upload files

BIB_Extraction.py

@@ -0,0 +1,80 @@
+from akaocr import TextEngine, BoxEngine
+import cv2
+import numpy as np
+from PIL import Image
+import re
+
+
+def transform_image(image, box):
+    # Get perspective transform image
+    assert len(box) == 4, "Shape of points must be 4x2"
+    img_crop_width = int(
+        max(
+            np.linalg.norm(box[0] - box[1]),
+            np.linalg.norm(box[2] - box[3])))
+    img_crop_height = int(
+        max(
+            np.linalg.norm(box[0] - box[3]),
+            np.linalg.norm(box[1] - box[2])))
+    pts_std = np.float32([[0, 0],
+                          [img_crop_width, 0],
+                          [img_crop_width, img_crop_height],
+                          [0, img_crop_height]])
+    box = np.array(box, dtype="float32")
+    M = cv2.getPerspectiveTransform(box, pts_std)
+    dst_img = cv2.warpPerspective(
+        image,
+        M, (img_crop_width, img_crop_height),
+        borderMode=cv2.BORDER_REPLICATE,
+        flags=cv2.INTER_CUBIC)
+
+    img_height, img_width = dst_img.shape[0:2]
+    if img_height/img_width >= 1.25:
+        dst_img = np.rot90(dst_img, k=3)
+
+    return dst_img
+
+
+def two_pts(bounding_box):
+    # convert 4-points-bounding-box to 2-points-bounding-box
+    return (
+        (
+            round(min([x[0] for x in bounding_box])),
+            round(min([x[1] for x in bounding_box]))
+        ),
+        (
+            round(max([x[0] for x in bounding_box])),
+            round(max([x[1] for x in bounding_box]))
+        )
+    )
+
+
+class BIB_Extract:
+    def __init__(self):
+        # Initialize the OCR engines
+        self.box_engine = BoxEngine()
+        self.text_engine = TextEngine()
+
+    def __call__(self, image, bib_length):
+        boxes = self.box_engine(image)
+        images = []
+        # crop and transform images for recognition
+        for box in boxes[::-1]:
+            # org_image = cv2.polylines(org_image, [box.astype(
+            #     np.int32)], isClosed=True, color=(0, 255, 0), thickness=2)
+            crop_img = transform_image(image, box)
+            images.append(crop_img)
+
+        # Get the texts from the boxes
+        texts = self.text_engine(images)
+        return self.BIB_filter(texts, bib_length)
+
+    def BIB_filter(self, texts, bib_length):
+        pattern = rf'^\d{{{bib_length}}}$'
+        return [s[0] for s in texts if re.match(pattern, s[0])]
+
+
+if __name__ == '__main__':
+    image = cv2.imread("1.jpg")
+    engine = BIB_Extract()
+    print(engine(image, bib_length=4))

README.md

@@ -1,12 +1,13 @@
 ---
-title: Bib Number
-emoji: 📈
-colorFrom: blue
+title: Akaocr
+emoji: 🐨
+colorFrom: pink
 colorTo: blue
 sdk: streamlit
-sdk_version: 1.38.0
+sdk_version: 1.37.1
 app_file: app.py
 pinned: false
+license: apache-2.0
 ---
 
 Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+import streamlit as st
+from BIB_Extraction import BIB_Extract, two_pts
+import cv2
+import numpy as np
+
+engine = BIB_Extract()
+
+
+def main():
+    st.set_page_config(
+        page_title="BIB Detection",
+        page_icon=":flag-vn:",
+        # layout="wide"
+    )
+
+    uploaded_file = st.file_uploader(
+        "Choose an image...", type=["jpg", "jpeg", "png"])
+
+    if uploaded_file is not None:
+        # Convert the uploaded file to an OpenCV image
+        file_bytes = np.asarray(
+            bytearray(uploaded_file.read()), dtype=np.uint8)
+        org_image = cv2.imdecode(file_bytes, 1)
+        st.image(org_image, channels="BGR", caption='Uploaded Image')
+        st.text(f"BIB Numbers: {engine(org_image, bib_length=4)}")
+
+
+if __name__ == '__main__':
+    main()

requirements.txt

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+streamlit
+akaocr
+opencv-python-headless
+numpy