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sample_workflow.py

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+"""
+Minimal example: binarize a document image using the SBB ONNX model.
+
+    pip install onnxruntime-gpu numpy Pillow
+    python3 sample_workflow.py input.jpg output.tif
+"""
+
+import sys
+import numpy as np
+from PIL import Image
+import onnxruntime as ort
+
+MODEL = "model_convtranspose.onnx"
+PATCH = 448
+
+# Load model
+sess = ort.InferenceSession(MODEL, providers=["CUDAExecutionProvider", "CPUExecutionProvider"])
+
+# Load image
+img = np.array(Image.open(sys.argv[1]).convert("RGB"))
+h, w = img.shape[:2]
+
+# Extract 448x448 patches (the model requires fixed-size input)
+patches, positions = [], []
+for y in range(0, h, PATCH):
+    for x in range(0, w, PATCH):
+        patch = np.zeros((PATCH, PATCH, 3), dtype=np.uint8)
+        ph, pw = min(PATCH, h - y), min(PATCH, w - x)
+        patch[:ph, :pw] = img[y:y+ph, x:x+pw]
+        patches.append(patch)
+        positions.append((x, y))
+
+# Normalize (matches original TF model's float64->float32 rounding)
+lut = np.array([np.float32(np.float64(i) / 255.0) for i in range(256)], dtype=np.float32)
+patches_float = lut[np.array(patches).astype(np.int32)]
+
+# Run inference in batches
+outputs = []
+for i in range(0, len(patches), 64):
+    batch = patches_float[i:i+64]
+    out = sess.run(["activation_55"], {"input_1": batch})[0]
+    outputs.append(out)
+output = np.concatenate(outputs)
+
+# Threshold and reconstruct
+result = np.zeros((h, w), dtype=np.float32)
+weight = np.zeros((h, w), dtype=np.float32)
+for i, (x, y) in enumerate(positions):
+    prob = output[i, :, :, 1]
+    binary = np.where((prob * 255).astype(np.uint8) <= 128, 255.0, 0.0)
+    ah, aw = min(PATCH, h - y), min(PATCH, w - x)
+    result[y:y+ah, x:x+aw] += binary[:ah, :aw]
+    weight[y:y+ah, x:x+aw] += 1.0
+result = (result / np.maximum(weight, 1)).astype(np.uint8)
+
+# Save
+Image.fromarray(result, "L").convert("1").save(
+    sys.argv[2], format="TIFF", compression="group4", dpi=(300, 300)
+)
+print(f"Saved {sys.argv[2]}")