Init repo

.gitignore

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+.DS_Store
+__pycache__/
+datasets/
+runs/
+*.pt
+*.mp4

main.py

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+from ultralytics import YOLO
+
+# Load a model
+model = YOLO("yolov8n.pt")  # load a pretrained model (recommended for training)
+
+# Train the model
+results = model.train(data="./datasets/socker/socker.yaml", epochs=10, imgsz=1920)
+metrics = model.val()  # evaluate model performance on the validation set
+print(metrics)
+
+model.save("my_model.pt")  # save the model
+
+# Export the model
+path = model.export(format="onnx")
+print(path)

validation.py

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+import cv2
+from ultralytics import YOLO
+from ultralytics.utils.plotting import Annotator
+
+
+model = YOLO("my_model.pt")
+
+# Run inference
+results = model("datasets/socker/valid/images/img_307_jpeg.rf.8ee20e9d3ea38fa3a62c2495e7e824e3.jpg", imgsz=1920)
+print(results)
+
+# Draw bounding boxes on the image
+result = results[0]
+
+annotator = Annotator(result.orig_img, line_width=3)
+boxes = result.boxes
+for box in boxes:
+    b = box.xyxy[0]
+    c = box.cls
+    annotator.box_label(b, model.names[int(c)], color=(0, 0, 255))
+
+
+img = annotator.result()  
+cv2.imshow('YOLO V8 Detection', img)
+cv2.waitKey(0)

video.py

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+import numpy as np
+from ultralytics import YOLO
+import cv2
+import math
+
+cap = cv2.VideoCapture('CityUtdR video.mp4')
+
+if not cap.isOpened():
+    print("Error: Could not open video file.")
+    exit()
+
+# model
+model = YOLO('my_model.pt')
+
+# object classes
+classNames = ["ball", ]
+
+
+while True:
+    success, img = cap.read()
+    results = model(img, stream=True)
+
+    # coordinates
+    for r in results:
+        boxes = r.boxes
+
+        for box in boxes:
+            # bounding box
+            x1, y1, x2, y2 = box.xyxy[0]
+            x1, y1, x2, y2 = int(x1), int(y1), int(x2), int(y2) # convert to int values
+
+            # put box in cam
+            cv2.rectangle(img, (x1, y1), (x2, y2), (255, 0, 255), 3)
+
+            # object details
+            org = [x1, y1]
+            font = cv2.FONT_HERSHEY_SIMPLEX
+            fontScale = 1
+            color = (255, 0, 0)
+            thickness = 2
+
+            cv2.putText(img, classNames[np.argmax(box.cls)], org, font, fontScale, color, thickness)
+
+    cv2.imshow('Output', img)
+    if cv2.waitKey(1) == ord('q'):
+        break
+
+cap.release()
+cv2.destroyAllWindows()

we_used.txt

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+https://github.com/ultralytics/ultralytics/
+https://www.kaggle.com/datasets/michaelmortenson/football-soccer-ball-detection-dfl