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from transformers import AutoModelForObjectDetection, AutoImageProcessor
from torch.utils.data import Dataset, DataLoader
import os
from tqdm import tqdm
from PIL import Image
from pathlib import Path
from ultralytics.models.fastsam import FastSAMPredictor
import supervision as sv
import torch
import numpy as np
import cv2
from typing import List, Tuple, Dict, Any, Optional
from supervision.dataset.utils import approximate_mask_with_polygons
from supervision.detection.utils import (
contains_holes,
contains_multiple_segments,
)
detector = AutoModelForObjectDetection.from_pretrained("Yifeng-Liu/rt-detr-finetuned-for-satellite-image-roofs-detection")
detector_processor = AutoImageProcessor.from_pretrained("Yifeng-Liu/rt-detr-finetuned-for-satellite-image-roofs-detection")
overrides = dict(conf=0.25, task="segment", mode="predict", model="FastSAM-x.pt", save=False)
segment_predictor = FastSAMPredictor(overrides=overrides)
# IMG_FORMATS = {"bmp", "dng", "jpeg", "jpg", "mpo", "png", "tif", "tiff", "webp", "pfm"} # image suffixes
class ImageInferenceDataset(Dataset):
def __init__(self, image_paths: Path, image_processor):
"""
A custom dataset class for image inference without annotations or masks.
Args:
image_folder (Path): The path to the folder containing images.
image_processor: A callable for processing images (usually a transformer or feature extractor).
image_formats (set): A set of supported image formats to be filtered.
"""
self.image_processor = image_processor
# Filter out files that are not supported image formats
self.image_files = image_paths
def __len__(self) -> int:
return len(self.image_files)
def __getitem__(self, idx: int) -> Tuple[torch.Tensor, str]:
"""
Get an image from the dataset at the specified index.
Args:
idx (int): The index of the image.
Returns:
Tuple[torch.Tensor, str]: A tuple containing the processed image tensor and the image file path.
"""
image_path = self.image_files[idx]
# Open image using PIL and process it using the provided image processor
with Image.open(image_path) as img:
orig_size = img.size
img = img.convert("RGB") # Ensure all images are in RGB format for consistency
processed_img = self.image_processor(images=img, return_tensors="pt")["pixel_values"].squeeze(0)
return processed_img, str(image_path), orig_size
def collate_fn_inference(batch: List[Tuple[torch.Tensor, str]]) -> dict:
"""
Collate function for batching images for inference.
Args:
batch (List[Tuple[torch.Tensor, str]]): A list of tuples where each tuple contains
the processed image tensor and image path.
Returns:
dict: A dictionary containing the batched image tensors and corresponding image file paths.
"""
pixel_values = [item[0] for item in batch] # Extract processed images
image_paths = [item[1] for item in batch] # Extract image paths
orig_sizes = [item[2] for item in batch]
# Pad the images to match the largest image in the batch
encoding = detector_processor.pad(pixel_values, return_tensors="pt")
return {
'pixel_values': encoding['pixel_values'],
'pixel_mask': encoding['pixel_mask'], # Padding mask (if needed by the model)
'image_paths': image_paths,
'orig_sizes': orig_sizes
}
class ModelInference:
def __init__(self, detector, detector_processor, segment_predictor, id2label, CONFIDENCE_TRESHOLD):
self.detector = detector
self.detector_processor = detector_processor
self.segment_predictor = segment_predictor
self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
self.CONFIDENCE_TRESHOLD = CONFIDENCE_TRESHOLD
self.id2label = id2label
self.mask_annotator = sv.MaskAnnotator()
self.detector.to(self.device)
def predict_one(self, image_path):
image = cv2.imread(image_path)
with torch.no_grad():
# load image and predict
inputs = self.detector_processor(images=image, return_tensors='pt').to(self.device)
outputs = self.detector(**inputs)
# post-process
target_sizes = torch.tensor([image.shape[:2]]).to(self.device)
results = detector_processor.post_process_object_detection(
outputs=outputs,
threshold=self.CONFIDENCE_TRESHOLD,
target_sizes=target_sizes
)[0]
if results['boxes'].numel() == 0:
print("No bounding box detected")
return None, None
else:
det_detections = sv.Detections.from_transformers(transformers_results=results).with_nms(threshold=0.5)
everything_results = self.segment_predictor(image)
if everything_results[0].masks is not None:
bbox_results = self.segment_predictor.prompt(everything_results, det_detections.xyxy.tolist())[0]
seg_detections = sv.Detections.from_ultralytics(bbox_results)
seg_detections = self.filter_small_masks(seg_detections)
max_length = max(len(name) for name in self.id2label.values())
# Create a new NumPy array with the appropriate dtype based on the longest string
seg_detections.data['class_name'] = np.array(seg_detections.data['class_name'], dtype=f'<U{max_length}')
for idx, class_name in enumerate(seg_detections.data['class_name']):
if class_name == 'object':
seg_detections.data['class_name'][idx] = self.id2label[seg_detections.class_id[idx]]
annotated_frame = image.copy()
annotated_frame = self.mask_annotator.annotate(scene=annotated_frame, detections=seg_detections)
return seg_detections, annotated_frame
else:
print("No segmentation mask generated")
return None, None
def predict_folder(self, image_paths, batch_size=4):
dataset = ImageInferenceDataset(image_paths=image_paths, image_processor=detector_processor)
# Create DataLoader instance with the custom collate function
dataloader = DataLoader(dataset, batch_size=batch_size, collate_fn=collate_fn_inference)
detector_failed_list = []
segmentor_failed_list = []
id2label = {0: 'building'}
max_length = max(len(name) for name in id2label.values())
all_image_paths = []
all_results = []
for idx, batch in enumerate(tqdm(dataloader)):
pixel_values = batch["pixel_values"].to(self.device)
pixel_mask = batch["pixel_mask"].to(self.device)
image_paths = batch["image_paths"]
orig_sizes = batch["orig_sizes"]
orig_target_sizes = torch.tensor(orig_sizes, device=self.device)
with torch.no_grad():
outputs = self.detector(
pixel_values=pixel_values, pixel_mask=pixel_mask)
# orig_target_sizes = torch.stack([target["orig_size"] for target in labels], dim=0)
detector_results = detector_processor.post_process_object_detection(
outputs,
target_sizes=orig_target_sizes)
detector_detections = []
detector_to_remove = []
for idx, detector_result in enumerate(detector_results):
if detector_result['boxes'].numel() == 0:
# The tensor is empty
detector_to_remove.append(idx)
else:
detector_detections.append(sv.Detections.from_transformers(transformers_results=detector_result))
if detector_to_remove is not None:
# Remove items from detector_results and image_ids by reversing the indices to avoid index shifting
for idx in sorted(detector_to_remove, reverse=True):
detector_failed_list.append(image_paths[idx])
del image_paths[idx]
images_raw = [cv2.imread(image_path) for image_path in image_paths]
boxes = [detections.xyxy.tolist() for detections in detector_detections]
results = []
to_remove_seg = []
for idx, (image_path, image, box) in enumerate(zip(image_paths, images_raw, boxes)):
try:
with torch.no_grad():
# segmentation_result = segment_model(image, bboxes=box)[0]
everything_results = self.segment_predictor(image)
if everything_results[0].masks is not None:
bbox_results = self.segment_predictor.prompt(everything_results, box)[0]
seg_detections = sv.Detections.from_ultralytics(bbox_results)
seg_detections = self.filter_small_masks(seg_detections)
seg_detections.data['class_name'] = np.array(seg_detections.data['class_name'], dtype=f'<U{max_length}')
for idx, class_name in enumerate(seg_detections.data['class_name']):
if class_name == 'object':
seg_detections.data['class_name'][idx] = id2label[seg_detections.class_id[idx]]
results.append(seg_detections)
else:
to_remove_seg.append(idx)
except Exception as e:
print(f"An error occurred: {e}")
print(f"box: {box}")
print(f"image id: {image_path}")
# result = sv.Detections.from_ultralytics(segmentation_result)
# results.append(result)
if to_remove_seg is not None:
for idx in sorted(to_remove_seg, reverse=True):
segmentor_failed_list.append(image_paths[idx])
del image_paths[idx]
if len(results) != len(image_paths):
print(f"Length of results ({len(results)}) does not match the length of image_ids ({len(image_paths)})")
continue
all_image_paths.extend(image_paths)
all_results.extend(results)
annotated_frame = cv2.imread(all_image_paths[0]).copy()
annotated_frame = self.mask_annotator.annotate(scene=annotated_frame, detections=all_results[0])
return all_image_paths, all_results, annotated_frame, detector_failed_list, segmentor_failed_list
def filter_small_masks(self, detections: sv.Detections) -> sv.Detections:
valid_indices = []
min_image_area_percentage = 0.002
max_image_area_percentage = 0.80
approximation_percentage = 0.75
for i, mask in enumerate(detections.mask):
# Check for structural issues in the mask
if not (contains_holes(mask) or contains_multiple_segments(mask)):
# Check if the mask can be approximated to a polygon successfully
if not approximate_mask_with_polygons(mask=mask,
min_image_area_percentage=min_image_area_percentage,
max_image_area_percentage=max_image_area_percentage,
approximation_percentage=approximation_percentage,
):
print(f"Skipping mask {i} due to structural issues")
continue
# If all checks pass, add index to valid_indices
valid_indices.append(i)
filtered_xyxy = detections.xyxy[valid_indices]
filtered_mask = detections.mask[valid_indices]
filtered_confidence = detections.confidence[valid_indices]
filtered_class_id = detections.class_id[valid_indices]
filtered_class_name = detections.data['class_name'][valid_indices]
detections.xyxy = filtered_xyxy
detections.mask = filtered_mask
detections.confidence = filtered_confidence
detections.class_id = filtered_class_id
detections.data['class_name'] = filtered_class_name
return detections
def get_dict(
self,
image_paths: List[Any],
detections: List[Any]
) -> Dict[str, Any]:
detections_dict = {}
for idx, image_path in enumerate(image_paths):
detections_dict[image_path] = detections[idx]
return detections_dict
def save_annotations(self,
image_paths,
detections,
class_names,
annotation_path,
MIN_IMAGE_AREA_PERCENTAGE=0.002,
MAX_IMAGE_AREA_PERCENTAGE=0.80,
APPROXIMATION_PERCENTAGE=0.75):
# image_dir = annotation_path.parent
detections_dict = self.get_dict(image_paths, detections)
sv.DetectionDataset(
classes=class_names,
images=image_paths,
annotations=detections_dict
).as_coco(
images_directory_path=None,
annotations_path=annotation_path,
min_image_area_percentage=MIN_IMAGE_AREA_PERCENTAGE,
max_image_area_percentage=MAX_IMAGE_AREA_PERCENTAGE,
approximation_percentage=APPROXIMATION_PERCENTAGE
)
return
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