fix(replay): fix poetry lock, ulralytics.py, update ERROR_REPORTING_DSN

* fix dependencies, update ultralytics.py

* update ERROR_REPORTING_DSN

* black

Author: abrichr

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:
@@ -90,100 +85,35 @@ 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,
         retina_masks=retina_masks,
         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)
 
 

openadapt/config.py

@@ -126,7 +126,8 @@ class SegmentationAdapter(str, Enum):
     # Error reporting
     ERROR_REPORTING_ENABLED: bool = True
     ERROR_REPORTING_DSN: ClassVar = (
-        "https://dcf5d7889a3b4b47ae12a3af9ffcbeb7@app.glitchtip.com/3798"
+        # "https://dcf5d7889a3b4b47ae12a3af9ffcbeb7@app.glitchtip.com/3798"
+        "https://5d24fc5a2e674ea6b42275e5702499ce@app.glitchtip.com/8771",
     )
     ERROR_REPORTING_BRANCH: ClassVar = "main"