下面是整理后的代碼，將各個步驟清晰地分段處理，并更新了模型權(quán)重的路徑為本地文件路徑：

# Import required libraries
from detectron2.engine import DefaultPredictor
from detectron2.config import get_cfg
from detectron2.utils.visualizer import Visualizer
from detectron2.data import MetadataCatalog
import cv2

# Load the image
im = cv2.imread("./tmp/input.jpg")

# Configure the model
cfg = get_cfg()
cfg.merge_from_file("configs/COCO-Detection/faster_rcnn_R_101_FPN_3x.yaml")
cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST = 0.5  # Set the threshold for object detection
cfg.MODEL.WEIGHTS = "models/model_final_f6e8b1.pkl"  # Path to the local model file

# Create predictor
predictor = DefaultPredictor(cfg)

# Make prediction
outputs = predictor(im)

# Visualize the output (optional)
# Uncomment the following lines if you want to visualize and save the result
v = Visualizer(im[:, :, ::-1], MetadataCatalog.get(cfg.DATASETS.TRAIN[0]), scale=1.2)
out = v.draw_instance_predictions(outputs["instances"].to("cpu"))
# cv2.imshow("Predictions", out.get_image()[:, :, ::-1])
# cv2.waitKey(0)  # Display until a key is pressed
cv2.imwrite("./tmp/output.ObjectDetection.jpg", out.get_image()[:, :, ::-1])  # Save the output

說明

• cfg.MODEL.WEIGHTS 已更新為 models/model_final_f6e8b1.pkl，請確保此路徑和文件名與本地文件匹配。
• 如果需要可視化預(yù)測結(jié)果，可取消最后幾行的注釋。
• 確保在代碼運行時已安裝并配置好 detectron2 環(huán)境。

識別效果：

output.jpg