Cyingyinging
commented
3 months ago I made changes in post_process.py.It can run:
!!! Output paths need to be optimized !!!
def save_crop_images(results, output_dir, overwrite=False): """ Save cropped images based on the detection bounding boxes.
Args:
results (list):
Detection results containing image ID and detections.
output_dir (str):
Directory to save the cropped images.
overwrite (bool):
Whether overwriting existing image folders. Default to False.
"""
# assert isinstance(results, list)
os.makedirs(output_dir, exist_ok=True)
with sv.ImageSink(target_dir_path=output_dir, overwrite=overwrite) as sink:
# for entry in results:
# print(entry)
for i, (xyxy, _, _, cat, _) in enumerate(results["detections"]):
cropped_img = sv.crop_image(
image=np.array(Image.open(results["img_id"]).convert("RGB")), xyxy=xyxy
)
sink.save_image(
image=cv2.cvtColor(cropped_img, cv2.COLOR_RGB2BGR),
image_name="{}_{}_{}".format(
int(cat), i, results["img_id"].rsplit(os.sep, 1)[1]
),
)
danielaruizl1
zhmiao
Search before asking
Bug
when i run the demo, I find a question : I can not save the crop image. The problem is: File "D:\anaconda3\envs\island_project\lib\site-packages\PytorchWildlife\utils\post_process.py", line 77, in save_crop_images assert isinstance(results, list) AssertionError. So, i checked error
Environment
No response
Minimal Reproducible Example
import numpy as np from PIL import Image import torch from torch.utils.data import DataLoader from PytorchWildlife.models import detection as pw_detection from PytorchWildlife.data import transforms as pw_trans from PytorchWildlife.data import datasets as pw_data from PytorchWildlife import utils as pw_utils import os
Setting the device to use for computations ('cuda' indicates GPU)
DEVICE = "cuda" if torch.cuda.is_available() else "cpu" detection_model = pw_detection.MegaDetectorV5(device=DEVICE, pretrained=True) tgt_img_path = os.path.join(".","10050028_1.JPG") img = np.array(Image.open(tgt_img_path).convert("RGB")) transform = pw_trans.MegaDetector_v5_Transform(target_size=detection_model.IMAGE_SIZE, stride=detection_model.STRIDE) results = detection_model.single_image_detection(transform(img), img.shape, tgt_img_path) pw_utils.save_detection_images(results, os.path.join(".","demo_output"), overwrite=False) pw_utils.save_crop_images(results, os.path.join(".","crop_output"))
Additional
No response
Are you willing to submit a PR?