYAIR_KEY: ${{ secrets.BIZYAIR_KEY }}
+        BIZYAIR_API_KEY: ${{ secrets.BIZYAIR_KEY }}
+        BIZYAIR_SERVER_ADDRESS: ${{ vars.BIZYAIR_SERVER_ADDRESS }}
+        PYTHONPATH: ${{ github.workspace }}/ComfyUI
+        BIZYAIR_TEST_SKIP_WORKFLOW_IDS: ${{ vars.BIZYAIR_TEST_SKIP_WORKFLOW_IDS }}
+        BIZYAIR_OFFICIAL_WORKFLOW_MAX_TOTAL: ${{ vars.BIZYAIR_OFFICIAL_WORKFLOW_MAX_TOTAL }}
+
+    - name: Run ComfyUI on Windows
+      if: matrix.os == 'windows-latest'
+      run: |
+        cd ComfyUI
+        Start-Process -FilePath "python3" -ArgumentList "main.py --port 8188 --cpu" -NoNewWindow
+        cd custom_nodes/BizyAir
+        Write-Output "Current directory: $(Get-Location)"
+        python3 tests/write_api_ini_file.py
+        python3 tests/${{ env.test_script }}
+      env:
+        BIZYAIR_KEY: ${{ secrets.BIZYAIR_KEY }}
+        BIZYAIR_API_KEY: ${{ secrets.BIZYAIR_KEY }}
+        BIZYAIR_SERVER_ADDRESS: ${{ vars.BIZYAIR_SERVER_ADDRESS }}
+        PYTHONPATH: ${{ github.workspace }}/ComfyUI
+        PYTHONIOENCODING: utf-8
+        BIZYAIR_TEST_SKIP_WORKFLOW_IDS: ${{ vars.BIZYAIR_TEST_SKIP_WORKFLOW_IDS }}
+        BIZYAIR_OFFICIAL_WORKFLOW_MAX_TOTAL: ${{ vars.BIZYAIR_OFFICIAL_WORKFLOW_MAX_TOTAL }}

tests/test_sam.py

@@ -0,0 +1,210 @@
+import base64
+import io
+import json
+import os
+import sys
+from io import BytesIO
+
+import imageio.v2 as imageio
+import numpy as np
+import requests
+import torch
+from PIL import Image
+
+# current_dir = os.path.dirname(os.path.abspath(__file__))
+# full_path = os.path.abspath(os.path.join(current_dir, "../../.."))
+# sys.path.append(full_path)
+
+
+def convert_image_to_rgb(image: Image.Image) -> Image.Image:
+    if image.mode != "RGB":
+        return image.convert("RGB")
+    return image
+
+
+def encode_image_to_base64(
+    image: Image.Image, format: str = "WEBP", quality: int = 100, **kwargs
+) -> str:
+    image = convert_image_to_rgb(image)
+    with io.BytesIO() as output:
+        imageio.imwrite(output, image, format=format, quality=quality)
+        output.seek(0)
+        img_bytes = output.getvalue()
+    return base64.b64encode(img_bytes).decode("utf-8")
+
+
+def send_request(create_task_url, payload):
+    with TaskClient(create_task_url) as client:
+        response = client.pull(payload)
+
+        if response is None:
+            raise RuntimeError()
+        ret = response.json()
+
+        if "result" in ret:
+            msg = json.loads(ret["result"])
+        else:
+            msg = ret
+        # print("why msg: ", msg)
+        msg = msg["data"]
+        if msg["type"] not in (
+            "comfyair",
+            "bizyair",
+        ):
+            raise Exception(f"Unexpected response type: {msg}")
+
+        if "error" in msg:
+            raise Exception(f"Error happens: {msg}")
+
+        # img = msg["image"]
+        # mask_img = msg["mask_image"]
+
+        # output_file1 = "sam_test.webp"
+        # output_file2 = "sam_test_mask.webp"
+        # decode_base64_to_image(img, "webp").save(output_file1)
+        # decode_base64_to_image(mask_img, "webp").save(output_file2)
+
+
+class TaskClient:
+    def __init__(self, create_task_url):
+        self.create_task_url = create_task_url
+
+    def __enter__(self):
+        return self
+
+    def __exit__(self, exc_type, exc_value, traceback):
+        pass
+
+    def pull(self, payload):
+        response = requests.post(
+            self.create_task_url,
+            json=payload,
+            headers={"Content-Type": "application/json"},
+        )
+        return response
+
+
+def test_task_creation_and_result_retrieval():
+    create_task_url = "http://0.0.0.0:9899/supernode/sam"
+    # create_task_url = "https://bizyair-api.siliconflow.cn/x/v1/supernode/sam"
+    image_url = (
+        "https://bizy-air.oss-cn-beijing.aliyuncs.com/examples_asset/sam-people.png"
+    )
+
+    # image_to_sam = "people.png"
+    # img_pil = Image.open(image_to_sam)
+
+    response = requests.get(image_url)
+    if response.status_code == 200:
+        img_pil = Image.open(BytesIO(response.content))
+    else:
+        raise Exception(
+            f"Failed to retrieve the image, status code: {response.status_code}"
+        )
+
+    mode = 2  # 0: auto mode  1:text mode 2: points/boxes 3: batched boxes
+
+    ######################使用Point作为Prompt##############################
+    input_points = np.array([[500, 375]])
+    input_points = json.dumps(input_points.tolist())
+    input_label = np.array([1])
+    input_label = json.dumps(input_label.tolist())
+    payload = {
+        "image": encode_image_to_base64(img_pil),
+        "mode": mode,  # 0: auto mode  1:text mode 2: points/boxes 3: batched boxes
+        "params": {
+            "input_points": input_points,
+            "input_label": input_label,
+            "input_boxes": None,
+        },
+    }
+
+    send_request(create_task_url, payload)
+
+    # ###################使用Box作为Prompt##############################
+    input_boxes = np.array([451.8652, 71.6301, 648.0280, 1022.0955])
+    input_boxes = json.dumps(input_boxes.tolist())
+    payload = {
+        "image": encode_image_to_base64(img_pil),
+        "mode": mode,  # 0: auto mode  1:text mode 2: points/boxes 3: batched boxes
+        "params": {
+            "input_points": None,
+            "input_label": None,
+            "input_boxes": input_boxes,
+        },
+    }
+
+    send_request(create_task_url, payload)
+
+    # ######################使用Points和Box作为Prompt##############################
+    input_boxes = np.array([451.8652, 71.6301, 648.0280, 1022.0955])
+    input_boxes = json.dumps(input_boxes.tolist())
+
+    input_points = np.array([[575, 750]])
+    input_points = json.dumps(input_points.tolist())
+    input_label = np.array([0])
+    input_label = json.dumps(input_label.tolist())
+    payload = {
+        "image": encode_image_to_base64(img_pil),
+        "mode": mode,  # 0: auto mode  1:text mode 2: points/boxes 3: batched boxes
+        "params": {
+            "input_points": input_points,
+            "input_label": input_label,
+            "input_boxes": input_boxes,
+        },
+    }
+
+    send_request(create_task_url, payload)
+
+    # #####################使用Batched Box作为Prompt##############################
+    input_boxes = torch.tensor(
+        [
+            [24.0652, 127.0906, 181.5945, 932.2192],
+            [451.8652, 71.6301, 648.0280, 1022.0955],
+            [731.7250, 201.1820, 956.5409, 1022.2478],
+            [236.7201, 131.5688, 414.6645, 979.8284],
+            [145.1680, 183.1925, 308.3481, 955.5884],
+            [358.4024, 192.8287, 506.4283, 1011.5869],
+            [588.2826, 152.5342, 798.1860, 1021.6285],
+        ]
+    )
+    input_boxes = json.dumps(input_boxes.tolist())
+    mode = 3
+    payload = {
+        "image": encode_image_to_base64(img_pil),
+        "mode": mode,  # 0: auto mode  1:text mode 2: points/boxes 3: batched boxes
+        "params": {
+            "input_boxes": input_boxes,
+        },
+    }
+
+    send_request(create_task_url, payload)
+
+    # ####################使用自动模式作为Prompt##############################
+    mode = 0
+    payload = {
+        "image": encode_image_to_base64(img_pil),
+        "mode": mode,  # 0: auto mode  1:text mode 2: points/boxes 3: batched boxes
+    }
+    send_request(create_task_url, payload)
+
+    ######################使用text模式作为Prompt##############################
+    text = "human"
+    box_threshold = 0.3
+    text_threshold = 0.25
+    mode = 1
+    payload = {
+        "image": encode_image_to_base64(img_pil),
+        "mode": mode,  # 0: auto mode  1:text mode 2: points/boxes 3: batched boxes
+        "params": {
+            "prompt": text,
+            "box_threshold": box_threshold,  # 检测框置信度
+            "text_threshold": text_threshold,  # 文本置信度
+        },
+    }
+
+    send_request(create_task_url, payload)
+
+
+if __name__ == "__main__":
+    test_task_creation_and_result_retrieval()