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app.py

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+import os
+import cv2
+import gradio as gr
+import numpy as np
+# from configs.config import Configuration
+from scripts.charExtraction import CharExtraction
+from scripts.bboxAnnotator import BBOXAnnotator
+
+wPathPlat = "licence_plat.pt"
+wPathChar = "licence_character.pt"
+classList = np.array(['A','B','C','D','E','F','G','H','I','J','K','L','M',
+                            'N','O','P','Q','R','S','T','U','V','W','X','Y','Z',
+                            '0','1','2','3','4','5','6','7','8','9'])
+sizePlat = (416,200)
+# imageSample = Configuration['imageSample']
+
+
+extractor = CharExtraction(wPlatePath=wPathPlat, wCharacterPath=wPathChar, classList=classList,
+                            sizePlate=sizePlat, conf=0.5)
+annotator = BBOXAnnotator()
+
+def getAnnotatedImage(frame, conf):
+    bbox, plateNum, confidence = extractor.predict(image=frame, conf=conf)
+    annotateImg, plateNum = annotator.draw_bbox(frame, bbox, plateNum)
+    prob_dict = {key: value for key, value in zip(plateNum, confidence)}
+
+    # pathOu = os.path.join(os.path.abspath(''), "output/sample.jpg")
+    # cv2.imwrite(pathOu, annotateImg)
+
+    return annotateImg, prob_dict
+
+def video_identity(frame, conf=0.45):
+    image, plateNumber = getAnnotatedImage(frame, conf)
+
+    return image, plateNumber
+
+
+demo = gr.Interface(fn=video_identity,
+                    inputs=[gr.Image(label='Input image'), gr.Slider(minimum=0.1, maximum=1.0, label='Conf value')],
+                    outputs=[gr.Image(label='Result'), gr.Label(label='Plate Number Detected')],
+                    examples = [["sample_image2.jpg"], ["sample_image3.jpg"], ["sample_image5.jpg"], ["sample_image6.jpg"]],
+                    cache_examples=True)
+
+if __name__ == "__main__":
+    demo.queue()
+    demo.launch()

configs/config.py

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+import os
+import numpy as np
+
+current_dir = os.getcwd()
+# print('current direc: ', current_dir)
+Configuration = {
+    'sizePlat': (416,200),
+    'weightPlatDir': os.path.join(current_dir, "weights\licence_plat.pt"),
+    'weightCharDir': os.path.join(current_dir, "weights\licence_character.pt"),
+    # 'imageSample': os.path.join(current_dir, "samples\mobil_2.jpg"),
+    # 'outputFile': os.path.join(current_dir, "runs\detect"),
+    'classListCharacter': np.array(['A','B','C','D','E','F','G','H','I','J','K','L','M',
+                            'N','O','P','Q','R','S','T','U','V','W','X','Y','Z',
+                            '0','1','2','3','4','5','6','7','8','9'])
+}

licence_character.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:84025e3a1af9d88f9dee0320a139b7ee05a9adfbdeae02532094cdba6781fe32
+size 87648392

licence_plat.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:3d6ae8c01ce44cbed9cb40cda72da699c45b08520156fe558c936cfdb989b209
+size 87605512

packages.txt

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+python3-opencv

requirements.txt

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+gradio
+opencv-python
+ultralytics