docker build 构建修改

deepdoc/vision/t_ocr.py

@@ -0,0 +1,93 @@
+#
+#  Copyright 2025 The InfiniFlow 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.
+#
+
+import os
+import sys
+sys.path.insert(
+    0,
+    os.path.abspath(
+        os.path.join(
+            os.path.dirname(
+                os.path.abspath(__file__)),
+            '../../')))
+
+from deepdoc.vision.seeit import draw_box
+from deepdoc.vision import OCR, init_in_out
+import argparse
+import numpy as np
+import trio
+
+# os.environ['CUDA_VISIBLE_DEVICES'] = '0,2' #2 gpus, uncontinuous
+os.environ['CUDA_VISIBLE_DEVICES'] = '0' #1 gpu
+# os.environ['CUDA_VISIBLE_DEVICES'] = '' #cpu
+
+
+def main(args):
+    import torch.cuda
+
+    cuda_devices = torch.cuda.device_count()
+    limiter = [trio.CapacityLimiter(1) for _ in range(cuda_devices)] if cuda_devices > 1 else None
+    ocr = OCR()
+    images, outputs = init_in_out(args)
+
+    def __ocr(i, id, img):
+        print("Task {} start".format(i))
+        bxs = ocr(np.array(img), id)
+        bxs = [(line[0], line[1][0]) for line in bxs]
+        bxs = [{
+            "text": t,
+            "bbox": [b[0][0], b[0][1], b[1][0], b[-1][1]],
+            "type": "ocr",
+            "score": 1} for b, t in bxs if b[0][0] <= b[1][0] and b[0][1] <= b[-1][1]]
+        img = draw_box(images[i], bxs, ["ocr"], 1.)
+        img.save(outputs[i], quality=95)
+        with open(outputs[i] + ".txt", "w+", encoding='utf-8') as f:
+            f.write("\n".join([o["text"] for o in bxs]))
+
+        print("Task {} done".format(i))
+
+    async def __ocr_thread(i, id, img, limiter = None):
+        if limiter:
+            async with limiter:
+                print("Task {} use device {}".format(i, id))
+                await trio.to_thread.run_sync(lambda: __ocr(i, id, img))
+        else:
+            __ocr(i, id, img)
+
+    async def __ocr_launcher():
+        if cuda_devices > 1:
+            async with trio.open_nursery() as nursery:
+                for i, img in enumerate(images):
+                    nursery.start_soon(__ocr_thread, i, i % cuda_devices, img, limiter[i % cuda_devices])
+                    await trio.sleep(0.1)
+        else:
+            for i, img in enumerate(images):
+                await __ocr_thread(i, 0, img)
+
+    trio.run(__ocr_launcher)
+
+    print("OCR tasks are all done")
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--inputs',
+                        help="Directory where to store images or PDFs, or a file path to a single image or PDF",
+                        required=True)
+    parser.add_argument('--output_dir', help="Directory where to store the output images. Default: './ocr_outputs'",
+                        default="./ocr_outputs")
+    args = parser.parse_args()
+    main(args)