diff --git a/openadapt/adapters/ultralytics.py b/openadapt/adapters/ultralytics.py index d6f236421..8a8194c4c 100644 --- a/openadapt/adapters/ultralytics.py +++ b/openadapt/adapters/ultralytics.py @@ -26,10 +26,9 @@ from ultralytics import FastSAM -from ultralytics.models.fastsam import FastSAMPrompt +from ultralytics.models.fastsam import FastSAMPredictor from ultralytics.models.sam import Predictor as SAMPredictor import fire -import numpy as np import ultralytics from openadapt import cache @@ -41,13 +40,11 @@ SAM_MODEL_NAMES = ( "sam_b.pt", # base "sam_l.pt", # large - # "mobile_sam.pt", ) MODEL_NAMES = FASTSAM_MODEL_NAMES + SAM_MODEL_NAMES DEFAULT_MODEL_NAME = MODEL_NAMES[0] -# TODO: rename def fetch_segmented_image( image: Image.Image, model_name: str = DEFAULT_MODEL_NAME, @@ -74,14 +71,12 @@ def fetch_segmented_image( def do_fastsam( image: Image, model_name: str, - # TODO: inject from config device: str = "cpu", retina_masks: bool = True, imgsz: int | tuple[int, int] | None = 1024, - # threshold below which boxes will be filtered out min_confidence_threshold: float = 0.4, - # discards all overlapping boxes with IoU > iou_threshold max_iou_threshold: float = 0.9, + max_det: int = 1000, max_retries: int = 5, retry_delay_seconds: float = 0.1, ) -> Image: @@ -90,22 +85,10 @@ def do_fastsam( For usage of thresholds see: github.com/ultralytics/ultralytics/blob/dacbd48fcf8407098166c6812eeb751deaac0faf /ultralytics/utils/ops.py#L164 - - Args: - TODO - min_confidence_threshold (float, optional): The minimum confidence score - that a detection must meet or exceed to be considered valid. Detections - below this threshold will not be marked. Defaults to 0.00. - max_iou_threshold (float, optional): The maximum allowed Intersection over - Union (IoU) value for overlapping detections. Detections that exceed this - IoU threshold are considered for suppression, keeping only the - detection with the highest confidence. Defaults to 0.05. """ model = FastSAM(model_name) - imgsz = imgsz or image.size - # Run inference on image everything_results = model( image, device=device, @@ -113,77 +96,24 @@ def do_fastsam( imgsz=imgsz, conf=min_confidence_threshold, iou=max_iou_threshold, + max_det=max_det, ) - - # Prepare a Prompt Process object - prompt_process = FastSAMPrompt(image, everything_results, device="cpu") - - # Everything prompt - annotations = prompt_process.everything_prompt() - - # TODO: support other modes once issues are fixed - # https://github.com/ultralytics/ultralytics/issues/13218#issuecomment-2142960103 - - # Bbox default shape [0,0,0,0] -> [x1,y1,x2,y2] - # annotations = prompt_process.box_prompt(bbox=[200, 200, 300, 300]) - - # Text prompt - # annotations = prompt_process.text_prompt(text='a photo of a dog') - - # Point prompt - # points default [[0,0]] [[x1,y1],[x2,y2]] - # point_label default [0] [1,0] 0:background, 1:foreground - # annotations = prompt_process.point_prompt(points=[[200, 200]], pointlabel=[1]) - - assert len(annotations) == 1, len(annotations) - annotation = annotations[0] - - # hide original image - annotation.orig_img = np.ones(annotation.orig_img.shape) - - # TODO: in memory, e.g. with prompt_process.fast_show_mask() - with TemporaryDirectory() as tmp_dir: - # Force the output format to PNG to prevent JPEG compression artefacts - annotation.path = annotation.path.replace(".jpg", ".png") - prompt_process.plot( - [annotation], - tmp_dir, - with_contours=False, - retina=False, + assert len(everything_results) == 1, len(everything_results) + annotation = everything_results[0] + + segmented_image = Image.fromarray( + annotation.plot( + img=np.ones(annotation.orig_img.shape, dtype=annotation.orig_img.dtype), + kpt_line=False, + labels=False, + boxes=False, + probs=False, + color_mode="instance", ) - result_name = os.path.basename(annotation.path) - logger.info(f"{annotation.path=}") - segmented_image_path = Path(tmp_dir) / result_name - segmented_image = Image.open(segmented_image_path) - - # Ensure the image is fully loaded before deletion to avoid errors or incomplete operations, - # as some operating systems and file systems lock files during read or processing. - segmented_image.load() - - # Attempt to delete the file with retries and delay - retries = 0 - - while retries < max_retries: - try: - os.remove(segmented_image_path) - break # If deletion succeeds, exit loop - except OSError as e: - if e.errno == errno.ENOENT: # File not found - break - else: - retries += 1 - time.sleep(retry_delay_seconds) - - if retries == max_retries: - logger.warning(f"Failed to delete {segmented_image_path}") - # Check if the dimensions of the original and segmented images differ - # XXX TODO this is a hack, this plotting code should be refactored, but the - # bug may exist in ultralytics, since they seem to resize as well; see: - # https://github.com/ultralytics/ultralytics/blob/main/ultralytics/utils/plotting.py#L238 - # https://github.com/ultralytics/ultralytics/issues/561#issuecomment-1403079910 + ) + if image.size != segmented_image.size: logger.warning(f"{image.size=} != {segmented_image.size=}, resizing...") - # Resize segmented_image to match original using nearest neighbor interpolation segmented_image = segmented_image.resize(image.size, Image.NEAREST) assert image.size == segmented_image.size, (image.size, segmented_image.size) @@ -194,7 +124,6 @@ def do_fastsam( def do_sam( image: Image.Image, model_name: str, - # TODO: add params ) -> Image.Image: # Create SAMPredictor overrides = dict( @@ -207,20 +136,7 @@ def do_sam( predictor = SAMPredictor(overrides=overrides) # Segment with additional args - # results = predictor(source=image, crop_n_layers=1, points_stride=64) - results = predictor( - source=image, - # crop_n_layers=3, - # crop_overlap_ratio=0.5, - # crop_downscale_factor=1, - # point_grids=None, - # points_stride=12, - # points_batch_size=128, - # conf_thres=0.8, - # stability_score_thresh=0.95, - # stability_score_offset=0.95, - # crop_nms_thresh=0.8, - ) + results = predictor(source=image) mask_ims = results_to_mask_images(results) segmented_image = colorize_masks(mask_ims) return segmented_image @@ -238,8 +154,7 @@ def results_to_mask_images( def colorize_masks(masks: list[Image.Image]) -> Image.Image: - """ - Takes a list of PIL images containing binary masks and returns a new PIL.Image + """Takes a list of PIL images containing binary masks and returns a new PIL.Image where each mask is colored differently using a unique color for each mask. Args: @@ -249,15 +164,11 @@ def colorize_masks(masks: list[Image.Image]) -> Image.Image: PIL.Image: A new image with each mask in a different color. """ if not masks: - return None # Return None if the list is empty + return None - # Assuming all masks are the same size, get dimensions width, height = masks[0].size - - # Create an empty array with 3 color channels (RGB) result_image = np.zeros((height, width, 3), dtype=np.uint8) - # Generate unique colors using HSV color space num_masks = len(masks) colors = [ tuple( @@ -271,17 +182,12 @@ def colorize_masks(masks: list[Image.Image]) -> Image.Image: ] for idx, mask in enumerate(masks): - # Convert PIL Image to numpy array mask_array = np.array(mask) - - # Apply the color to the mask for c in range(3): - # Only colorize where the mask is True (assuming mask is binary: 0 or 255) result_image[:, :, c] += (mask_array / 255 * colors[idx][c]).astype( np.uint8 ) - # Convert the result back to a PIL image return Image.fromarray(result_image) diff --git a/openadapt/config.py b/openadapt/config.py index 432c44e5f..908a3cc1c 100644 --- a/openadapt/config.py +++ b/openadapt/config.py @@ -126,7 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