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"""
Main pipeline class for the Sorghum Plant Phenotyping Pipeline.
This module orchestrates the entire pipeline from data loading
to feature extraction and result output.
"""
import os
import subprocess
import logging
from pathlib import Path
from typing import Dict, Any, Optional, List, Set
import numpy as np
import cv2
import torch
from torchvision import transforms
from transformers import AutoModelForImageSegmentation
from sklearn.decomposition import PCA
try:
from tqdm import tqdm
except Exception:
tqdm = None
from .config import Config
from .data import DataLoader, ImagePreprocessor, MaskHandler
from .features import TextureExtractor, VegetationIndexExtractor, MorphologyExtractor
from .output import OutputManager
from .segmentation import SegmentationManager
# Make occlusion handling optional if the module is not present
try:
from .segmentation.occlusion_handler import OcclusionHandler # type: ignore
except Exception:
OcclusionHandler = None # type: ignore
class SorghumPipeline:
"""
Main pipeline class for sorghum plant phenotyping.
This class orchestrates the entire pipeline from data loading
to feature extraction and result output.
"""
def __init__(self, config_path: Optional[str] = None, config: Optional[Config] = None, include_ignored: bool = False, enable_occlusion_handling: bool = False, enable_instance_integration: bool = False, strict_loader: bool = False, excluded_dates: Optional[List[str]] = None):
"""
Initialize the pipeline.
Args:
config_path: Path to configuration file
config: Configuration object (if not using file)
include_ignored: Whether to include ignored plants
enable_occlusion_handling: Whether to enable SAM2Long occlusion handling
"""
# Setup logging
self._setup_logging()
# Load configuration
if config is not None:
self.config = config
elif config_path is not None:
self.config = Config(config_path)
else:
raise ValueError("Either config_path or config must be provided")
# Validate configuration
self.config.validate()
# Store settings
self.enable_occlusion_handling = enable_occlusion_handling
self.enable_instance_integration = enable_instance_integration
self.strict_loader = strict_loader
self.excluded_dates = excluded_dates or []
# Initialize components
self._initialize_components(include_ignored)
logger.info("Sorghum Pipeline initialized successfully")
def _setup_logging(self):
"""Setup logging configuration."""
logging.basicConfig(
level=logging.INFO,
format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
handlers=[
logging.StreamHandler(),
logging.FileHandler('sorghum_pipeline.log')
]
)
global logger
logger = logging.getLogger(__name__)
def _initialize_components(self, include_ignored: bool = False):
"""Initialize all pipeline components."""
# Data components
self.data_loader = DataLoader(
input_folder=self.config.paths.input_folder,
debug=True,
include_ignored=include_ignored,
strict_loader=self.strict_loader,
excluded_dates=self.excluded_dates,
)
self.preprocessor = ImagePreprocessor(
target_size=self.config.processing.target_size
)
self.mask_handler = MaskHandler(
min_area=self.config.processing.min_component_area,
kernel_size=self.config.processing.morphology_kernel_size
)
# Feature extractors
self.texture_extractor = TextureExtractor(
lbp_points=self.config.processing.lbp_points,
lbp_radius=self.config.processing.lbp_radius,
hog_orientations=self.config.processing.hog_orientations,
hog_pixels_per_cell=self.config.processing.hog_pixels_per_cell,
hog_cells_per_block=self.config.processing.hog_cells_per_block,
lacunarity_window=self.config.processing.lacunarity_window,
ehd_threshold=self.config.processing.ehd_threshold,
angle_resolution=self.config.processing.angle_resolution
)
self.vegetation_extractor = VegetationIndexExtractor(
epsilon=self.config.processing.epsilon,
soil_factor=self.config.processing.soil_factor
)
self.morphology_extractor = MorphologyExtractor(
pixel_to_cm=self.config.processing.pixel_to_cm,
prune_sizes=self.config.processing.prune_sizes
)
# Segmentation
self.segmentation_manager = SegmentationManager(
model_name=self.config.model.model_name,
device=self.config.get_device(),
threshold=self.config.processing.segmentation_threshold,
trust_remote_code=self.config.model.trust_remote_code,
cache_dir=self.config.model.cache_dir if getattr(self.config.model, 'cache_dir', '') else None,
local_files_only=getattr(self.config.model, 'local_files_only', False),
)
# Occlusion handling (optional)
self.occlusion_handler = None
if self.enable_occlusion_handling and OcclusionHandler is not None:
try:
self.occlusion_handler = OcclusionHandler(
device=self.config.get_device(),
model="tiny", # Can be made configurable
confidence_threshold=0.5,
iou_threshold=0.1
)
logger.info("Occlusion handler initialized successfully")
except Exception as e:
logger.warning(f"Failed to initialize occlusion handler: {e}")
logger.warning("Continuing without occlusion handling")
self.occlusion_handler = None
elif self.enable_occlusion_handling and OcclusionHandler is None:
logger.warning("Occlusion handler module not found; continuing without occlusion handling")
# Output manager
self.output_manager = OutputManager(
output_folder=self.config.paths.output_folder,
settings=self.config.output
)
def _free_gpu_memory_before_instance(self) -> None:
"""Attempt to free GPU memory prior to running SAM2Long in a subprocess.
- Moves BRIA segmentation model to CPU if present
- Deletes the model reference to release VRAM
- Calls torch.cuda.empty_cache()
"""
try:
import torch as _torch # type: ignore
# Move BRIA model to CPU and drop reference
try:
if getattr(self, 'segmentation_manager', None) is not None:
mdl = getattr(self.segmentation_manager, 'model', None)
if mdl is not None:
try:
mdl.to('cpu')
except Exception:
pass
try:
delattr(self.segmentation_manager, 'model')
except Exception:
pass
# Ensure attribute exists but is None for future checks
try:
self.segmentation_manager.model = None # type: ignore
except Exception:
pass
except Exception:
pass
# Free CUDA cache
try:
if _torch.cuda.is_available():
_torch.cuda.empty_cache()
except Exception:
pass
logger.info("Freed GPU memory before SAM2Long invocation (moved BRIA to CPU and emptied cache)")
except Exception as e:
logger.warning(f"Failed to free GPU memory before instance segmentation: {e}")
def run(self, load_all_frames: bool = False, segmentation_only: bool = False, filter_plants: Optional[List[str]] = None, filter_frames: Optional[List[str]] = None, run_instance_segmentation: bool = False, features_frame_only: Optional[int] = None, reuse_instance_results: bool = False, instance_mapping_path: Optional[str] = None, force_reprocess: bool = False, respect_instance_frame_rules_for_features: bool = False, substitute_feature_image_from_instance_src: bool = False) -> Dict[str, Any]:
"""
Run the complete pipeline.
Args:
load_all_frames: Whether to load all frames or selected frames
segmentation_only: If True, run segmentation only and skip feature extraction
Returns:
Dictionary containing all results
"""
logger.info("Starting Sorghum Pipeline...")
try:
import time
total_start = time.perf_counter()
# Step 1: Load data
logger.info("Step 1/6: Loading data...")
# In reuse mode we need all frames to select the mapped frame per plant
if reuse_instance_results:
plants = self.data_loader.load_all_frames()
else:
# If specific frames are requested, we must load all frames to filter correctly
if load_all_frames or (filter_frames is not None and len(filter_frames) > 0):
plants = self.data_loader.load_all_frames()
else:
plants = self.data_loader.load_selected_frames()
# Optional filter by specific plant names (e.g., ["plant1"])
if filter_plants:
allowed = set(filter_plants)
plants = {
key: pdata for key, pdata in plants.items()
if len(key.split('_')) > 3 and key.split('_')[3] in allowed
}
# Optional filter by specific frame numbers (e.g., ["9"] or ["frame9"])
if filter_frames:
# Normalize to 'frameX' tokens
wanted = set(
[f if str(f).startswith('frame') else f"frame{str(f)}" for f in filter_frames]
)
plants = {
key: pdata for key, pdata in plants.items()
if key.split('_')[-1] in wanted
}
if not plants:
raise ValueError("No plant data loaded")
logger.info(f"Loaded {len(plants)} plants")
# If reusing instance results with mapping, restrict to exactly the mapped frame per plant (default frame8)
if reuse_instance_results:
try:
import json as _json
if instance_mapping_path is None:
raise ValueError("instance_mapping_path is required in reuse mode")
_map = _json.load(open(instance_mapping_path, 'r'))
# Normalize mapping plant keys and compute target frame (default 8)
target_frame_by_plant = {}
for pk, pv in _map.items():
k_norm = pk if str(pk).startswith('plant') else f"plant{int(pk)}" if str(pk).isdigit() else str(pk)
try:
target_frame_by_plant[k_norm] = int(pv.get('frame', 8))
except Exception:
target_frame_by_plant[k_norm] = 8
before = len(plants)
plants = {
key: pdata for key, pdata in plants.items()
if (len(key.split('_')) > 3 and key.split('_')[3] in target_frame_by_plant
and key.split('_')[-1] == f"frame{target_frame_by_plant[key.split('_')[3]]}")
}
logger.info(f"Restricted loaded data by mapping frames: {before} -> {len(plants)} items")
except Exception as e:
logger.warning(f"Failed to restrict loaded data by mapping frames: {e}")
# Skip plants that already have saved results (unless force_reprocess)
if not force_reprocess:
try:
before = len(plants)
filtered = {}
for key, pdata in plants.items():
parts = key.split('_')
if len(parts) < 5:
filtered[key] = pdata
continue
date_key = "_".join(parts[:3])
plant_name = parts[3]
plant_dir = Path(self.config.paths.output_folder) / date_key / plant_name
meta_ok = (plant_dir / 'metadata.json').exists()
seg_mask_ok = (plant_dir / self.config.output.segmentation_dir / 'mask.png').exists()
if meta_ok or seg_mask_ok:
continue
filtered[key] = pdata
plants = filtered
logger.info(f"Skip-existing filter: {before} -> {len(plants)} items to process")
except Exception as e:
logger.warning(f"Skip-existing filter failed: {e}")
# Pre-segmentation borrowing: use plant12 images for plant13 from the start
try:
rewired = 0
borrow_map: Dict[str, str] = {
'plant13': 'plant12',
'plant14': 'plant13',
'plant15': 'plant14',
'plant16': 'plant15',
}
for _k in list(plants.keys()):
_parts = _k.split('_')
# Expect keys like YYYY_MM_DD_plantX_frameY
if len(_parts) < 5:
continue
_date_key = "_".join(_parts[:3])
_plant_name = _parts[3]
_frame_token = _parts[4]
# Do NOT borrow on 2025_05_08
if _date_key == '2025_05_08':
continue
if _plant_name not in borrow_map:
continue
_src_plant = borrow_map[_plant_name]
_src_key = f"{_date_key}_{_src_plant}_{_frame_token}"
_src = plants.get(_src_key)
if not _src:
# Fallback: load raw image for source plant directly from disk
try:
from PIL import Image as _Image
_date_folder = _date_key.replace('_', '-')
_frame_num = int(_frame_token.replace('frame', ''))
_date_dir = Path(self.config.paths.input_folder)
# If input folder is a parent of dates, append date folder
if _date_dir.name != _date_folder:
_date_dir = _date_dir / _date_folder
_frame_path = _date_dir / _src_plant / f"{_src_plant}_frame{_frame_num}.tif"
if _frame_path.exists():
_img = _Image.open(str(_frame_path))
_src = {"raw_image": (_img, _frame_path.name), "plant_name": _plant_name, "file_path": str(_frame_path)}
else:
_src = None
except Exception:
_src = None
if not _src:
continue
_tgt = plants[_k]
# Preserve original raw image once
if 'raw_image' in _tgt and 'raw_image_original' not in _tgt:
_tgt['raw_image_original'] = _tgt['raw_image']
if 'raw_image' in _src:
_tgt['raw_image'] = _src['raw_image']
_tgt['borrowed_from'] = _src_plant
rewired += 1
if rewired > 0:
logger.info(f"Pre-seg borrowing applied: rewired {rewired} frames for plants 13/14/15/16")
except Exception as e:
logger.warning(f"Pre-seg borrowing failed: {e}")
# Step 2: Create composites
logger.info("Step 2/6: Creating composites...")
step_start = time.perf_counter()
plants = self.preprocessor.create_composites(plants)
logger.info(f"Composites done in {(time.perf_counter()-step_start):.2f}s")
# Step 3: Segment plants (optionally with bounding boxes)
logger.info("Step 3/6: Segmenting plants...")
step_start = time.perf_counter()
bbox_lookup = None
try:
bbox_dir = getattr(self.config.paths, 'boundingbox_dir', None)
# Default to project BoundingBox dir if unset or falsy
if not bbox_dir:
try:
self.config.paths.boundingbox_dir = "/home/grads/f/fahimehorvatinia/Documents/my_full_project/BoundingBox"
bbox_dir = self.config.paths.boundingbox_dir
except Exception:
bbox_dir = None
if bbox_dir:
bbox_lookup = self.data_loader.load_bounding_boxes(bbox_dir)
logger.info(f"Loaded bounding boxes from {bbox_dir}")
except Exception as e:
logger.warning(f"Failed to load bounding boxes: {e}")
bbox_lookup = None
plants = self._segment_plants(plants, bbox_lookup)
logger.info(f"Segmentation done in {(time.perf_counter()-step_start):.2f}s")
# Step 3.5: Handle occlusion if enabled
if self.enable_occlusion_handling and self.occlusion_handler is not None:
logger.info("Step 3.5/6: Handling occlusion with SAM2Long...")
step_start = time.perf_counter()
plants = self._handle_occlusion(plants)
logger.info(f"Occlusion handling done in {(time.perf_counter()-step_start):.2f}s")
# Optional: Export RMBG maskouts with white background and run instance segmentation
if (run_instance_segmentation or self.enable_instance_integration) and not reuse_instance_results:
if not load_all_frames:
logger.warning("Instance segmentation expects all 13 frames; consider running with load_all_frames=True.")
logger.info("Step 3.6: Exporting white-background RMBG images for instance segmentation...")
# Derive date-specific export/result directories when a single date is present
date_keys = set()
try:
for _k in plants.keys():
_p = _k.split('_')
if len(_p) >= 3:
date_keys.add("_".join(_p[:3]))
except Exception:
pass
if len(date_keys) == 1:
date_key = next(iter(date_keys))
base_dir = Path(self.config.paths.output_folder) / date_key
export_dir = base_dir / "instance_input_maskouts"
instance_results_dir = base_dir / "instance_results"
else:
export_dir = Path(self.config.paths.output_folder) / "instance_input_maskouts"
instance_results_dir = Path(self.config.paths.output_folder) / "instance_results"
export_dir.mkdir(parents=True, exist_ok=True)
instance_results_dir.mkdir(parents=True, exist_ok=True)
self._export_white_background_maskouts(plants, export_dir)
logger.info("Invoking final SAM2Long instance segmentation on exported images...")
# Free GPU memory before launching SAM2Long to avoid CUDA OOM
self._free_gpu_memory_before_instance()
env = os.environ.copy()
env["SAM2LONG_IMAGES_DIR"] = str(export_dir)
env["SAM2LONG_RESULTS_DIR"] = str(instance_results_dir)
# Ensure instance outputs include all frames for all dates
try:
env.pop("INSTANCE_OUTPUT_FRAMES", None)
except Exception:
pass
script_path = "/home/grads/f/fahimehorvatinia/Documents/my_full_project/Experiments3_code/sam2long_instance_integration.py"
try:
subprocess.run(["python", script_path], check=True, env=env)
except subprocess.CalledProcessError as e:
logger.error(f"Instance segmentation failed: {e}")
else:
# Integrate instance masks (track_0 as target) into pdata before feature extraction
try:
self._apply_instance_masks(plants, instance_results_dir)
logger.info("Applied instance segmentation masks to pipeline data")
except Exception as e:
logger.warning(f"Failed to apply instance masks: {e}")
elif reuse_instance_results:
# Reuse existing instance masks from mapping file
if instance_mapping_path is None:
raise ValueError("reuse_instance_results=True requires instance_mapping_path to be provided")
try:
self._apply_instance_masks_from_mapping(plants, Path(instance_mapping_path))
logger.info("Applied instance masks from mapping file")
except Exception as e:
logger.error(f"Failed to apply instance masks from mapping: {e}")
if not segmentation_only:
# If reusing instance results with a mapping, restrict features to mapped frames per plant
if reuse_instance_results and instance_mapping_path is not None:
try:
import json as _json
_map = _json.load(open(instance_mapping_path, 'r'))
# Normalize map
_norm = {}
for pk, pv in _map.items():
k_norm = pk if str(pk).startswith('plant') else f"plant{int(pk)}" if str(pk).isdigit() else str(pk)
_norm[k_norm] = int(pv.get('frame', 8))
before = len(plants)
plants = {
k: v for k, v in plants.items()
if len(k.split('_')) > 3 and k.split('_')[3] in _norm and k.split('_')[-1] == f"frame{_norm[k.split('_')[3]]}"
}
logger.info(f"Restricted feature extraction by mapping: {before} -> {len(plants)} items")
except Exception as e:
logger.warning(f"Failed to restrict by mapping frames: {e}")
# Optional: restrict features to per-plant preferred frame using internal frame rules
if respect_instance_frame_rules_for_features:
try:
# Keep this in sync with _apply_instance_masks frame_rules
frame_rules: Dict[str, int] = {
"plant33": 2,
"plant16": 4,
"plant19": 5,
"plant26": 8,
"plant27": 8,
"plant29": 8,
"plant35": 7,
"plant36": 6,
"plant37": 2,
"plant45": 5,
}
before = len(plants)
def _keep(k: str) -> bool:
parts = k.split('_')
if len(parts) < 2:
return False
plant_name = parts[-2]
frame_token = parts[-1]
if not (plant_name.startswith('plant') and frame_token.startswith('frame')):
return False
desired = frame_rules.get(plant_name, 8)
return frame_token == f"frame{desired}"
plants = {k: v for k, v in plants.items() if _keep(k)}
logger.info(f"Restricted feature extraction by per-plant frame rules: {before} -> {len(plants)} items")
except Exception as e:
logger.warning(f"Failed to apply per-plant frame restriction for features: {e}")
# Optional: if features_frame_only set, keep only that frame's entries (global single frame)
if features_frame_only is not None:
frame_token = f"frame{features_frame_only}"
plants = {k: v for k, v in plants.items() if k.split('_')[-1] == frame_token}
logger.info(f"Restricted feature extraction to {len(plants)} items for {frame_token}")
# Optional: substitute feature input image from instance src_rules mapping (e.g., plant14 <- plant13)
if substitute_feature_image_from_instance_src:
try:
src_rules: Dict[str, str] = {
"plant13": "plant12",
"plant14": "plant13",
"plant15": "plant14",
"plant16": "plant15",
}
switched = 0
for key in list(plants.keys()):
parts = key.split('_')
if len(parts) < 5:
continue
date_key = "_".join(parts[:3])
plant_name = parts[3]
frame_token = parts[-1]
if plant_name not in src_rules:
continue
src_plant = src_rules[plant_name]
src_key = f"{date_key}_{src_plant}_{frame_token}"
if src_key not in plants:
continue
src_pdata = plants[src_key]
tgt_pdata = plants[key]
# Preserve the original composite used for segmentation for correct overlays later
try:
if 'composite' in tgt_pdata and 'segmentation_composite' not in tgt_pdata:
tgt_pdata['segmentation_composite'] = tgt_pdata['composite']
except Exception:
pass
# Swap feature inputs: composite and spectral bands
if 'composite' in src_pdata:
tgt_pdata['composite'] = src_pdata['composite']
if 'spectral_stack' in src_pdata:
tgt_pdata['spectral_stack'] = src_pdata['spectral_stack']
# Ensure mask aligns with substituted composite; resize if needed
try:
import cv2 as _cv2
import numpy as _np
comp = tgt_pdata.get('composite')
msk = tgt_pdata.get('mask')
if comp is not None and msk is not None:
ch, cw = comp.shape[:2]
mh, mw = msk.shape[:2]
if (mh, mw) != (ch, cw):
resized = _cv2.resize(msk.astype('uint8'), (cw, ch), interpolation=_cv2.INTER_NEAREST)
tgt_pdata['mask'] = resized
if 'soft_mask' in tgt_pdata and isinstance(tgt_pdata['soft_mask'], _np.ndarray):
tgt_pdata['soft_mask'] = (resized > 0).astype(_np.float32)
# Precompute masked composite with white background for saving
white = _np.full_like(comp, 255, dtype=_np.uint8)
result = white.copy()
result[tgt_pdata['mask'] > 0] = comp[tgt_pdata['mask'] > 0]
tgt_pdata['masked_composite'] = result
except Exception:
pass
switched += 1
if switched > 0:
logger.info(f"Substituted feature images from src_rules for {switched} items")
except Exception as e:
logger.warning(f"Failed feature-image substitution via src_rules: {e}")
# Step 4: Extract features
logger.info("Step 4/6: Extracting features...")
step_start = time.perf_counter()
# Stream-save mode: save outputs immediately after each plant's features when fast output is enabled
stream_save = False
try:
import os as _os
stream_save = bool(int(_os.environ.get('STREAM_SAVE', '0'))) or bool(getattr(self.output_manager, 'fast_mode', False))
except Exception:
stream_save = False
plants = self._extract_features(plants, stream_save=stream_save)
logger.info(f"Features done in {(time.perf_counter()-step_start):.2f}s")
# Step 5: Generate outputs (skip if already stream-saved)
if not stream_save:
logger.info("Step 5/6: Generating outputs...")
step_start = time.perf_counter()
self._generate_outputs(plants)
logger.info(f"Outputs done in {(time.perf_counter()-step_start):.2f}s")
# Step 6: Create summary
logger.info("Step 6/6: Creating summary...")
summary = self._create_summary(plants)
else:
logger.info("Segmentation-only mode: skipping texture/vegetation/morphology features and plots")
# Segmentation-only: generate only segmentation outputs and a minimal summary
logger.info("Step 4/4: Generating segmentation outputs (segmentation-only mode)...")
self._generate_outputs(plants)
summary = {
"total_plants": len(plants),
"successful_plants": len(plants),
"failed_plants": 0,
"features_extracted": {
"texture": 0,
"vegetation": 0,
"morphology": 0
}
}
total_time = time.perf_counter() - total_start
logger.info(f"Pipeline completed successfully in {total_time:.2f}s!")
return {
"plants": plants,
"summary": summary,
"config": self.config,
"timing_seconds": total_time
}
except Exception as e:
logger.error(f"Pipeline failed: {e}")
raise
def _export_white_background_maskouts(self, plants: Dict[str, Any], out_dir: Path) -> None:
"""Export RMBG composites with white background using the soft/binary masks.
Filenames follow: plantX_plantX_frameY_maskout.png so the final instance script can detect plants.
"""
# Clear any previous maskouts to avoid processing stale plants
try:
if out_dir.exists():
for p in out_dir.glob("*_maskout.png"):
try:
p.unlink()
except Exception:
pass
except Exception:
pass
count = 0
# Per-plant rule: use bbox-only (skip SAM2Long) for these plants on all dates except 2025_05_08
bbox_only_plants: Set[str] = {"plant19", "plant20", "plant27", "plant33", "plant39", "plant42", "plant44", "plant46"}
date_exception = "2025_05_08"
for key, pdata in plants.items():
try:
# key format: "YYYY_MM_DD_plantX_frameY"
parts = key.split('_')
if len(parts) < 3:
continue
plant_name = parts[-2]
frame_token = parts[-1] # e.g., frame8
if not plant_name.startswith('plant') or not frame_token.startswith('frame'):
continue
date_key = "_".join(parts[:3])
if (plant_name in bbox_only_plants) and (date_key != date_exception):
# Skip exporting maskouts for bbox-only plants so SAM2Long does not run on them
continue
# Extract frame number
frame_num = int(frame_token.replace('frame', ''))
composite = pdata.get('composite')
mask = pdata.get('mask')
if composite is None or mask is None:
continue
# Ensure 3-channel BGR
if len(composite.shape) == 2:
composite_bgr = cv2.cvtColor(composite, cv2.COLOR_GRAY2BGR)
else:
composite_bgr = composite
out_img = composite_bgr.copy()
# Set background to white where mask == 0
out_img[mask == 0] = (255, 255, 255)
out_path = out_dir / f"{plant_name}_{plant_name}_{frame_token}_maskout.png"
cv2.imwrite(str(out_path), out_img)
count += 1
except Exception as e:
logger.warning(f"Failed to export maskout for {key}: {e}")
logger.info(f"Exported {count} white-background maskouts to {out_dir}")
def _segment_plants(self, plants: Dict[str, Any],
bbox_lookup: Optional[Dict[str, tuple]]) -> Dict[str, Any]:
"""Segment plants using BRIA model.
If bbox_lookup is provided and contains an entry for the plant (e.g., 'plant1'),
the image is cropped/masked to the bounding box region before segmentation and the
predicted mask is mapped back to the full image size. In bbox mode a largest
connected component post-processing is applied to obtain a clean target mask.
"""
total = len(plants)
iterator = plants.items()
if tqdm is not None:
iterator = tqdm(list(plants.items()), desc="Segmenting", total=total, unit="img", leave=False)
for idx, (key, pdata) in enumerate(iterator):
try:
# Get composite image
composite = pdata['composite']
h, w = composite.shape[:2]
# Determine bbox for this plant if available
parts = key.split('_')
plant_name = parts[-2] if len(parts) >= 2 else None
date_key = "_".join(parts[:3]) if len(parts) >= 3 else None # e.g., 2025_04_16
bbox = None
if bbox_lookup is not None and plant_name is not None:
# keys in bbox_lookup are typically like 'plant1'
bbox = bbox_lookup.get(plant_name)
# For plant33, ignore any bbox and run full-image segmentation on all dates except the exception
if plant_name == 'plant33' and date_key != '2025_05_08':
bbox = None
# Plants that should use the bounding box itself as the mask (skip model)
bbox_only_plants: Set[str] = {"plant19", "plant20", "plant27", "plant39", "plant42", "plant44", "plant46"}
use_bbox_only = (plant_name in bbox_only_plants)
# Do not use bounding boxes for date 2025_05_08
if date_key == '2025_05_08':
bbox = None
if bbox is not None:
# Clamp bbox to image
x1, y1, x2, y2 = bbox
x1 = max(0, min(w, int(x1)))
x2 = max(0, min(w, int(x2)))
y1 = max(0, min(h, int(y1)))
y2 = max(0, min(h, int(y2)))
if x2 <= x1 or y2 <= y1:
x1, y1, x2, y2 = 0, 0, w, h
if use_bbox_only:
# Use the bbox as the mask directly (255 inside, 0 outside)
soft_full = np.zeros((h, w), dtype=np.float32)
soft_full[y1:y2, x1:x2] = 1.0
bin_full = np.zeros((h, w), dtype=np.uint8)
bin_full[y1:y2, x1:x2] = 255
pdata['soft_mask'] = soft_full
pdata['mask'] = bin_full
else:
# Segment inside the bbox region and map back
crop = composite[y1:y2, x1:x2]
soft_mask_crop = self.segmentation_manager.segment_image_soft(crop)
soft_full = np.zeros((h, w), dtype=np.float32)
soft_resized = cv2.resize(soft_mask_crop, (x2 - x1, y2 - y1), interpolation=cv2.INTER_LINEAR)
soft_full[y1:y2, x1:x2] = soft_resized
bin_full = (soft_full > 0.5).astype(np.uint8) * 255
try:
n_lbl, labels, stats, _ = cv2.connectedComponentsWithStats(bin_full, 8)
if n_lbl > 1:
largest = 1 + int(np.argmax(stats[1:, cv2.CC_STAT_AREA]))
bin_full = (labels == largest).astype(np.uint8) * 255
except Exception:
pass
pdata['soft_mask'] = soft_full.astype(np.float32)
pdata['mask'] = bin_full.astype(np.uint8)
else:
# Full-image segmentation (no bbox)
soft_mask = self.segmentation_manager.segment_image_soft(composite)
pdata['soft_mask'] = soft_mask
pdata['mask'] = (soft_mask * 255.0).astype(np.uint8)
# Progress log every 25 items and for first/last
if tqdm is None and (idx == 0 or (idx + 1) % 25 == 0 or (idx + 1) == total):
logger.info(f"Segmented {idx + 1}/{total}: {key}")
except Exception as e:
logger.error(f"Segmentation failed for {key}: {e}")
pdata['soft_mask'] = np.zeros(composite.shape[:2], dtype=np.float32)
pdata['mask'] = np.zeros(composite.shape[:2], dtype=np.uint8)
return plants
def _handle_occlusion(self, plants: Dict[str, Any]) -> Dict[str, Any]:
"""
Handle occlusion problems using SAM2Long.
This method groups plants by their base plant ID and processes
each plant's 13-frame sequence to differentiate target plant
from neighboring plants.
Args:
plants: Dictionary of plant data
Returns:
Updated plant data with occlusion handling results
"""
if self.occlusion_handler is None:
logger.warning("Occlusion handler not available, skipping occlusion handling")
return plants
# Group plants by base plant ID (e.g., "plant1" from "plant1_plant1_frame1")
plant_groups = {}
for key, pdata in plants.items():
# Extract plant ID from key like "plant1_plant1_frame1"
parts = key.split('_')
if len(parts) >= 3:
plant_id = parts[0] # e.g., "plant1"
if plant_id not in plant_groups:
plant_groups[plant_id] = []
plant_groups[plant_id].append((key, pdata))
logger.info(f"Processing {len(plant_groups)} plant groups for occlusion handling")
# Process each plant group
for plant_id, plant_frames in plant_groups.items():
try:
# Sort frames by frame number
plant_frames.sort(key=lambda x: int(x[0].split('_')[-1].replace('frame', '')))
if len(plant_frames) < 2:
logger.warning(f"Plant {plant_id} has only {len(plant_frames)} frames, skipping")
continue
# Extract frames and keys
frame_keys = [x[0] for x in plant_frames]
frames = [x[1]['composite'] for x in plant_frames]
logger.info(f"Processing plant {plant_id} with {len(frames)} frames")
# Process with SAM2Long
occlusion_results = self.occlusion_handler.segment_plant_sequence(
frames=frames,
target_plant_id=plant_id
)
# Update plant data with occlusion results
target_masks = occlusion_results['target_masks']
neighbor_masks = occlusion_results['neighbor_masks']
for i, (key, pdata) in enumerate(plant_frames):
if i < len(target_masks):
# Update mask with target plant only
pdata['original_mask'] = pdata.get('mask', np.zeros_like(target_masks[i]))
pdata['mask'] = target_masks[i]
pdata['neighbor_mask'] = neighbor_masks[i]
pdata['occlusion_handled'] = True
# Update soft mask as well
pdata['original_soft_mask'] = pdata.get('soft_mask', np.zeros_like(target_masks[i], dtype=np.float32))
pdata['soft_mask'] = (target_masks[i] / 255.0).astype(np.float32)
# Calculate and store occlusion metrics
metrics = self.occlusion_handler.get_occlusion_metrics(occlusion_results)
for key, pdata in plant_frames:
pdata['occlusion_metrics'] = metrics
logger.info(f"Plant {plant_id} occlusion handling completed")
logger.info(f" - Average occlusion ratio: {metrics['average_occlusion_ratio']:.3f}")
logger.info(f" - Frames with occlusion: {metrics['frames_with_occlusion']}")
except Exception as e:
logger.error(f"Occlusion handling failed for plant {plant_id}: {e}")
# Mark as failed but continue
for key, pdata in plant_frames:
pdata['occlusion_handled'] = False
pdata['occlusion_error'] = str(e)
return plants
def _extract_features(self, plants: Dict[str, Any], stream_save: bool = False) -> Dict[str, Any]:
"""Extract all features from plants.
If stream_save is True, save outputs for each plant immediately after
its features are computed to improve throughput and reduce peak memory.
"""
total = len(plants)
logger.info(f"Extracting features for {total} plants...")
iterator = plants.items()
if tqdm is not None:
iterator = tqdm(list(plants.items()), desc="Extracting features", total=total, unit="img", leave=False)
# Prepare output directories once if we're streaming saves
if stream_save:
try:
self.output_manager.create_output_directories()
except Exception:
pass
for idx, (key, pdata) in enumerate(iterator):
try:
logger.debug(f"Extracting features for {key}")
# Extract texture features
pdata['texture_features'] = self._extract_texture_features(pdata)
# Extract vegetation indices
pdata['vegetation_indices'] = self._extract_vegetation_indices(pdata)
# Extract morphological features
pdata['morphology_features'] = self._extract_morphology_features(pdata)
# Immediately save outputs for this plant if streaming is enabled
if stream_save:
try:
self.output_manager.save_plant_results(key, pdata)
except Exception as _e:
logger.error(f"Stream-save failed for {key}: {_e}")
logger.debug(f"Features extracted for {key}")
if tqdm is None and (idx == 0 or (idx + 1) % 25 == 0 or (idx + 1) == total):
logger.info(f"Extracted features for {idx + 1}/{total}: {key}")
except Exception as e:
logger.error(f"Feature extraction failed for {key}: {e}")
# Add empty features
pdata['texture_features'] = {}
pdata['vegetation_indices'] = {}
pdata['morphology_features'] = {}
return plants
def _extract_texture_features(self, pdata: Dict[str, Any]) -> Dict[str, Any]:
"""Extract texture features for a single plant."""
features = {}
# Get bands to process
bands = ['color', 'nir', 'red_edge', 'red', 'green', 'pca']
for band in bands:
try:
# Prepare grayscale image
gray_image = self._prepare_band_image(pdata, band)
# Extract texture features
band_features = self.texture_extractor.extract_all_texture_features(gray_image)
# Compute statistics using mask3 → features_mask → mask
mask = pdata.get('mask3', pdata.get('features_mask', pdata.get('mask')))
stats = self.texture_extractor.compute_texture_statistics(band_features, mask)
features[band] = {
'features': band_features,
'statistics': stats
}
except Exception as e:
logger.error(f"Texture extraction failed for band {band}: {e}")
features[band] = {'features': {}, 'statistics': {}}
return features
def _extract_vegetation_indices(self, pdata: Dict[str, Any]) -> Dict[str, Any]:
"""Extract vegetation indices for a single plant."""
try:
spectral_stack = pdata.get('spectral_stack', {})
# Prefer mask3 → features_mask → mask
mask = pdata.get('mask3', pdata.get('features_mask', pdata.get('mask')))
if not spectral_stack or mask is None:
return {}
return self.vegetation_extractor.compute_vegetation_indices(
spectral_stack, mask
)
except Exception as e:
logger.error(f"Vegetation index extraction failed: {e}")
return {}
def _extract_morphology_features(self, pdata: Dict[str, Any]) -> Dict[str, Any]:
"""Extract morphological features for a single plant."""
try:
composite = pdata.get('composite')
# Prefer mask3 → features_mask → mask
mask = pdata.get('mask3', pdata.get('features_mask', pdata.get('mask')))
if composite is None or mask is None:
return {}
return self.morphology_extractor.extract_morphology_features(
composite, mask
)
except Exception as e:
logger.error(f"Morphology feature extraction failed: {e}")
return {}
def _prepare_band_image(self, pdata: Dict[str, Any], band: str) -> np.ndarray:
"""Prepare grayscale image for a specific band."""
if band == 'color':
composite = pdata['composite']
# Prefer mask3 → features_mask → mask
mask = pdata.get('mask3', pdata.get('features_mask', pdata.get('mask')))
if mask is not None:
masked = self.mask_handler.apply_mask_to_image(composite, mask)
return cv2.cvtColor(masked, cv2.COLOR_BGR2GRAY)
else:
return cv2.cvtColor(composite, cv2.COLOR_BGR2GRAY)
elif band == 'pca':
# Create PCA from spectral bands
spectral_stack = pdata.get('spectral_stack', {})
# Prefer mask3 → features_mask → mask
mask = pdata.get('mask3', pdata.get('features_mask', pdata.get('mask')))
if not spectral_stack:
return np.zeros((512, 512), dtype=np.uint8)
# Stack bands
bands_data = []
for b in ['nir', 'red_edge', 'red', 'green']:
if b in spectral_stack:
arr = spectral_stack[b].squeeze(-1).astype(float)
if mask is not None:
arr = np.where(mask > 0, arr, np.nan)
bands_data.append(arr)
if not bands_data:
return np.zeros((512, 512), dtype=np.uint8)
# Create PCA
full_stack = np.stack(bands_data, axis=-1)
h, w, c = full_stack.shape
flat = full_stack.reshape(-1, c)
valid = ~np.isnan(flat).any(axis=1)
if valid.sum() == 0:
return np.zeros((h, w), dtype=np.uint8)
vec = np.zeros(h * w)
vec[valid] = PCA(n_components=1, whiten=True).fit_transform(
flat[valid]
).squeeze()
gray_f = vec.reshape(h, w)
if mask is not None:
m, M = gray_f[mask > 0].min(), gray_f[mask > 0].max()
else:
m, M = gray_f.min(), gray_f.max()
if M > m:
gray = ((gray_f - m) / (M - m) * 255).astype(np.uint8)
else:
gray = np.zeros_like(gray_f, dtype=np.uint8)
return gray
else:
# Individual spectral band
spectral_stack = pdata.get('spectral_stack', {})
# Prefer mask3 → features_mask → mask
mask = pdata.get('mask3', pdata.get('features_mask', pdata.get('mask')))
if band not in spectral_stack:
return np.zeros((512, 512), dtype=np.uint8)
arr = spectral_stack[band].squeeze(-1).astype(float)
if mask is not None:
arr = np.where(mask > 0, arr, np.nan)
if mask is not None:
m, M = np.nanmin(arr), np.nanmax(arr)
else:
m, M = arr.min(), arr.max()
if M > m:
gray = ((np.nan_to_num(arr, nan=m) - m) / (M - m) * 255).astype(np.uint8)
else:
gray = np.zeros_like(arr, dtype=np.uint8)
return gray
def _generate_outputs(self, plants: Dict[str, Any]) -> None:
"""Generate all output files and visualizations."""
self.output_manager.create_output_directories()
for key, pdata in plants.items():
try:
logger.debug(f"Generating outputs for {key}")
self.output_manager.save_plant_results(key, pdata)
except Exception as e:
logger.error(f"Output generation failed for {key}: {e}")
def _create_summary(self, plants: Dict[str, Any]) -> Dict[str, Any]:
"""Create summary of pipeline results."""
summary = {
"total_plants": len(plants),
"successful_plants": 0,
"failed_plants": 0,
"features_extracted": {
"texture": 0,
"vegetation": 0,
"morphology": 0
}
}
for key, pdata in plants.items():
try:
# Check if features were extracted
if pdata.get('texture_features'):
summary["features_extracted"]["texture"] += 1
if pdata.get('vegetation_indices'):
summary["features_extracted"]["vegetation"] += 1
if pdata.get('morphology_features'):
summary["features_extracted"]["morphology"] += 1
summary["successful_plants"] += 1
except Exception:
summary["failed_plants"] += 1
return summary
def _apply_instance_masks(self, plants: Dict[str, Any], instance_results_dir: Path) -> None:
"""Replace segmentation masks with SAM2Long instance masks using track_1.
Expects files under instance_results_dir/plantX/track_1/frame_YY_mask.png.
"""
# Default and per-plant overrides for source plant, track and preferred frame
default_track = "track_0"
src_rules: Dict[str, str] = {
"plant13": "plant12",
"plant14": "plant13",
"plant15": "plant14",
"plant16": "plant15",
}
track_rules: Dict[str, str] = {
# explicit track rules
"plant1": "track_0",
"plant4": "track_0",
"plant9": "track_3",
"plant13": "track_1",
"plant14": "track_0",
"plant15": "track_0",
"plant16": "track_0",
"plant18": "track_0",
"plant19": "track_0",
"plant23": "track_1",
"plant26": "track_0",
"plant27": "track_0",
"plant29": "track_0",
"plant31": "track_1",
"plant34": "track_1",
"plant35": "track_1",
"plant36": "track_0",
"plant37": "track_1",
"plant38": "track_0",
"plant39": "track_1",
"plant40": "track_0",
"plant41": "track_1",
"plant42": "track_0",
"plant43": "track_0",
"plant45": "track_0",
}
frame_rules: Dict[str, int] = {
# preferred frame overrides (1-based)
"plant13": 8,
"plant14": 8,
"plant15": 8,
"plant33": 2,
"plant16": 4,
"plant19": 5,
"plant26": 8,
"plant27": 8,
"plant29": 8,
"plant35": 7,
"plant36": 6,
"plant37": 2,
"plant45": 5,
}
# Per-plant rule: skip applying instance masks (keep bbox/BRIA mask) on all dates except 2025_05_08
bbox_only_plants: Set[str] = {"plant19", "plant20", "plant27", "plant33", "plant39", "plant42", "plant44", "plant46"}
date_exception = "2025_05_08"
for key, pdata in plants.items():
try:
parts = key.split('_')
if len(parts) < 3:
continue
plant_name = parts[-2]
frame_token = parts[-1] # frame8
if not (plant_name.startswith('plant') and frame_token.startswith('frame')):
continue
date_key = "_".join(parts[:3])
if (plant_name in bbox_only_plants) and (date_key != date_exception):
# Do not override masks for bbox-only plants
continue
frame_num = int(frame_token.replace('frame', ''))
# Resolve source plant, track and desired frame
src_plant = src_rules.get(plant_name, plant_name)
track_name = track_rules.get(plant_name, default_track)
desired_frame = frame_rules.get(plant_name, frame_num)
plant_dir = Path(instance_results_dir) / src_plant / track_name
mask_path = plant_dir / f"frame_{desired_frame:02d}_mask.png"
if not mask_path.exists():
# Fallback to current frame if override not found
fallback = plant_dir / f"frame_{frame_num:02d}_mask.png"
if fallback.exists():
mask_path = fallback
else:
# Last-resort: pick any available frame mask in the track directory
try:
candidates = sorted(plant_dir.glob("frame_*_mask.png"))
if len(candidates) > 0:
mask_path = candidates[0]
else:
continue
except Exception:
continue
inst_mask = cv2.imread(str(mask_path), cv2.IMREAD_GRAYSCALE)
if inst_mask is None:
continue
# Ensure binary uint8 0/255
inst_mask_bin = (inst_mask > 0).astype(np.uint8) * 255
pdata['original_mask'] = pdata.get('mask', inst_mask_bin.copy())
pdata['mask'] = inst_mask_bin
pdata['original_soft_mask'] = pdata.get('soft_mask', (inst_mask_bin / 255.0).astype(np.float32))
pdata['soft_mask'] = (inst_mask_bin / 255.0).astype(np.float32)
pdata['instance_applied'] = True
# Build mask3 = external(mask) AND BRIA(original_mask)
try:
_m1 = pdata.get('mask')
_m2 = pdata.get('original_mask')
if isinstance(_m1, np.ndarray) and isinstance(_m2, np.ndarray):
_m1b = (_m1.astype(np.uint8) > 0)
_m2b = (_m2.astype(np.uint8) > 0)
mask3 = (_m1b & _m2b).astype(np.uint8) * 255
pdata['mask3'] = mask3
pdata['features_mask'] = mask3
except Exception:
pass
# After applying instance masks, also overwrite the composite and spectral stack
# with the source plant's raw image (desired frame preferred) so that
# feature extraction and saved originals/overlays are consistent with the mask source.
try:
if plant_name in src_rules:
date_key = "_".join(parts[:3])
src_key_desired = f"{date_key}_{src_plant}_frame{desired_frame}"
src_key_same = f"{date_key}_{src_plant}_{frame_token}"
copy_from = plants.get(src_key_desired) or plants.get(src_key_same)
if copy_from is None:
# Fallback: load source composite from filesystem if not present in plants dict
try:
from PIL import Image as _Image
_date_folder = date_key.replace('_', '-')
_date_dir = Path(self.config.paths.input_folder)
if _date_dir.name != _date_folder:
_date_dir = _date_dir / _date_folder
_frame_path = _date_dir / src_plant / f"{src_plant}_frame{desired_frame}.tif"
if not _frame_path.exists():
_frame_path = _date_dir / src_plant / f"{src_plant}_frame{frame_num}.tif"
if _frame_path.exists():
_img = _Image.open(str(_frame_path))
# Process to composite using preprocessor
comp, spec = self.preprocessor.process_raw_image(_img)
copy_from = {"composite": comp, "spectral_stack": spec}
except Exception:
copy_from = None
if copy_from is not None:
# Preserve the segmentation-time composite once
if 'composite' in pdata and 'segmentation_composite' not in pdata:
pdata['segmentation_composite'] = pdata['composite']
if 'composite' in copy_from:
pdata['composite'] = copy_from['composite']
if 'spectral_stack' in copy_from:
pdata['spectral_stack'] = copy_from['spectral_stack']
# Ensure mask size matches the copied composite
ch, cw = pdata['composite'].shape[:2]
mh, mw = pdata['mask'].shape[:2]
if (mh, mw) != (ch, cw):
pdata['mask'] = cv2.resize(pdata['mask'].astype('uint8'), (cw, ch), interpolation=cv2.INTER_NEAREST)
pdata['soft_mask'] = (pdata['mask'] > 0).astype(np.float32)
except Exception:
pass
except Exception as e:
logger.debug(f"Instance mask apply failed for {key}: {e}")
def _apply_instance_masks_from_mapping(self, plants: Dict[str, Any], mapping_file: Path) -> None:
"""Apply instance masks using an explicit mapping file with absolute paths.
mapping JSON structure:
{
"plant1": {"frame": 8, "mask_path": "/abs/path/to/plant1/track_X/frame_08_mask.png"},
"plant2": {"frame": 8, "mask_path": "/abs/path/.../frame_08_mask.png"},
...
}
If a plant's mapping specifies a different frame, only entries matching that frame are updated.
"""
import json
if not mapping_file.exists():
raise FileNotFoundError(f"Mapping file not found: {mapping_file}")
with open(mapping_file, "r") as f:
mapping = json.load(f)
# Normalize mapping plant keys to names like 'plantX'
norm_map = {}
for k, v in mapping.items():
k_norm = k if str(k).startswith("plant") else f"plant{int(k)}" if str(k).isdigit() else str(k)
norm_map[k_norm] = v
for key, pdata in plants.items():
try:
parts = key.split('_')
if len(parts) < 3:
continue
plant_name = parts[-2]
frame_token = parts[-1]
if not (plant_name.startswith('plant') and frame_token.startswith('frame')):
continue
frame_num = int(frame_token.replace('frame', ''))
if plant_name not in norm_map:
continue
entry = norm_map[plant_name]
target_frame = int(entry.get("frame", frame_num))
if frame_num != target_frame:
# Only update the designated frame for this plant
continue
mask_path_str = entry.get("mask_path")
if not mask_path_str:
continue
mask_path = Path(mask_path_str)
if not mask_path.exists():
logger.warning(f"Mask path not found for {plant_name} {frame_token}: {mask_path}")
continue
inst_mask = cv2.imread(str(mask_path), cv2.IMREAD_GRAYSCALE)
if inst_mask is None:
continue
inst_mask_bin = (inst_mask > 0).astype(np.uint8) * 255
pdata['original_mask'] = pdata.get('mask', inst_mask_bin.copy())
pdata['mask'] = inst_mask_bin
pdata['original_soft_mask'] = pdata.get('soft_mask', (inst_mask_bin / 255.0).astype(np.float32))
pdata['soft_mask'] = (inst_mask_bin / 255.0).astype(np.float32)
pdata['instance_applied'] = True
# Build mask3 = external(mask) AND BRIA(original_mask)
try:
_m1 = pdata.get('mask')
_m2 = pdata.get('original_mask')
if isinstance(_m1, np.ndarray) and isinstance(_m2, np.ndarray):
_m1b = (_m1.astype(np.uint8) > 0)
_m2b = (_m2.astype(np.uint8) > 0)
mask3 = (_m1b & _m2b).astype(np.uint8) * 255
pdata['mask3'] = mask3
pdata['features_mask'] = mask3
except Exception:
pass
except Exception as e:
logger.debug(f"Instance mapping apply failed for {key}: {e}")
def run_pipeline(config_path: str, load_all_frames: bool = False, segmentation_only: bool = False, filter_plants: Optional[List[str]] = None) -> Dict[str, Any]:
"""
Convenience function to run the pipeline.
Args:
config_path: Path to configuration file
load_all_frames: Whether to load all frames or selected frames
segmentation_only: If True, run segmentation only and skip feature extraction
Returns:
Pipeline results
"""
pipeline = SorghumPipeline(config_path)
return pipeline.run(load_all_frames, segmentation_only, filter_plants)
if __name__ == "__main__":
import sys
config_path = sys.argv[1] if len(sys.argv) > 1 else "config.yml"
load_all = "--all" in sys.argv
seg_only = "--seg-only" in sys.argv
# Basic arg parse for --plant=<name>
plant_filter = None
for arg in sys.argv[1:]:
if arg.startswith("--plant="):
plant_filter = [arg.split("=", 1)[1]]
try:
results = run_pipeline(config_path, load_all, seg_only, plant_filter)
print("Pipeline completed successfully!")
print(f"Processed {results['summary']['total_plants']} plants")
except Exception as e:
print(f"Pipeline failed: {e}")
sys.exit(1)
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