Initial commit or another suitable message

TESTING.py

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+from ultralytics import YOLO
+
+# Load a pretrained YOLOv8n model
+model = YOLO('runs/detect/train18/weights/best.pt')
+
+# Define remote image or video URL
+source = 'https://source.roboflow.com/dRDeH1hYd0WQDBtCSKP39MHuZOY2/3eO0urIBcEjzYDWshuFg/original.jpg'
+
+# Run inference on the source
+results = model(source)  # list of Results objects

data.yaml

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+path: C:\Users\Ahmed\PycharmProjects\car plate.v26i.yolov8\data
+train: train/images
+val: valid/images
+test: test/images
+nc: 51
+names: ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9', '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', 'exp', 'new_DUBAI', 'new_RAK', 'new_abudabi', 'new_ajman', 'new_am', 'new_fujairah', 'old_DUBAI', 'old_RAK', 'old_abudabi', 'old_ajman', 'old_am', 'old_fujira', 'old_sharka', 'plate']
+
+roboflow:
+  workspace: zara-hara
+  project: car-plate-k6xij
+  version: 26
+  license: CC BY 4.0
+  url: https://universe.roboflow.com/zara-hara/car-plate-k6xij/dataset/26

main.py

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+from ultralytics import YOLO
+
+# Load a model
+model = YOLO("yolov8n.yaml")  # build a new model from scratch
+
+# Use the model
+model.train(data="data.yaml", epochs=3)  # train the model

plate_only.py

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+import os
+from ultralytics import YOLO
+import cv2
+import requests
+import numpy as np
+from io import BytesIO
+import matplotlib.pyplot as plt
+
+model_path = os.path.join('.', 'runs', 'detect', 'train23', 'weights', 'last.pt')
+
+specific_classes = ['0', '1', '2', '3', '4', '5', '6', '7', '8',
+                    '9', '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']
+state_classes = {
+    'exp': 'Export',
+    'new_DUBAI': 'Dubai',
+    'new_RAK': 'Ras Al-Kaimah',
+    'new_abudabi': 'Abu Dhabi',
+    'new_ajman': 'Ajman',
+    'new_am': 'Umm Al Quwain',
+    'new_fujairah': 'Fujairah',
+    'old_DUBAI': 'Dubai',
+    'old_RAK': 'Ras Al-Kaimah',
+    'old_abudabi': 'Abu Dhabi',
+    'old_ajman': 'Ajman',
+    'old_am': 'Umm Al-Quwain',
+    'old_fujira': 'Fujairah',
+    'old_sharka': 'Sharjah'
+}
+
+# Load a model
+model = YOLO(model_path)  # load a custom model
+
+threshold = 0.4
+
+
+def detect_text(image):
+    output_array = []
+    state_name = "couldn\'t be detected"
+
+    H, W, _ = image.shape
+
+    results = model(image)[0]
+
+    # Sort the results by the x-axis position
+    sorted_results = sorted(results.boxes.data.tolist(), key=lambda x: x[0])
+
+
+    for x1, y1, x2, y2, score, class_id in sorted_results:
+        class_name = model.names[class_id]
+
+        if (score > threshold) and (class_name in specific_classes):
+                output_array.append(class_name)
+
+
+
+    for x1, y1, x2, y2, score, class_id in sorted_results:
+        class_name = model.names[class_id]
+        if score > threshold and (class_name in state_classes):
+                state_name = state_classes[class_name]
+                break
+
+
+    if 'D' in output_array and 'Q' in output_array:    #error correction
+        output_array.remove('Q')
+
+
+
+    return output_array , state_name
+
+
+def main():
+    # URL list containing image URLs
+    image_urls = [
+        'https://c8.alamy.com/comp/A8G1XR/luxury-car-bmw-with-dubai-license-plate-photo-by-willy-matheisl-A8G1XR.jpg',
+        # Add more image URLs here
+    ]
+
+    for image_url in image_urls:
+        response = requests.get(image_url)
+        if response.status_code == 200:
+            image_data = BytesIO(response.content)
+            image = cv2.imdecode(np.frombuffer(image_data.read(), np.uint8), cv2.IMREAD_COLOR)
+
+            H, W, _ = image.shape
+
+            results = model(image)[0]
+
+            sorted_results = sorted (results.boxes.data.tolist (), key=lambda x: x[0])
+
+            for result in sorted_results:
+                x1, y1, x2, y2, score, class_id = result
+
+                if score > threshold and results.names[int(class_id)] == 'plate':
+                    cv2.rectangle(image, (int(x1), int(y1)), (int(x2), int(y2)), (0, 255, 0), 4)
+                    plate_img = image[int(y1):int(y2), int(x1):int(x2)]
+                    output , state = detect_text(plate_img)
+                    print(output)
+                    print(f"The state this car belongs to {state}")
+
+
+            plt.figure()
+            plt.imshow(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
+
+            plt.show()
+
+        else:
+            print(f"Failed to retrieve image from URL: {image_url}")
+
+
+if __name__ == "__main__":
+    main()

runs/detect/train/args.yaml

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+task: detect
+mode: train
+model: yolov8n.yaml
+data: data.yaml
+epochs: 3
+patience: 50
+batch: 16
+imgsz: 640
+save: true
+save_period: -1
+cache: false
+device: null
+workers: 8
+project: null
+name: null
+exist_ok: false
+pretrained: true
+optimizer: auto
+verbose: true
+seed: 0
+deterministic: true
+single_cls: false
+rect: false
+cos_lr: false
+close_mosaic: 10
+resume: false
+amp: true
+fraction: 1.0
+profile: false
+freeze: null
+overlap_mask: true
+mask_ratio: 4
+dropout: 0.0
+val: true
+split: val
+save_json: false
+save_hybrid: false
+conf: null
+iou: 0.7
+max_det: 300
+half: false
+dnn: false
+plots: true
+source: null
+show: false
+save_txt: false
+save_conf: false
+save_crop: false
+show_labels: true
+show_conf: true
+vid_stride: 1
+stream_buffer: false
+line_width: null
+visualize: false
+augment: false
+agnostic_nms: false
+classes: null
+retina_masks: false
+boxes: true
+format: torchscript
+keras: false
+optimize: false
+int8: false
+dynamic: false
+simplify: false
+opset: null
+workspace: 4
+nms: false
+lr0: 0.01
+lrf: 0.01
+momentum: 0.937
+weight_decay: 0.0005
+warmup_epochs: 3.0
+warmup_momentum: 0.8
+warmup_bias_lr: 0.1
+box: 7.5
+cls: 0.5
+dfl: 1.5
+pose: 12.0
+kobj: 1.0
+label_smoothing: 0.0
+nbs: 64
+hsv_h: 0.015
+hsv_s: 0.7
+hsv_v: 0.4
+degrees: 0.0
+translate: 0.1
+scale: 0.5
+shear: 0.0
+perspective: 0.0
+flipud: 0.0
+fliplr: 0.5
+mosaic: 1.0
+mixup: 0.0
+copy_paste: 0.0
+cfg: null
+tracker: botsort.yaml
+save_dir: runs\detect\train

runs/detect/train/results.csv

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+                  epoch,         train/box_loss,         train/cls_loss,         train/dfl_loss,   metrics/precision(B),      metrics/recall(B),       metrics/mAP50(B),    metrics/mAP50-95(B),           val/box_loss,           val/cls_loss,           val/dfl_loss,                 lr/pg0,                 lr/pg1,                 lr/pg2
+                      1,                 4.4059,                 7.2949,                 3.9288,                0.35655,                 0.0046,                0.00174,                0.00047,                 3.2456,                  5.315,                 3.2751,              6.057e-05,              6.057e-05,              6.057e-05
+                      2,                 3.1671,                 4.2795,                  2.623,                0.09093,                0.02468,                0.01277,                0.00496,                 2.8609,                 3.8934,                  2.029,             8.1229e-05,             8.1229e-05,             8.1229e-05
+                      3,                 2.7716,                 3.3153,                 1.8106,                0.11494,                0.02624,                0.01891,                0.00866,                 2.4415,                 3.1568,                 1.6992,             6.1847e-05,             6.1847e-05,             6.1847e-05

runs/detect/train17/args.yaml

@@ -0,0 +1,98 @@
+task: detect
+mode: train
+model: yolov8n.yaml
+data: data.yaml
+epochs: 600
+patience: 50
+batch: 16
+imgsz: 640
+save: true
+save_period: -1
+cache: false
+device: null
+workers: 8
+project: null
+name: null
+exist_ok: false
+pretrained: true
+optimizer: auto
+verbose: true
+seed: 0
+deterministic: true
+single_cls: false
+rect: false
+cos_lr: false
+close_mosaic: 10
+resume: false
+amp: true
+fraction: 1.0
+profile: false
+freeze: null
+overlap_mask: true
+mask_ratio: 4
+dropout: 0.0
+val: true
+split: val
+save_json: false
+save_hybrid: false
+conf: null
+iou: 0.7
+max_det: 300
+half: false
+dnn: false
+plots: true
+source: null
+show: false
+save_txt: false
+save_conf: false
+save_crop: false
+show_labels: true
+show_conf: true
+vid_stride: 1
+stream_buffer: false
+line_width: null
+visualize: false
+augment: false
+agnostic_nms: false
+classes: null
+retina_masks: false
+boxes: true
+format: torchscript
+keras: false
+optimize: false
+int8: false
+dynamic: false
+simplify: false
+opset: null
+workspace: 4
+nms: false
+lr0: 0.01
+lrf: 0.01
+momentum: 0.937
+weight_decay: 0.0005
+warmup_epochs: 3.0
+warmup_momentum: 0.8
+warmup_bias_lr: 0.1
+box: 7.5
+cls: 0.5
+dfl: 1.5
+pose: 12.0
+kobj: 1.0
+label_smoothing: 0.0
+nbs: 64
+hsv_h: 0.015
+hsv_s: 0.7
+hsv_v: 0.4
+degrees: 0.0
+translate: 0.1
+scale: 0.5
+shear: 0.0
+perspective: 0.0
+flipud: 0.0
+fliplr: 0.5
+mosaic: 1.0
+mixup: 0.0
+copy_paste: 0.0
+cfg: null
+tracker: botsort.yaml
+save_dir: runs\detect\train17