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.gitattributes

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 Helmet-Detect-model/best.pt filter=lfs diff=lfs merge=lfs -text
 Helmet-Detect-model/yolov11nbest.pt filter=lfs diff=lfs merge=lfs -text
+ANPR_IND/licence_character.pt filter=lfs diff=lfs merge=lfs -text
+ANPR_IND/licence_plat.pt filter=lfs diff=lfs merge=lfs -text
+ANPR_IND/output/sample.jpg filter=lfs diff=lfs merge=lfs -text
+ANPR_IND/sample_image3.jpg filter=lfs diff=lfs merge=lfs -text
+ANPR_IND/sample/video/sample_video.mp4 filter=lfs diff=lfs merge=lfs -text

ANPR_IND/.gitattributes

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+*.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
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+*tfevents* filter=lfs diff=lfs merge=lfs -text

ANPR_IND/README.md

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+---
+title: ANPR IND
+emoji: 🔥
+colorFrom: gray
+colorTo: indigo
+sdk: gradio
+sdk_version: 4.13.0
+app_file: app.py
+pinned: false
+license: mit
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

ANPR_IND/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()

ANPR_IND/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'])
+}

ANPR_IND/licence_character.pt

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

ANPR_IND/licence_plat.pt

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

ANPR_IND/packages.txt

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+python3-opencv

ANPR_IND/requirements.txt

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+gradio
+opencv-python
+ultralytics

ANPR_IND/sample/video/sample_video.mp4

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+version https://git-lfs.github.com/spec/v1
+oid sha256:73dfcf18c6bbe34e552dcf72f099ae993a5fb3097974f8eed99280141c4f93ad
+size 852036

ANPR_IND/scripts/__init__.py

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+

ANPR_IND/scripts/bboxAnnotator.py

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+import numpy as np
+import cv2
+
+class BBOXAnnotator:
+    def draw_bbox(self, ImgPath, bboxPlate, plateNumber):
+        img = ImgPath
+
+        for idc, bb in enumerate(bboxPlate):
+            # print('bboxp : ', bboxp)
+            # print('bb : ', bb)
+            x1, y1, x2, y2 = map(int, bb)  # Ubah koordinat ke dalam integer
+            color = (0, 255, 0)  # Warna bounding box (hijau)
+            thickness = 2  # Ketebalan garis bounding box
+
+            # Gambar bounding box pada gambar
+            cv2.rectangle(img, (x1, y1), (x2, y2), color, thickness)
+
+            # Tambahkan label nama di atas bounding box
+            if len(plateNumber) == len(bboxPlate):
+                print('plateNumber : ', plateNumber)
+                # print('idc : ', idc)
+                cv2.putText(img, plateNumber[idc], (x1, y1 - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, thickness)
+            
+        if len(plateNumber) == 0:
+            plateNumber = ['No Plate Detected']
+        
+        return img, plateNumber

ANPR_IND/scripts/charExtraction.py

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+from .extractor import CharExtractor
+from ultralytics import YOLO
+import numpy as np
+import cv2
+
+
+class CharExtraction:
+    def __init__(self, wPlatePath, wCharacterPath, classList, sizePlate, conf):
+        self.wPlatePath = wPlatePath
+        self.wCharacterPath = wCharacterPath
+        self.classList = classList
+        self.width, self.height = sizePlate
+        self.conf = conf
+        
+        self.model_plat = YOLO(self.wPlatePath)
+        self.model_char = YOLO(self.wCharacterPath)
+        
+        self.extractor = CharExtractor()
+        
+    def predict(self, image, conf):
+        if conf == None:
+            self.conf = 0.3
+        else:
+            self.conf = conf
+
+        charpredict = []
+        platePrediction = self.model_plat(image, conf=self.conf, verbose=False) ##Melakukan prediksi dan rectange gambar
+        platBoxes, croppedPlateImg, confident = self._get_cropped_plate(platePrediction)
+        # print('lcrop : ', len(croppedPlateImg))
+        
+        for croppImg in croppedPlateImg:
+            characterPrediction = self.model_char(croppImg, conf=self.conf, verbose=False)
+            charpredict.append(characterPrediction)
+            
+        # print('lchar : ', len(charpredict))
+        characterBBOX, characterClass = self._get_bboxClass_char(charpredict, self.classList)
+        # print('charBox : ', len(characterBBOX))
+        plateNumber = self.extractor._extract_bbox(characterBBOX, characterClass)
+        # print('Plate Number : ', plateNumber)   
+        
+        # print('plateboxes : ', platBoxes)
+        # print('platenum : ', plateNumber)        
+        return platBoxes, plateNumber, confident             
+    
+    def _get_cropped_plate(self, imgPlate): # Get cropped plate image from whole image
+        croppedImage = [] ##Bisa berisi banyak bbox
+        boxes = imgPlate[0].boxes
+        originalImage = imgPlate[0].orig_img
+        confid = boxes.conf.numpy()
+        h,w,c = originalImage.shape
+        if len(boxes) != 0:
+            for box in boxes.xyxy:
+                x1,y1,x2,y2 = int(box[0]), int(box[1]), int(box[2]), int(box[3])
+                croppedPlateImage = originalImage[y1:y2, x1:x2, :]
+                croppedPlateImage = cv2.resize(croppedPlateImage, (self.width, self.height))
+                croppedImage.append(croppedPlateImage)
+        else:
+            croppedImage.append(originalImage)
+        
+        return boxes.xyxy, croppedImage, confid
+    
+    def _get_bboxClass_char(self, imgCroppedPlate, classList):
+        boxChar, classC = [], []
+        for imgCropped in imgCroppedPlate:
+            boxes = imgCropped[0].boxes
+            if len(boxes.cls) != 0:
+                # print(boxes.cls)
+                classId = [int(x) for x in boxes.cls.numpy()]
+                # print('classId : ', classId)
+                classCharacter = np.array(classList[classId])
+                characterBBOX = boxes.xywh
+                boxChar.append(characterBBOX)
+                classC.append(classCharacter)
+            # else:
+            #     classCharacter = []
+            #     characterBBOX = []
+            
+        return boxChar, classC
+        

ANPR_IND/scripts/extractor.py

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+import cv2
+import numpy as np
+import matplotlib.pyplot as plt
+
+class CharExtractor:
+    def __init__(self):
+        self.classes = None
+        self.class_char = []
+        self.y_class = None
+        self.x_class = None
+        self.bbox = None
+        self.x_bbox = None
+        self.y_bbox = None
+    
+    def _extract_bbox(self, bboxes, classes):
+        plate_number = []
+        self.bbox = bboxes
+        self.classes = classes
+        
+        for ix,bbox in enumerate(bboxes):
+            if len(bbox) != 0:
+                lowest_x, highest_y = self._get_xy(bbox, ix)
+                print('x_class : ', self.x_class)
+                # print('y_bbox : ', self.y_bbox)
+                for idx, point in enumerate(self.y_bbox):
+                    x, y = point[0], point[1]
+                    if y <= highest_y and x >= lowest_x and self.x_class.size > 1:
+                        self.class_char.append(self.x_class[idx])
+
+                plate_num = ''.join(str(v) for v in self.class_char)
+                self.class_char.clear()
+        
+            else:
+                plate_num = ''
+            
+            plate_number.append(plate_num)
+        
+        return plate_number
+    
+    def _get_xy(self, bbox, ix):
+        y_id = bbox[:, 1].argsort()
+        # print('y_id : ', y_)
+        if len(bbox[y_id]) >= 4:
+            sorted_y = bbox[y_id][:-4]
+            self.y_bbox = sorted_y
+            # print('y_class : ', self.classes[ix])
+            # print('yid : ', y_id)
+            self.y_class = np.array(self.classes[ix][y_id])[:-4]
+            highest_y = max([x[1] for x in sorted_y])
+            
+            x_id = sorted_y[:,0].argsort()
+            sorted_x = sorted_y[x_id]
+            self.x_bbox = sorted_x
+            # print('char : ', self.y_class)
+            self.x_class = self.y_class[x_id]
+            lowest_x = min([x[0] for x in sorted_x])
+            
+        else:
+            sorted_y = bbox[y_id]
+            self.y_bbox = sorted_y
+            # print('y_class : ', self.classes)
+            # print('yid : ', y_id)
+            self.y_class = np.array(self.classes[ix][y_id])
+            highest_y = max([x[1] for x in sorted_y])
+            
+            x_id = sorted_y[:,0].argsort()
+            sorted_x = sorted_y[x_id]
+            self.x_bbox = sorted_x
+            # print('char : ', self.y_class)
+            self.x_class = self.y_class
+            lowest_x = min([x[0] for x in sorted_x])
+        
+        return lowest_x, highest_y
+ 
+            
+        

ANPR_IND/test.txt

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+'test application'