feat: mnn matting model (#73)

* 基于mnn框架推理 * mnn导入方式修改 * Update requirements.txt --------- Co-authored-by: Ze-Yi LIN <[email protected]>
Zeyi-Lin · Sep 7, 2024 · 9740752 · 9740752
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commit 9740752
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Showing 3 changed files with 41 additions and 2 deletions.
diff --git a/hivision/creator/human_matting.py b/hivision/creator/human_matting.py
@@ -25,6 +25,11 @@
         "weights",
         "modnet_photographic_portrait_matting.onnx",
     ),
+    "mnn_hivision_modnet": os.path.join(
+        os.path.dirname(__file__),
+        "weights",
+        "mnn_hivision_modnet.mnn",
+    )
 }
 
 
@@ -40,6 +45,32 @@ def extract_human(ctx: Context):
     ctx.matting_image = ctx.processing_image.copy()
 
 
+def get_mnn_modnet_matting(input_image, checkpoint_path, ref_size=512):
+    try:
+        import MNN.expr as expr
+        import MNN.nn as nn
+    except ImportError as e:
+        raise ImportError("MNN模块未安装或导入错误。请确保已安装MNN库，使用命令 'pip install mnn' 安装。") from e
+    config = {}
+    config['precision'] = 'low'  # 当硬件支持（armv8.2）时使用fp16推理
+    config['backend'] = 0  # CPU
+    config['numThread'] = 4  # 线程数
+    im, width, length = read_modnet_image(input_image, ref_size=512)
+    rt = nn.create_runtime_manager((config,))
+    net = nn.load_module_from_file(checkpoint_path, ['input1'], ['output1'], runtime_manager=rt)
+    input_var = expr.convert(im, expr.NCHW)
+    output_var = net.forward(input_var)
+    matte = expr.convert(output_var, expr.NCHW)
+    matte = matte.read()#var转换为np
+    matte = (matte * 255).astype("uint8")
+    matte = np.squeeze(matte)
+    mask = cv2.resize(matte, (width, length), interpolation=cv2.INTER_AREA)
+    b, g, r = cv2.split(np.uint8(input_image))
+
+    output_image = cv2.merge((b, g, r, mask))
+
+    return output_image
+
 def extract_human_modnet_photographic_portrait_matting(ctx: Context):
     """
     人像抠图
@@ -53,6 +84,11 @@ def extract_human_modnet_photographic_portrait_matting(ctx: Context):
     ctx.processing_image = hollow_out_fix(matting_image)
     ctx.matting_image = ctx.processing_image.copy()
 
+def extract_human_mnn_modnet(ctx: Context):
+    matting_image = get_mnn_modnet_matting(ctx.processing_image, WEIGHTS["mnn_hivision_modnet"])
+    ctx.processing_image = hollow_out_fix(matting_image)
+    ctx.matting_image = ctx.processing_image.copy()
+
 
 def hollow_out_fix(src: np.ndarray) -> np.ndarray:
     """

diff --git a/inference.py b/inference.py
@@ -12,6 +12,7 @@
 from hivision.creator.human_matting import (
     extract_human_modnet_photographic_portrait_matting,
     extract_human,
+    extract_human_mnn_modnet,
 )
 
 parser = argparse.ArgumentParser(description="HivisionIDPhotos 证件照制作推理程序。")
@@ -24,7 +25,7 @@
     "add_background",
     "generate_layout_photos",
 ]
-MATTING_MODEL = ["hivision_modnet", "modnet_photographic_portrait_matting"]
+MATTING_MODEL = ["hivision_modnet", "modnet_photographic_portrait_matting", "mnn_hivision_modnet"]
 RENDER = [0, 1, 2]
 
 parser.add_argument(
@@ -64,6 +65,8 @@
     creator.matting_handler = extract_human
 elif args.matting_model == "modnet_photographic_portrait_matting":
     creator.matting_handler = extract_human_modnet_photographic_portrait_matting
+elif args.matting_model == "mnn_hivision_modnet":
+    creator.matting_handler = extract_human_mnn_modnet
 
 root_dir = os.path.dirname(os.path.abspath(__file__))
 input_image = cv2.imread(args.input_image_dir, cv2.IMREAD_UNCHANGED)

diff --git a/requirements.txt b/requirements.txt
@@ -2,4 +2,4 @@ opencv-python>=4.8.1.78
 onnxruntime>=1.15.0
 numpy<=1.26.4
 requests
-mtcnn-runtime
+mtcnn-runtime