Model detection

detect.py

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+from ultralytics import YOLO
+from pathlib import Path
+from tqdm import tqdm
+
+import os
+import cv2
+
+
+if __name__ == '__main__':
+    model_path = str(Path.cwd()) + r'/Model/Model_Data/runs/detect/Yolov8/weights/best.pt'
+
+    img_path = str(Path.cwd()) + r'/Sample Images'
+    save_img_path = str(Path.cwd()) + r'/Results'
+
+    CONF = 0.5
+    IOU = 0.75
+    CLASSES = None # [or list]
+
+    SAVE = False
+    SAVE_TXT = False
+    SHOW_LABELS = True
+    SHOW_CONF = True
+    SHOW_BOXES = True
+    LINE_WIDTH = 2
+
+    # Supported formats
+    # https://docs.ultralytics.com/modes/predict/#images
+
+    model = YOLO(model_path)
+
+    if not os.path.isdir(save_img_path):
+        os.mkdir(save_img_path)
+
+    img_list = os.listdir(img_path)
+    for i in tqdm(range(len(img_list))):
+        SOURCE = img_path + '/' + img_list[i]
+
+        image = cv2.imread(SOURCE)
+        IMGSZ = (image.shape[0], image.shape[1])
+
+        results = model.predict(
+            source=SOURCE,
+            imgsz=IMGSZ,
+            conf=CONF,
+            iou=IOU,
+            classes=CLASSES,
+            save=SAVE,
+            save_txt=SAVE_TXT,
+            show_conf=SHOW_CONF,
+            show_labels=SHOW_LABELS,
+            show_boxes=SHOW_BOXES,
+            line_width=LINE_WIDTH
+        )
+
+        for r in results:
+            arr = r.plot()
+            cv2.imwrite(save_img_path + '/' + img_list[i], arr)