Update mix_inference.py

替换文本OCR模型为paddleocr

src/models/utils/mix_inference.py

@@ -7,9 +7,7 @@ from collections import Counter
 from typing import List
 from PIL import Image
 
-from surya.detection import batch_text_detection
-from surya.input.processing import slice_polys_from_image
-from surya.recognition import batch_recognition
+from paddleocr.ppocr.utils.utility import alpha_to_color
 
 from ..det_model.inference import predict as latex_det_predict
 from ..det_model.Bbox import Bbox, draw_bboxes
@@ -126,6 +124,21 @@ def split_conflict(ocr_bboxes: List[Bbox], latex_bboxes: List[Bbox]) -> List[Bbo
     return res
 
 
+def slice_from_image(img: np.ndarray, ocr_bboxes: List[Bbox]) -> List[np.ndarray]:
+    sliced_imgs = []
+    for bbox in ocr_bboxes:
+        x, y = int(bbox.p.x), int(bbox.p.y)
+        w, h = int(bbox.w), int(bbox.h)
+        sliced_img = img[y:y+h, x:x+w]
+        sliced_imgs.append(sliced_img)
+    return sliced_imgs
+
+
+def preprocess_image(_image):
+    _image = alpha_to_color(_image, (255, 255, 255))
+    return _image
+
+
 def mix_inference(
     img_path: str,
     language: str,
@@ -143,6 +156,7 @@ def mix_inference(
     '''
     global img
     img = cv2.imread(img_path)
+    img = alpha_to_color(img, (255, 255, 255))
     corners = [tuple(img[0, 0]), tuple(img[0, -1]),
                tuple(img[-1, 0]), tuple(img[-1, -1])]
     bg_color = np.array(Counter(corners).most_common(1)[0][0])
@@ -156,50 +170,33 @@ def mix_inference(
     draw_bboxes(Image.fromarray(img), latex_bboxes, name="latex_bboxes(merged).png")
     masked_img = mask_img(img, latex_bboxes, bg_color)
 
-    det_model, det_processor, rec_model, rec_processor = lang_ocr_models
-    images = [Image.fromarray(masked_img)]
-    det_prediction = batch_text_detection(images, det_model, det_processor)[0]
+    det_model, rec_model = lang_ocr_models
+    det_prediction, _ = det_model(masked_img)
     # log results
     draw_bboxes(Image.fromarray(img), latex_bboxes, name="ocr_bboxes(unmerged).png")
 
-    lang = [language]
-    slice_map = []
-    all_slices = []
-    all_langs = []
     ocr_bboxes = [
         Bbox(
-            p.bbox[0], p.bbox[1], p.bbox[3] - p.bbox[1], p.bbox[2] - p.bbox[0],
+            p[0][0], p[0][1], p[3][1]-p[0][1], p[1][0]-p[0][0],
             label="text",
-            confidence=p.confidence,
+            confidence=None,
             content=None
         )
-        for p in det_prediction.bboxes
+        for p in det_prediction
     ]
     ocr_bboxes = sorted(ocr_bboxes)
     ocr_bboxes = bbox_merge(ocr_bboxes)
     draw_bboxes(Image.fromarray(img), ocr_bboxes, name="ocr_bboxes(merged).png")
     ocr_bboxes = split_conflict(ocr_bboxes, latex_bboxes)
     ocr_bboxes = list(filter(lambda x: x.label == "text", ocr_bboxes))
-    polygons = [
-        [
-            [bbox.ul_point.x, bbox.ul_point.y],
-            [bbox.ur_point.x, bbox.ur_point.y],
-            [bbox.lr_point.x, bbox.lr_point.y],
-            [bbox.ll_point.x, bbox.ll_point.y]
-        ]
-        for bbox in ocr_bboxes
-    ]
 
-    slices = slice_polys_from_image(images[0], polygons)
-    slice_map.append(len(slices))
-    all_slices.extend(slices)
-    all_langs.extend([lang] * len(slices))
+    sliced_imgs: List[np.ndarray] = slice_from_image(img, ocr_bboxes)
+    rec_predictions, _ = rec_model(sliced_imgs)
 
-    rec_predictions, _ = batch_recognition(all_slices, all_langs, rec_model, rec_processor)
 
     assert len(rec_predictions) == len(ocr_bboxes)
     for content, bbox in zip(rec_predictions, ocr_bboxes):
-        bbox.content = content
+        bbox.content = content[0]
     
     latex_imgs =[]
     for bbox in latex_bboxes: