init

.gitattributes

@@ -0,0 +1,35 @@
+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tar filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.wasm filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text

.gitignore

@@ -0,0 +1,8 @@
+.DS_Store
+__pycache__/
+sources/static/face/*
+sources/static/results/*
+**/*.jpg
+!sources/static/face/.placeholder
+!sources/static/results/.placeholder
+

.pre-commit-config.yaml

@@ -0,0 +1,38 @@
+# See https://pre-commit.com for more information
+# See https://pre-commit.com/hooks.html for more hooks
+
+# Don't run pre-commit on files under third-party/
+exclude: "^\
+  (third-party/.*)\
+  "
+
+repos:
+  - repo: https://github.com/pre-commit/pre-commit-hooks
+    rev: v4.1.0
+    hooks:
+      - id: check-added-large-files # prevents giant files from being committed.
+      - id: check-merge-conflict # checks for some markers such as "<<<<<<<", "=======", and ">>>>>>>".
+      - id: detect-private-key # detects the presence of private keys.
+      - id: end-of-file-fixer # ensures that a file is either empty, or ends with one newline.
+      - id: requirements-txt-fixer # sorts entries in requirements.txt.
+      - id: trailing-whitespace # trims trailing whitespace at the end of lines.
+
+  # # Format YAML and other files
+  # - repo: https://github.com/pre-commit/mirrors-prettier
+  #   rev: v2.5.1
+  #   hooks:
+  #     - id: prettier
+  #       files: \.(js|ts|jsx|tsx|css|less|html|json|markdown|md|yaml|yml)$
+
+  # Format Python files
+  - repo: https://github.com/psf/black
+    rev: 23.7.0
+    hooks:
+      - id: black
+
+  # Sort the order of importing libs
+  - repo: https://github.com/PyCQA/isort
+    rev: 5.12.0
+    hooks:
+      - id: isort
+        args: [--profile=black]

Dockerfile

@@ -0,0 +1,29 @@
+FROM python:3.10.12
+
+RUN apt-get update -y
+
+RUN apt-get install -y software-properties-common \
+    build-essential \
+    hdf5-tools \
+    libgl1 \
+    libgtk2.0-dev \
+    libgeos-dev
+
+RUN useradd -m -u 1000 user
+
+USER user
+
+ENV HOME=/home/user \
+    PATH=/home/user/.local/bin:$PATH
+
+WORKDIR $HOME/app
+
+COPY --chown=user ./requirements.txt ./requirements.txt
+
+RUN pip3 install -r requirements.txt
+
+COPY --chown=user . .
+
+EXPOSE 8080
+
+CMD ["python3", "run.py"]

LICENSE

@@ -0,0 +1,201 @@
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+                        http://www.apache.org/licenses/
+
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+   See the License for the specific language governing permissions and
+   limitations under the License.

README.md

@@ -0,0 +1,11 @@
+---
+title: Idcard Ocr
+emoji: 👁
+colorFrom: red
+colorTo: pink
+sdk: docker
+pinned: false
+app_port: 8080
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

requirements.txt

@@ -0,0 +1,9 @@
+fastapi
+python-multipart
+mmcv==2.1.0
+numpy==1.26.2
+opencv_python==4.8.1.78
+Pillow==9.5.0
+uvicorn==0.17.6
+vietocr==0.3.12
+yolov5==6.0.4

run.py

@@ -0,0 +1,9 @@
+# from imp import reload
+import uvicorn
+
+from sources.Controllers.config import PORT
+
+if __name__ == "__main__":
+    uvicorn.run(
+        "sources:app", host="0.0.0.0", port=int(PORT), reload=True, debug=True
+    )  # workers=1

sources/Controllers/config.py

@@ -0,0 +1,17 @@
+PORT = 8080
+CONF_CONTENT_THRESHOLD = 0.7
+IOU_CONTENT_THRESHOLD = 0.7
+
+# CONF_CORNER_THRESHOLD = 0.8
+# IOU_CORNER_THRESHOLD = 0.5
+
+CORNER_MODEL_PATH = "sources/Database/OCR/weights/corner.pt"
+CONTENT_MODEL_PATH = "sources/Database/OCR/weights/content.pt"
+FACE_MODEL_PATH = "sources/Database/OCR/weights/face.pt"
+# OCR_MODEL_PATH = "sources/Database/OCR/weights/seq2seq.pth"
+# OCR_CFG = 'sources/Database/OCR/config/seq2seq_config.yml'
+DEVICE = "cpu"  # or "cuda:0" if using GPU
+# Config directory
+UPLOAD_FOLDER = "sources/Database/uploads"
+SAVE_DIR = "sources/static/results"
+FACE_DIR = "sources/static/face"

sources/Controllers/main.py

@@ -0,0 +1,126 @@
+import os
+
+import numpy as np
+import yolov5
+from fastapi import File, UploadFile
+from fastapi.encoders import jsonable_encoder
+from fastapi.responses import JSONResponse
+from PIL import Image
+from vietocr.tool.config import Cfg
+from vietocr.tool.predictor import Predictor
+import mmcv
+from io import BytesIO
+
+
+import sources.Controllers.config as cfg
+from sources import app
+from sources.Controllers import utils
+
+
+# Init yolov5 model
+CORNER_MODEL = yolov5.load(cfg.CORNER_MODEL_PATH)
+CONTENT_MODEL = yolov5.load(cfg.CONTENT_MODEL_PATH)
+FACE_MODEL = yolov5.load(cfg.FACE_MODEL_PATH)
+
+# Set conf and iou threshold -> Remove overlap and low confident bounding boxes
+CONTENT_MODEL.conf = cfg.CONF_CONTENT_THRESHOLD
+CONTENT_MODEL.iou = cfg.IOU_CONTENT_THRESHOLD
+
+# CORNER_MODEL.conf = cfg.CONF_CORNER_THRESHOLD
+# CORNER_MODEL.iou = cfg.IOU_CORNER_THRESHOLD
+
+# Config directory
+UPLOAD_FOLDER = cfg.UPLOAD_FOLDER
+SAVE_DIR = cfg.SAVE_DIR
+FACE_CROP_DIR = cfg.FACE_DIR
+
+""" ---- ##### -----"""
+
+
+""" Recognizion detected parts in ID """
+config = Cfg.load_config_from_name(
+    "vgg_seq2seq"
+)  # OR vgg_transformer -> acc || vgg_seq2seq -> time
+# config = Cfg.load_config_from_file(cfg.OCR_CFG)
+# config['weights'] = cfg.OCR_MODEL_PATH
+config["cnn"]["pretrained"] = False
+config["device"] = cfg.DEVICE
+config["predictor"]["beamsearch"] = False
+detector = Predictor(config)
+
+
+@app.post(
+    "/uploader",
+)
+async def upload(file: UploadFile = File(...)):
+    img_bytes = await file.read()
+    img = mmcv.imfrombytes(img_bytes)
+    CORNER = CORNER_MODEL(img)
+    CORNER.save(save_dir="results/")
+    predictions = CORNER.pred[0]
+    categories = predictions[:, 5].tolist()  # Class
+    if len(categories) != 4:
+        error = "Detecting corner failed!"
+        return JSONResponse(status_code=401, content={"message": error})
+    boxes = utils.class_Order(predictions[:, :4].tolist(), categories)
+    IMG = Image.open(BytesIO(img_bytes))
+    center_points = list(map(utils.get_center_point, boxes))
+
+    """ Temporary fixing """
+    c2, c3 = center_points[2], center_points[3]
+    c2_fix, c3_fix = (c2[0], c2[1] + 30), (c3[0], c3[1] + 30)
+    center_points = [center_points[0], center_points[1], c2_fix, c3_fix]
+    center_points = np.asarray(center_points)
+    aligned = utils.four_point_transform(IMG, center_points)
+    aligned = Image.fromarray(aligned)
+
+    CONTENT = CONTENT_MODEL(aligned)
+    predictions = CONTENT.pred[0]
+    categories = predictions[:, 5].tolist()
+    if 7 not in categories:
+        if len(categories) < 9:
+            error = "Missing fields! Detecting content failed!"
+            return JSONResponse(status_code=402, content={"message": error})
+    elif 7 in categories:
+        if len(categories) < 10:
+            error = "Missing fields! Detecting content failed!"
+            return JSONResponse(status_code=402, content={"message": error})
+
+    boxes = predictions[:, :4].tolist()
+
+    """ Non Maximum Suppression """
+    boxes, categories = utils.non_max_suppression_fast(np.array(boxes), categories, 0.7)
+    boxes = utils.class_Order(boxes, categories)
+    if not os.path.isdir(SAVE_DIR):
+        os.mkdir(SAVE_DIR)
+    else:
+        for f in os.listdir(SAVE_DIR):
+            os.remove(os.path.join(SAVE_DIR, f))
+
+    for index, box in enumerate(boxes):
+        left, top, right, bottom = box
+        if 5 < index < 9:
+            right = right + 100
+        cropped_image = aligned.crop((left, top, right, bottom))
+        cropped_image.save(os.path.join(SAVE_DIR, f"{index}.jpg"))
+
+    FIELDS_DETECTED = []
+    for idx, img_crop in enumerate(sorted(os.listdir(SAVE_DIR))):
+        if idx > 0:
+            img_ = Image.open(os.path.join(SAVE_DIR, img_crop))
+            s = detector.predict(img_)
+            FIELDS_DETECTED.append(s)
+
+    if 7 in categories:
+        FIELDS_DETECTED = (
+            FIELDS_DETECTED[:6]
+            + [FIELDS_DETECTED[6] + ", " + FIELDS_DETECTED[7]]
+            + [FIELDS_DETECTED[8]]
+        )
+
+    response = {"data": FIELDS_DETECTED}
+
+    response = jsonable_encoder(response)
+    return JSONResponse(
+        content=response,
+    )

sources/Controllers/utils.py

@@ -0,0 +1,115 @@
+import cv2
+import numpy as np
+
+
+def class_Order(boxes, categories):
+    Z = []
+    # Z = [x for _,x in sorted(zip(categories, boxes))]
+    cate = np.argsort(categories)
+    for index in cate:
+        Z.append(boxes[index])
+
+    return Z
+
+
+def non_max_suppression_fast(boxes, labels, overlapThresh):
+    # if there are no boxes, return an empty list
+    if len(boxes) == 0:
+        return []
+
+        # if the bounding boxes integers, convert them to floats --
+        # this is important since we'll be doing a bunch of divisions
+    if boxes.dtype.kind == "i":
+        boxes = boxes.astype("float")
+
+        # initialize the list of picked indexes
+    pick = []
+    # grab the coordinates of the bounding boxes
+    x1 = boxes[:, 1]
+    y1 = boxes[:, 0]
+    x2 = boxes[:, 3]
+    y2 = boxes[:, 2]
+
+    # compute the area of the bounding boxes and sort the bounding
+    # boxes by the bottom-right y-coordinate of the bounding box
+    area = (x2 - x1 + 1) * (y2 - y1 + 1)
+    idxs = np.argsort(y2)
+
+    # keep looping while some indexes still remain in the indexes
+    # list
+    while len(idxs) > 0:
+        # grab the last index in the indexes list and add the
+        # index value to the list of picked indexes
+        last = len(idxs) - 1
+        i = idxs[last]
+        pick.append(i)
+
+        # find the largest (x, y) coordinates for the start of
+        # the bounding box and the smallest (x, y) coordinates
+        # for the end of the bounding box
+        xx1 = np.maximum(x1[i], x1[idxs[:last]])
+        yy1 = np.maximum(y1[i], y1[idxs[:last]])
+        xx2 = np.minimum(x2[i], x2[idxs[:last]])
+        yy2 = np.minimum(y2[i], y2[idxs[:last]])
+
+        # compute the width and height of the bounding box
+        w = np.maximum(0, xx2 - xx1 + 1)
+        h = np.maximum(0, yy2 - yy1 + 1)
+
+        # compute the ratio of overlap
+        overlap = (w * h) / area[idxs[:last]]
+
+        # delete all indexes from the index list that have
+        idxs = np.delete(
+            idxs, np.concatenate(([last], np.where(overlap > overlapThresh)[0]))
+        )
+
+    # return only the bounding boxes that were picked using the
+    # integer data type
+    final_labels = [labels[idx] for idx in pick]
+    final_boxes = boxes[pick].astype("int")
+    return final_boxes, final_labels
+
+
+def get_center_point(box):
+    left, top, right, bottom = box
+    return left + ((right - left) // 2), top + (
+        (bottom - top) // 2
+    )  # (x_c, y_c) # Need to fix bottom_left and bottom_right
+
+
+def order_points(pts):
+    rect = np.zeros((4, 2), dtype="float32")
+    s = pts.sum(axis=1)
+    rect[0] = pts[np.argmin(s)]
+    rect[2] = pts[np.argmax(s)]
+    diff = np.diff(pts, axis=1)
+    rect[1] = pts[np.argmin(diff)]
+    rect[3] = pts[np.argmax(diff)]
+    return rect
+
+
+def four_point_transform(image, pts):
+    image = np.asarray(image)
+    rect = order_points(pts)
+    (tl, tr, br, bl) = rect
+    widthA = np.sqrt(((br[0] - bl[0]) ** 2) + ((br[1] - bl[1]) ** 2))
+    widthB = np.sqrt(((tr[0] - tl[0]) ** 2) + ((tr[1] - tl[1]) ** 2))
+    maxWidth = max(int(widthA), int(widthB))
+    heightA = np.sqrt(((tr[0] - br[0]) ** 2) + ((tr[1] - br[1]) ** 2))
+    heightB = np.sqrt(((tl[0] - bl[0]) ** 2) + ((tl[1] - bl[1]) ** 2))
+    maxHeight = max(int(heightA), int(heightB))
+    dst = np.array(
+        [[0, 0], [maxWidth - 1, 0], [maxWidth - 1, maxHeight - 1], [0, maxHeight - 1]],
+        dtype="float32",
+    )
+    M = cv2.getPerspectiveTransform(rect, dst)
+    warped = cv2.warpPerspective(image, M, (maxWidth, maxHeight))
+    return warped
+
+
+# def getMissingCorner(categories, boxes): # boxes: top_left, top_right, bottom_left, bottom_right
+# 	if 0 not in categories: # Missing top_left
+# 		delta_vertical = boxes[3][2] - boxes[1][2]
+# 		delta_horizon = boxes[3][3] - boxes[2][3]
+# 		x_miss =

sources/Database/OCR/weights/content.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:fc7727021d6d941dadb04046a0992f608df0f7681ffa388206d1ee03d19cd1b7
+size 14429237

sources/Database/OCR/weights/corner.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0256e0bd683e16ef99e03e6cd13f67b786592773f53bca820edcfddc9f0f18cc
+size 14470581

sources/Database/OCR/weights/face.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:2618f3768eb305e9c635a784994f3a238d1df6705680b52bc4947ed24b67868d
+size 14377503

sources/Database/OCR/weights/seq2seq.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:0921503a41375a0584268e23ef3d414ea478a8fe8777865c7745d38f2d0bc5db
+size 89575371

sources/__init__.py

@@ -0,0 +1,7 @@
+from fastapi import FastAPI
+
+
+app = FastAPI()
+
+
+from sources.Controllers import main