first public

Author: buitrongdat1108

.idea/.gitignore

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+FROM python:3.9.7
+
+WORKDIR /app
+
+COPY ./main.py ./
+COPY ./requirements.txt ./
+COPY ./src ./src
+# COPY ./src/*.py ./src
+# COPY ./src/character_recognition/*.py ./src/character_recognition
+# COPY ./src/lp_detection/*.py ./src/lp_detection
+# COPY ./src/weight_folder/*.h5 ./src/weight_folder
+# COPY ./src/weight_folder/*.json ./src/weight_folder
+# COPY ./src/weight_folder/config/*.py ./src/weight_folder/config
+# COPY ./src/weight_folder/config/*.yml ./src/weight_folder/config
+# COPY ./src/weight_folder/model/all_plates/*.pth  ./src/weight_folder/model/all_plates
+# COPY ./src/weight_folder/model/all_plates_cut_and_padding/*.pth  ./src/weight_folder/model/all_plates_cut_and_padding
+# COPY ./src/weight_folder/model/all_plates_padding/*.pth  ./src/weight_folder/model/all_plates_padding
+# COPY ./src/weight_folder/model/pretrain/*.pth ./src/weight_folder/model/pretrain
+# COPY ./vietocr/*.py ./vietocr
+# COPY ./vietocr/model/*.py ./vietocr/model
+# COPY ./vietocr/model/backbone/*.py ./vietocr/model/backbone
+# COPY ./vietocr/model/seqmodel/*.py ./vietocr/model/seqmodel
+# COPY ./vietocr/tool/*.py ./vietocr/tool
+COPY ./vietocr ./vietocr
+RUN apt-get update && apt-get install -y python3-opencv
+RUN pip install --upgrade pip
+RUN pip install --no-cache-dir -r requirements.txt
+
+ENTRYPOINT ["python"]
+CMD ["main.py"]

.idea/ALPR_Ver7.iml

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+import flask
+import os
+os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
+from src.lp_detection.detect import get_plate
+from src.detect_and_recognize import load_wpod_net_model, cvtBase64, paddingImg, get_plate_numbers_for_rec_plate, \
+    get_plate_numbers_for_square_plate
+from vietocr.tool.predictor import Predictor
+from vietocr.tool.config import Cfg
+from flask import Flask, request
+from flask_cors import CORS, cross_origin
+import cv2
+import numpy as np
+wpod_net = load_wpod_net_model()
+
+config = Cfg.load_config_from_file('./src/weight_folder/config/vgg_transformer.yml')
+config['cnn']['pretrained'] = False
+config['device'] = 'cpu'
+config['predictor']['beamsearch'] = False
+
+detector = Predictor(config)
+# Build Flask app
+app = Flask(__name__)
+CORS(app)
+app.config['CORS_HEADERS'] = 'Content-Type'
+
+
+def detect_and_recognize_image(inputImg):
+    inputImg = cvtBase64(inputImg)
+    inputImg = paddingImg(inputImg)
+    plate_image, LpType, cor = get_plate(wpod_net, inputImg, Dmax=700, Dmin=500)
+    if LpType == 1:
+        output_plate_image = get_plate_numbers_for_rec_plate(plate_image)
+        plateString = detector.predict(output_plate_image)
+    elif LpType == 2:
+        output_plate_image = get_plate_numbers_for_square_plate(plate_image)
+        plateString = detector.predict(output_plate_image)
+    return plateString
+
+
+@app.route('/detect_plate_base64', methods=['GET', 'POST'])
+@cross_origin(origin='*')
+def plateDetect():
+    inputImg = request.form.get('image')
+    try:
+        plate_number = detect_and_recognize_image(inputImg)
+        return flask.jsonify(number=plate_number, plate_color="")
+    except:
+        return flask.jsonify(number="", plate_color="")
+
+
+if __name__ == '__main__':
+    app.run(threaded=True, port=8765, host='0.0.0.0')

.idea/inspectionProfiles/Project_Default.xml

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+torch
+Pillow
+torchvision
+pyyaml
+imgaug==0.4.0
+einops==0.2.0
+gdown==4.4.0
+prefetch_generator==1.0.1
+lmdb==1.0.0
+matplotlib
+flask
+flask_cors
+keras
+opencv-python
+tensorflow
+scikit-learn

.idea/inspectionProfiles/profiles_settings.xml

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+import cv2
+import numpy as np
+
+
+def predict_from_model(image, model, labels):
+    image = cv2.resize(image, (80, 80))
+    image = np.stack((image,)*3, axis=-1)
+    prediction = labels.inverse_transform([np.argmax(model.predict(image[np.newaxis, :]))])
+    return prediction

.idea/misc.xml

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+import base64
+import cv2
+import numpy as np
+from keras.models import model_from_json
+from PIL import Image
+LP_DETECTION_CFG = {
+    "wpod-net_update1.json": "./src/weight_folder/wpod-net_update1.json",
+    "wpod-net_update1.h5": "./src/weight_folder/wpod-net_update1.h5"
+}
+
+def load_wpod_net_model():
+    try:
+        #path = splitext(path)[0]
+        with open(LP_DETECTION_CFG["wpod-net_update1.json"], 'r') as json_file:
+            model_json = json_file.read()
+        model = model_from_json(model_json, custom_objects={})
+        model.load_weights(LP_DETECTION_CFG["wpod-net_update1.h5"])
+        print("[INFO] Wpod-Net_update1.h5 loaded successfully...")
+        return model
+    except Exception as e:
+        print(e)
+
+#convert base64-type to raw image
+def cvtBase64(base64Img):
+    try:
+        base64Img = np.fromstring(base64.b64decode(base64Img), dtype=np.uint8)
+        base64Img = cv2.imdecode(base64Img, cv2.IMREAD_COLOR)
+    except:
+        return None
+    return base64Img
+
+def paddingImg(inputImg, resize=False):
+    padding = np.zeros([inputImg.shape[1], inputImg.shape[1], 3])
+    a = int((inputImg.shape[1] - inputImg.shape[0]) / 2)
+    padding[a:(inputImg.shape[0] + a), 0:inputImg.shape[1], :] = inputImg
+    inputImg = cv2.resize(inputImg, (480, 480))
+    inputImg = inputImg / 255
+    if resize:
+        inputImg = cv2.resize(inputImg, (720, 720))
+    return inputImg
+
+def sort_contours(cnts, reverse=False):
+    i = 0
+    boundingBoxes = [cv2.boundingRect(c) for c in cnts]
+    (cnts, boundingBoxes) = zip(*sorted(zip(cnts, boundingBoxes), key=lambda b: b[1][i], reverse=reverse))
+    return cnts
+
+# image_count = 47176
+
+def get_plate_numbers_for_rec_plate(plate_image):
+    # global image_count
+    # image_count = image_count + 1
+    padding = np.zeros([470, 470, 3])
+    padding[180:290, 0:470, :] = plate_image
+    # padding = cv2.cvtColor(np.float32(padding), cv2.COLOR_BGR2GRAY)
+    # padding = cv2.merge((padding, padding, padding))
+    cv2.imwrite("temp.jpg", padding)
+    padding_resize = Image.fromarray(np.uint8(padding)).convert('RGB')
+    # path = "/home/vdtc/ALPR/test_components/data_2022_03_29/plate_image_train/%d.jpg" % image_count
+    # padding_resize.save(path)
+    return padding_resize
+
+
+def get_plate_numbers_for_square_plate(plate_image):
+    # global image_count
+    # image_count = image_count + 1
+    padding = np.zeros([470, 470, 3])
+    padding[135:335, 95:375, :] = plate_image
+    padding_resize = Image.fromarray(np.uint8(padding)).convert('RGB')
+    # path = "/home/vdtc/ALPR/test_components/data_2022_03_29/plate_image_train/%d.jpg" % image_count
+    # padding_resize.save(path)
+    return padding_resize
+
+