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detection.py
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detection.py
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import os
import math
import csv
import torch
import numpy as np
import pandas as pd
from skimage.feature import peak_local_max, blob_log
from skimage.measure import label, regionprops_table
from skimage.morphology import disk, dilation
from tqdm import tqdm
from biapy.data.data_2D_manipulation import load_and_prepare_2D_train_data
from biapy.data.data_3D_manipulation import load_and_prepare_3D_data
from biapy.data.post_processing.post_processing import (remove_close_points, detection_watershed,
measure_morphological_props_and_filter)
from biapy.data.pre_processing import create_detection_masks, norm_range01
from biapy.utils.util import save_tif, read_chunked_data, write_chunked_data, order_dimensions
from biapy.engine.metrics import detection_metrics, jaccard_index, weighted_bce_dice_loss, CrossEntropyLoss_wrapper
from biapy.engine.base_workflow import Base_Workflow
class Detection_Workflow(Base_Workflow):
"""
Detection workflow where the goal is to localize objects in the input image, not requiring a pixel-level class.
More details in `our documentation <https://biapy.readthedocs.io/en/latest/workflows/detection.html>`_.
Parameters
----------
cfg : YACS configuration
Running configuration.
Job_identifier : str
Complete name of the running job.
device : Torch device
Device used.
args : argpase class
Arguments used in BiaPy's call.
"""
def __init__(self, cfg, job_identifier, device, args, **kwargs):
super(Detection_Workflow, self).__init__(cfg, job_identifier, device, args, **kwargs)
# Detection stats
self.stats['d_precision'] = 0
self.stats['d_recall'] = 0
self.stats['d_f1'] = 0
self.stats['d_precision_per_crop'] = 0
self.stats['d_recall_per_crop'] = 0
self.stats['d_f1_per_crop'] = 0
self.original_test_mask_path = self.prepare_detection_data()
self.use_gt = False
if self.cfg.DATA.TEST.LOAD_GT or self.cfg.DATA.TEST.USE_VAL_AS_TEST:
self.use_gt = True
if self.cfg.TEST.BY_CHUNKS.ENABLE and self.cfg.TEST.BY_CHUNKS.WORKFLOW_PROCESS.ENABLE:
self.use_gt = False
if self.use_gt:
self.csv_files = sorted(next(os.walk(self.original_test_mask_path))[2])
self.cell_count_file = os.path.join(self.cfg.PATHS.RESULT_DIR.PATH, 'cell_counter.csv')
self.cell_count_lines = []
# From now on, no modification of the cfg will be allowed
self.cfg.freeze()
# Activations for each output channel:
# channel number : 'activation'
self.activations = {'0': 'CE_Sigmoid'}
# Workflow specific training variables
self.mask_path = cfg.DATA.TRAIN.GT_PATH
self.load_Y_val = True
# Workflow specific test variables
self.postpone_postproc = False
if cfg.TEST.BY_CHUNKS.ENABLE and cfg.TEST.BY_CHUNKS.WORKFLOW_PROCESS.ENABLE and \
cfg.TEST.BY_CHUNKS.WORKFLOW_PROCESS.TYPE == "chunk_by_chunk":
self.postpone_postproc = True
if self.cfg.TEST.POST_PROCESSING.DET_WATERSHED or self.cfg.TEST.POST_PROCESSING.REMOVE_CLOSE_POINTS:
self.post_processing['detection_post'] = True
else:
self.post_processing['detection_post'] = False
def define_metrics(self):
"""
Definition of self.metrics, self.metric_names and self.loss variables.
"""
self.metrics = [
jaccard_index(num_classes=self.cfg.MODEL.N_CLASSES,
first_not_binary_channel=self.cfg.MODEL.N_CLASSES, device=self.device,
torchvision_models=True if self.cfg.MODEL.SOURCE == "torchvision" else False)
]
self.metric_names = ["jaccard_index"]
if self.cfg.LOSS.TYPE == "CE":
self.loss = CrossEntropyLoss_wrapper(num_classes=self.cfg.MODEL.N_CLASSES,
torchvision_models=True if self.cfg.MODEL.SOURCE == "torchvision" else False)
elif self.cfg.LOSS.TYPE == "W_CE_DICE":
self.loss = weighted_bce_dice_loss(w_dice=0.66, w_bce=0.33)
def metric_calculation(self, output, targets, metric_logger=None):
"""
Execution of the metrics defined in :func:`~define_metrics` function.
Parameters
----------
output : Torch Tensor
Prediction of the model.
targets : Torch Tensor
Ground truth to compare the prediction with.
metric_logger : MetricLogger, optional
Class to be updated with the new metric(s) value(s) calculated.
Returns
-------
value : float
Value of the metric for the given prediction.
"""
with torch.no_grad():
train_iou = self.metrics[0](output, targets)
train_iou = train_iou.item() if not torch.isnan(train_iou) else 0
if metric_logger is not None:
metric_logger.meters[self.metric_names[0]].update(train_iou)
else:
return train_iou
def detection_process(self, pred, filenames, metric_names=[]):
"""
Detection workflow engine for test/inference. Process model's prediction to prepare detection output and
calculate metrics.
Parameters
----------
pred : Torch Tensor
Model predictions.
filenames : List of str
Predicted image's filenames.
metric_names : List of str
Metrics names.
"""
file_ext = os.path.splitext(filenames[0])[1]
ndim = 2 if self.cfg.PROBLEM.NDIM == "2D" else 3
pred_shape = pred.shape
print("Capturing the local maxima ")
all_points = []
all_classes = []
for channel in range(pred.shape[-1]):
print("Class {}".format(channel+1))
if len(self.cfg.TEST.DET_MIN_TH_TO_BE_PEAK) == 1:
min_th_peak = self.cfg.TEST.DET_MIN_TH_TO_BE_PEAK[0]
else:
min_th_peak = self.cfg.TEST.DET_MIN_TH_TO_BE_PEAK[channel]
# Find points
if self.cfg.TEST.DET_POINT_CREATION_FUNCTION == "peak_local_max":
pred_coordinates = peak_local_max(pred[...,channel].astype(np.float32),
threshold_abs=min_th_peak,
exclude_border=self.cfg.TEST.DET_EXCLUDE_BORDER)
else:
pred_coordinates = blob_log(pred[...,channel]*255, min_sigma=self.cfg.TEST.DET_BLOB_LOG_MIN_SIGMA,
max_sigma=self.cfg.TEST.DET_BLOB_LOG_MAX_SIGMA, num_sigma=self.cfg.TEST.DET_BLOB_LOG_NUM_SIGMA,
threshold=min_th_peak, exclude_border=self.cfg.TEST.DET_EXCLUDE_BORDER)
pred_coordinates = pred_coordinates[:,:3].astype(int) # Remove sigma
# Remove close points per class as post-processing method
if self.cfg.TEST.POST_PROCESSING.REMOVE_CLOSE_POINTS and not self.postpone_postproc:
if len(self.cfg.TEST.POST_PROCESSING.REMOVE_CLOSE_POINTS_RADIUS) == 1:
radius = self.cfg.TEST.POST_PROCESSING.REMOVE_CLOSE_POINTS_RADIUS[0]
else:
radius = self.cfg.TEST.POST_PROCESSING.REMOVE_CLOSE_POINTS_RADIUS[channel]
pred_coordinates = remove_close_points(pred_coordinates, radius, self.cfg.DATA.TEST.RESOLUTION,
ndim=ndim)
all_points.append(pred_coordinates)
c_size = 1 if len(pred_coordinates) == 0 else len(pred_coordinates)
all_classes.append(np.full(c_size, channel))
# Remove close points again seeing all classes together, as it can be that a point is detected in both classes
# if there is not clear distinction between them
classes = 1 if self.cfg.MODEL.N_CLASSES <= 2 else self.cfg.MODEL.N_CLASSES
if self.cfg.TEST.POST_PROCESSING.REMOVE_CLOSE_POINTS and classes > 1 and not self.postpone_postproc:
print("All classes together")
radius = np.min(self.cfg.TEST.POST_PROCESSING.REMOVE_CLOSE_POINTS_RADIUS)
all_points = np.concatenate(all_points, axis=0)
all_classes = np.concatenate(all_classes, axis=0)
new_points, all_classes = remove_close_points(all_points, radius, self.cfg.DATA.TEST.RESOLUTION,
classes=all_classes, ndim=ndim)
# Create again list of arrays of all points
all_points = []
for i in range(classes):
all_points.append([])
for i, c in enumerate(all_classes):
all_points[c].append(new_points[i])
del new_points
# Create a file with detected point and other image with predictions ids (if GT given)
print("Creating the images with detected points . . .")
points_pred = np.zeros(pred.shape[:-1], dtype=np.uint8)
for n, pred_coordinates in enumerate(all_points):
if self.use_gt:
pred_id_img = np.zeros(pred_shape[:-1], dtype=np.uint32)
for j, coord in enumerate(pred_coordinates):
z,y,x = coord
points_pred[z,y,x] = n+1
if self.use_gt:
pred_id_img[z,y,x] = j+1
# Dilate and save the prediction ids for the current class
if self.use_gt:
for i in range(pred_id_img.shape[0]):
pred_id_img[i] = dilation(pred_id_img[i], disk(3))
if file_ext in ['.hdf5', '.h5', ".zarr"]:
write_chunked_data(np.expand_dims(pred_id_img,-1), self.cfg.PATHS.RESULT_DIR.DET_ASSOC_POINTS,
os.path.splitext(filenames[0])[0]+'_class'+str(n+1)+'_pred_ids'+file_ext, dtype_str="uint32",
verbose=self.cfg.TEST.VERBOSE)
else:
save_tif(np.expand_dims(np.expand_dims(pred_id_img,0),-1), self.cfg.PATHS.RESULT_DIR.DET_ASSOC_POINTS,
[os.path.splitext(filenames[0])[0]+'_class'+str(n+1)+'_pred_ids.tif'], verbose=self.cfg.TEST.VERBOSE)
self.cell_count_lines.append([filenames, len(pred_coordinates)])
if self.use_gt: del pred_id_img
# Dilate and save the detected point image
if len(pred_coordinates) > 0:
for i in range(points_pred.shape[0]):
points_pred[i] = dilation(points_pred[i], disk(3))
if file_ext in ['.hdf5', '.h5', ".zarr"]:
write_chunked_data(np.expand_dims(points_pred,-1), self.cfg.PATHS.RESULT_DIR.DET_LOCAL_MAX_COORDS_CHECK, filenames[0],
dtype_str="uint8", verbose=self.cfg.TEST.VERBOSE)
else:
save_tif(np.expand_dims(np.expand_dims(points_pred,0),-1), self.cfg.PATHS.RESULT_DIR.DET_LOCAL_MAX_COORDS_CHECK,
filenames, verbose=self.cfg.TEST.VERBOSE)
# Detection watershed
if self.cfg.TEST.POST_PROCESSING.DET_WATERSHED:
data_filename = os.path.join(self.cfg.DATA.TEST.PATH, filenames[0])
w_dir = os.path.join(self.cfg.PATHS.WATERSHED_DIR, filenames[0])
check_wa = w_dir if self.cfg.PROBLEM.DETECTION.DATA_CHECK_MW else None
points_pred = detection_watershed(points_pred, all_points, data_filename, self.cfg.TEST.POST_PROCESSING.DET_WATERSHED_FIRST_DILATION,
clases, ndim=ndim, donuts_classes=self.cfg.TEST.POST_PROCESSING.DET_WATERSHED_DONUTS_CLASSES,
donuts_patch=self.cfg.TEST.POST_PROCESSING.DET_WATERSHED_DONUTS_PATCH,
donuts_nucleus_diameter=self.cfg.TEST.POST_PROCESSING.DET_WATERSHED_DONUTS_NUCLEUS_DIAMETER, save_dir=check_wa)
# Instance filtering by properties
points_pred, d_result = measure_morphological_props_and_filter(points_pred, self.cfg.DATA.TEST.RESOLUTION,
properties=self.cfg.TEST.POST_PROCESSING.MEASURE_PROPERTIES.REMOVE_BY_PROPERTIES.PROPS,
prop_values=self.cfg.TEST.POST_PROCESSING.MEASURE_PROPERTIES.REMOVE_BY_PROPERTIES.VALUES,
comp_signs=self.cfg.TEST.POST_PROCESSING.MEASURE_PROPERTIES.REMOVE_BY_PROPERTIES.SIGN,
coords_list=np.concatenate(all_points, axis=0))
if file_ext in ['.hdf5', '.h5', ".zarr"]:
write_chunked_data(np.expand_dims(points_pred,-1), self.cfg.PATHS.RESULT_DIR.DET_ASSOC_POINTS, filenames[0], dtype_str="uint8",
verbose=self.cfg.TEST.VERBOSE)
else:
save_tif(np.expand_dims(np.expand_dims(points_pred,0),-1), self.cfg.PATHS.RESULT_DIR.PER_IMAGE_POST_PROCESSING,
filenames, verbose=self.cfg.TEST.VERBOSE)
del points_pred
# Save coords in a couple of csv files
aux = np.concatenate(all_points, axis=0)
df = None
if len(aux) != 0:
if self.cfg.PROBLEM.NDIM == "3D":
prob = pred[aux[:,0], aux[:,1], aux[:,2], all_classes]
prob = np.concatenate(prob, axis=0)
all_classes = np.concatenate(all_classes, axis=0)
if self.cfg.TEST.POST_PROCESSING.DET_WATERSHED:
df = pd.DataFrame(zip(d_result['labels'], list(aux[:,0]), list(aux[:,1]), list(aux[:,2]), list(prob), list(all_classes),
d_result['npixels'], d_result['areas'], d_result['circularities'], d_result['diameters'], d_result['perimeters'],
d_result['comment'], d_result['conditions']), columns =['pred_id', 'axis-0', 'axis-1', 'axis-2', 'probability',
'class', 'npixels', 'volume', 'sphericity', 'diameter', 'perimeter (surface area)', 'comment', 'conditions'])
df = df.sort_values(by=['pred_id'])
else:
labels = []
for i, pred_coordinates in enumerate(all_points):
for j in range(len(pred_coordinates)):
labels.append(j+1)
df = pd.DataFrame(zip(labels, list(aux[:,0]), list(aux[:,1]), list(aux[:,2]), list(prob), list(all_classes)),
columns =['pred_id', 'axis-0', 'axis-1', 'axis-2', 'probability', 'class'])
df = df.sort_values(by=['pred_id'])
else:
aux = aux[:,1:]
prob = pred[0,aux[:,0], aux[:,1], all_classes]
prob = np.concatenate(prob, axis=0)
all_classes = np.concatenate(all_classes, axis=0)
if self.cfg.TEST.POST_PROCESSING.DET_WATERSHED:
df = pd.DataFrame(zip(d_result['labels'], list(aux[:,0]), list(aux[:,1]), list(prob), list(all_classes),
d_result['npixels'], d_result['areas'], d_result['circularities'], d_result['diameters'], d_result['perimeters'],
d_result['elongations'], d_result['comment'], d_result['conditions']), columns =['pred_id', 'axis-0', 'axis-1',
'probability', 'class', 'npixels', 'area', 'circularity', 'diameter', 'perimeter', 'elongation', 'comment',
'conditions'])
df = df.sort_values(by=['pred_id'])
else:
df = pd.DataFrame(zip(list(aux[:,0]), list(aux[:,1]), list(prob), list(all_classes)),
columns =['axis-0', 'axis-1', 'probability', 'class'])
df = df.sort_values(by=['axis-0'])
del aux
# Save jus the points and their probabilities
df.to_csv(os.path.join(self.cfg.PATHS.RESULT_DIR.DET_LOCAL_MAX_COORDS_CHECK, os.path.splitext(filenames[0])[0]+'_full_info.csv'))
if self.cfg.TEST.POST_PROCESSING.DET_WATERSHED:
if ndim == 2:
cols = ['class', 'pred_id', 'npixels', 'area', 'circularity', 'perimeter', 'elongation', 'comment', 'conditions']
else:
cols = ['class', 'pred_id', 'npixels', 'volume', 'sphericity', 'perimeter', 'surface area', 'comment', 'conditions']
df = df.drop(columns=cols)
else:
df = df.drop(columns=['class'])
df.to_csv(os.path.join(self.cfg.PATHS.RESULT_DIR.DET_LOCAL_MAX_COORDS_CHECK, os.path.splitext(filenames[0])[0]+'_prob.csv'))
# Calculate detection metrics
if self.use_gt:
all_channel_d_metrics = [0,0,0]
dfs = []
gt_all_coords = []
for ch, pred_coordinates in enumerate(all_points):
# Read the GT coordinates from the CSV file
csv_filename = os.path.join(self.original_test_mask_path, os.path.splitext(filenames[0])[0]+'.csv')
if not os.path.exists(csv_filename):
print("WARNING: The CSV file seems to have different name than image. Using the CSV file "
"with the same position as the CSV in the directory. Check if it is correct!")
csv_filename = os.path.join(self.original_test_mask_path, self.csv_files[self.f_numbers[0]])
print("Its respective CSV file seems to be: {}".format(csv_filename))
print("Reading GT data from: {}".format(csv_filename))
df_gt = pd.read_csv(csv_filename, index_col=0)
zcoords = df_gt['axis-0'].tolist()
ycoords = df_gt['axis-1'].tolist()
if self.cfg.PROBLEM.NDIM == '3D':
xcoords = df_gt['axis-2'].tolist()
gt_coordinates = [[z,y,x] for z,y,x in zip(zcoords,ycoords,xcoords)]
else:
gt_coordinates = [[0,y,x] for y,x in zip(zcoords,ycoords)]
gt_all_coords.append(gt_coordinates)
if self.cfg.PROBLEM.NDIM == '3D':
v_size = (self.cfg.DATA.TEST.RESOLUTION[0], self.cfg.DATA.TEST.RESOLUTION[1], self.cfg.DATA.TEST.RESOLUTION[2])
else:
v_size = (1,self.cfg.DATA.TEST.RESOLUTION[0], self.cfg.DATA.TEST.RESOLUTION[1])
# Calculate detection metrics
if len(pred_coordinates) > 0:
print("Detection (class "+str(ch+1)+")")
d_metrics, gt_assoc, fp = detection_metrics(gt_coordinates, pred_coordinates, tolerance=self.cfg.TEST.DET_TOLERANCE[ch],
voxel_size=v_size, return_assoc=True, verbose=self.cfg.TEST.VERBOSE)
print("Detection metrics: {}".format(d_metrics))
all_channel_d_metrics[0] += d_metrics[1]
all_channel_d_metrics[1] += d_metrics[3]
all_channel_d_metrics[2] += d_metrics[5]
# Save csv files with the associations between GT points and predicted ones
dfs.append([gt_assoc.copy(),fp.copy()])
if self.cfg.PROBLEM.NDIM == "2D":
gt_assoc = gt_assoc.drop(columns=['axis-0'])
fp = fp.drop(columns=['axis-0'])
gt_assoc = gt_assoc.rename(columns={'axis-1': 'axis-0', 'axis-2': 'axis-1'})
fp = fp.rename(columns={'axis-1': 'axis-0', 'axis-2': 'axis-1'})
gt_assoc.to_csv(os.path.join(self.cfg.PATHS.RESULT_DIR.DET_ASSOC_POINTS, os.path.splitext(filenames[0])[0]+'_class'+str(ch+1)+'_gt_assoc.csv'))
fp.to_csv(os.path.join(self.cfg.PATHS.RESULT_DIR.DET_ASSOC_POINTS, os.path.splitext(filenames[0])[0]+'_class'+str(ch+1)+'_fp.csv'))
else:
print("No point found to calculate the metrics!")
print("All classes "+str(ch+1))
all_channel_d_metrics[0] = all_channel_d_metrics[0]/len(all_points)
all_channel_d_metrics[1] = all_channel_d_metrics[1]/len(all_points)
all_channel_d_metrics[2] = all_channel_d_metrics[2]/len(all_points)
print("Detection metrics: {}".format(["Precision", all_channel_d_metrics[0],
"Recall", all_channel_d_metrics[1], "F1", all_channel_d_metrics[2]]))
self.stats[metric_names[0]] += all_channel_d_metrics[0]
self.stats[metric_names[1]] += all_channel_d_metrics[1]
self.stats[metric_names[2]] += all_channel_d_metrics[2]
print("Creating the image with a summary of detected points and false positives with colors . . .")
points_pred = np.zeros(pred_shape[:-1]+(3,), dtype=np.uint8)
for ch, gt_coords in enumerate(gt_all_coords):
if len(dfs) > 0:
gt_assoc, fp = dfs[ch]
# TP and FN
gt_id_img = np.zeros(pred_shape[:-1], dtype=np.uint32)
for j, cor in enumerate(gt_coords):
z,y,x = cor
z,y,x = int(z),int(y),int(x)
if len(dfs) > 0:
if gt_assoc[gt_assoc['gt_id'] == j+1]["tag"].iloc[0] == "TP":
points_pred[z,y,x] = (0,255,0)# Green
else:
points_pred[z,y,x] = (255,0,0)# Red
else:
points_pred[z,y,x] = (255,0,0)# Red
gt_id_img[z,y,x] = j+1
# Dilate and save the GT ids for the current class
for i in range(gt_id_img.shape[0]):
gt_id_img[i] = dilation(gt_id_img[i], disk(3))
if file_ext in ['.hdf5', '.h5', ".zarr"]:
write_chunked_data(np.expand_dims(gt_id_img,-1), self.cfg.PATHS.RESULT_DIR.DET_ASSOC_POINTS,
os.path.splitext(filenames[0])[0]+'_class'+str(ch+1)+'_gt_ids'+file_ext, dtype_str="uint32",
verbose=self.cfg.TEST.VERBOSE)
else:
save_tif(np.expand_dims(np.expand_dims(gt_id_img,0),-1), self.cfg.PATHS.RESULT_DIR.DET_ASSOC_POINTS,
[os.path.splitext(filenames[0])[0]+'_class'+str(ch+1)+'_gt_ids.csv'], verbose=self.cfg.TEST.VERBOSE)
# FP
if len(dfs) > 0:
for cor in zip(fp['axis-0'].tolist(),fp['axis-1'].tolist(),fp['axis-2'].tolist()):
z, y, x = cor
z,y,x = int(z),int(y),int(x)
points_pred[z,y,x] = (0,0,255) # Blue
# Dilate and save the predicted points for the current class
for i in range(points_pred.shape[0]):
for j in range(points_pred.shape[-1]):
points_pred[i,...,j] = dilation(points_pred[i,...,j], disk(3))
if file_ext in ['.hdf5', '.h5', ".zarr"]:
write_chunked_data(points_pred, self.cfg.PATHS.RESULT_DIR.DET_ASSOC_POINTS, filenames[0],
dtype_str="uint8", verbose=self.cfg.TEST.VERBOSE)
else:
save_tif(np.expand_dims(points_pred,0), self.cfg.PATHS.RESULT_DIR.DET_ASSOC_POINTS,
filenames, verbose=self.cfg.TEST.VERBOSE)
return df
def normalize_stats(self, image_counter):
"""
Normalize statistics.
Parameters
----------
image_counter : int
Number of images to average the metrics.
"""
super().normalize_stats(image_counter)
with open(self.cell_count_file, 'w', newline="") as file:
csvwriter = csv.writer(file)
csvwriter.writerow(['filename', 'cells'])
for nr in range(len(self.cell_count_lines)):
csvwriter.writerow([nr+1] + self.cell_count_lines[nr])
if self.cfg.DATA.TEST.LOAD_GT or self.cfg.DATA.TEST.USE_VAL_AS_TEST:
if self.cfg.TEST.STATS.PER_PATCH:
self.stats['d_precision_per_crop'] = self.stats['d_precision_per_crop'] / image_counter
self.stats['d_recall_per_crop'] = self.stats['d_recall_per_crop'] / image_counter
self.stats['d_f1_per_crop'] = self.stats['d_f1_per_crop'] / image_counter
if self.cfg.TEST.STATS.FULL_IMG:
self.stats['d_precision'] = self.stats['d_precision'] / image_counter
self.stats['d_recall'] = self.stats['d_recall'] / image_counter
self.stats['d_f1'] = self.stats['d_f1'] / image_counter
def after_merge_patches(self, pred):
"""
Steps need to be done after merging all predicted patches into the original image.
Parameters
----------
pred : Torch Tensor
Model prediction.
"""
self.detection_process(pred, self.processing_filenames, ['d_precision_per_crop', 'd_recall_per_crop', 'd_f1_per_crop'])
def after_merge_patches_by_chunks_proccess_patch(self, filename):
"""
Place any code that needs to be done after merging all predicted patches into the original image
but in the process made chunk by chunk. This function will operate patch by patch defined by
``DATA.PATCH_SIZE`` + ``DATA.PADDING``.
Parameters
----------
filename : List of str
Filename of the predicted image H5/Zarr.
"""
_filename, file_ext = os.path.splitext(os.path.basename(filename))
print("Detection workflow pipeline continues for image {}".format(_filename))
# Load H5/Zarr
pred_file, pred = read_chunked_data(filename)
t_dim, z_dim, c_dim, y_dim, x_dim = order_dimensions(pred.shape,
self.cfg.TEST.BY_CHUNKS.INPUT_IMG_AXES_ORDER)
# Fill the new data
z_vols = math.ceil(z_dim/self.cfg.DATA.PATCH_SIZE[0])
y_vols = math.ceil(y_dim/self.cfg.DATA.PATCH_SIZE[1])
x_vols = math.ceil(x_dim/self.cfg.DATA.PATCH_SIZE[2])
total_patches = z_vols*y_vols*x_vols
d = len(str(total_patches))
c=1
for z in tqdm(range(z_vols)):
for y in range(y_vols):
for x in range(x_vols):
print("Processing patch {}/{} of image".format(c, total_patches))
print("D: z: {}-{}, y: {}-{}, x: {}-{}".format(z*self.cfg.DATA.PATCH_SIZE[0],min(z_dim,self.cfg.DATA.PATCH_SIZE[0]*(z+1)),
y*self.cfg.DATA.PATCH_SIZE[1],min(y_dim,self.cfg.DATA.PATCH_SIZE[1]*(y+1)),x*self.cfg.DATA.PATCH_SIZE[2],min(x_dim,self.cfg.DATA.PATCH_SIZE[2]*(x+1))))
fname = _filename+"_patch"+str(c).zfill(d)+file_ext
slices = [
slice(max(0,z*self.cfg.DATA.PATCH_SIZE[0]-self.cfg.DATA.TEST.PADDING[0]),min(z_dim,self.cfg.DATA.PATCH_SIZE[0]*(z+1)+self.cfg.DATA.TEST.PADDING[0])),
slice(max(0,y*self.cfg.DATA.PATCH_SIZE[1]-self.cfg.DATA.TEST.PADDING[1]),min(y_dim,self.cfg.DATA.PATCH_SIZE[1]*(y+1)+self.cfg.DATA.TEST.PADDING[1])),
slice(max(0,x*self.cfg.DATA.PATCH_SIZE[2]-self.cfg.DATA.TEST.PADDING[2]),min(x_dim,self.cfg.DATA.PATCH_SIZE[2]*(x+1)+self.cfg.DATA.TEST.PADDING[2])),
slice(None), # Channel
]
data_ordered_slices = order_dimensions(
slices,
input_order = "ZYXC",
output_order = self.cfg.TEST.BY_CHUNKS.INPUT_IMG_AXES_ORDER,
default_value = 0,
)
raw_patch = pred[data_ordered_slices]
current_order = np.array(range(len(pred.shape)))
transpose_order = order_dimensions(
current_order,
input_order= "ZYXC",
output_order= self.cfg.TEST.BY_CHUNKS.INPUT_IMG_AXES_ORDER,
default_value= np.nan)
transpose_order = [x for x in transpose_order if not np.isnan(x)]
transpose_order = np.argsort(transpose_order)
transpose_order = current_order[transpose_order]
patch = raw_patch.transpose(transpose_order)
df_patch = self.detection_process(patch, [fname])
if z*self.cfg.DATA.PATCH_SIZE[0]-self.cfg.DATA.TEST.PADDING[0] >=0: # if a patch was added
df_patch['axis-0'] = df_patch['axis-0'] - self.cfg.DATA.TEST.PADDING[0] # shift the coordinates to the correct patch position
if y*self.cfg.DATA.PATCH_SIZE[1]-self.cfg.DATA.TEST.PADDING[1] >=0:
df_patch['axis-1'] = df_patch['axis-1'] - self.cfg.DATA.TEST.PADDING[1]
if x*self.cfg.DATA.PATCH_SIZE[2]-self.cfg.DATA.TEST.PADDING[2] >=0:
df_patch['axis-2'] = df_patch['axis-2'] - self.cfg.DATA.TEST.PADDING[2]
df_patch = df_patch[df_patch['axis-0'] >= 0] # remove all coordinate from the previous patch
df_patch = df_patch[df_patch['axis-0'] < self.cfg.DATA.PATCH_SIZE[0]] # remove all coordinate from the next patch
df_patch = df_patch[df_patch['axis-1'] >= 0]
df_patch = df_patch[df_patch['axis-1'] < self.cfg.DATA.PATCH_SIZE[1]]
df_patch = df_patch[df_patch['axis-2'] >= 0]
df_patch = df_patch[df_patch['axis-2'] < self.cfg.DATA.PATCH_SIZE[2]]
df_patch = df_patch.reset_index(drop=True)
# add the patch shift to the detected coordinates
shift = np.array([z*self.cfg.DATA.PATCH_SIZE[0], y*self.cfg.DATA.PATCH_SIZE[1], x*self.cfg.DATA.PATCH_SIZE[2]])
df_patch['axis-0'] = df_patch['axis-0'] + shift[0]
df_patch['axis-1'] = df_patch['axis-1'] + shift[1]
df_patch['axis-2'] = df_patch['axis-2'] + shift[2]
c+=1
if 'df' not in locals():
df = df_patch.copy()
df['file'] = fname
else:
if df_patch is not None:
df_patch['file'] = fname
df = pd.concat([df, df_patch], ignore_index=True)
# Apply post-processing of removing points
if self.cfg.TEST.POST_PROCESSING.REMOVE_CLOSE_POINTS and self.postpone_postproc:
# Take point coords
pred_coordinates = []
coordz = df['axis-0'].tolist()
coordy = df['axis-1'].tolist()
coordx = df['axis-2'].tolist()
for z,y,x in zip(coordz,coordy,coordx):
pred_coordinates.append([z,y,x])
radius = self.cfg.TEST.POST_PROCESSING.REMOVE_CLOSE_POINTS_RADIUS[0]
pred_coordinates, droped_pos = remove_close_points(pred_coordinates, radius, self.cfg.DATA.TEST.RESOLUTION,
ndim=3, return_drops=True)
# Remove points from dataframe
df = df.drop(droped_pos)
# Save large csv with all point of all patches
df = df.sort_values(by=['file'])
df.to_csv(os.path.join(self.cfg.PATHS.RESULT_DIR.DET_LOCAL_MAX_COORDS_CHECK, _filename+'_all_points.csv'))
if self.cfg.TEST.BY_CHUNKS.FORMAT == "h5":
pred_file.close()
def process_sample(self, norm):
"""
Function to process a sample in the inference phase.
Parameters
----------
norm : List of dicts
Normalization used during training. Required to denormalize the predictions of the model.
"""
if self.cfg.MODEL.SOURCE != "torchvision":
super().process_sample(norm)
else:
# Data channel check
if self.cfg.DATA.PATCH_SIZE[-1] != self._X.shape[-1]:
raise ValueError("Channel of the DATA.PATCH_SIZE given {} does not correspond with the loaded image {}. "
"Please, check the channels of the images!".format(self.cfg.DATA.PATCH_SIZE[-1], self._X.shape[-1]))
##################
### FULL IMAGE ###
##################
if self.cfg.TEST.STATS.FULL_IMG:
# Make the prediction
with torch.cuda.amp.autocast():
pred = self.model_call_func(self._X)
del self._X
def torchvision_model_call(self, in_img, is_train=False):
"""
Call a regular Pytorch model.
Parameters
----------
in_img : Tensor
Input image to pass through the model.
is_train : bool, optional
Whether if the call is during training or inference.
Returns
-------
prediction : Tensor
Image prediction.
"""
filename, file_extension = os.path.splitext(self.processing_filenames[0])
# Convert first to 0-255 range if uint16
if in_img.dtype == torch.float32:
if torch.max(in_img) > 1:
in_img = (norm_range01(in_img, torch.uint8)[0]*255).to(torch.uint8)
in_img = in_img.to(torch.uint8)
# Apply TorchVision pre-processing
in_img = self.torchvision_preprocessing(in_img)
pred = self.model(in_img)
bboxes = pred[0]['boxes'].cpu().numpy()
if not is_train and len(bboxes) != 0:
# Extract each output from prediction
labels = pred[0]['labels'].cpu().numpy()
scores = pred[0]['scores'].cpu().numpy()
# Save all info in a csv file
df = pd.DataFrame(zip(labels, scores, bboxes[:,0],bboxes[:,1],bboxes[:,2],bboxes[:,3]),
columns = ['label', 'scores', 'x1', 'y1', 'x2', 'y2'])
df = df.sort_values(by=['label'])
df.to_csv(os.path.join(self.cfg.PATHS.RESULT_DIR.FULL_IMAGE, filename+".csv"), index=False)
return None
def after_full_image(self, pred):
"""
Steps that must be executed after generating the prediction by supplying the entire image to the model.
Parameters
----------
pred : Torch Tensor
Model prediction.
"""
self.detection_process(pred, self.processing_filenames, ['d_precision', 'd_recall', 'd_f1'])
def after_all_images(self):
"""
Steps that must be done after predicting all images.
"""
super().after_all_images()
def print_stats(self, image_counter):
"""
Print statistics.
Parameters
----------
image_counter : int
Number of images to call ``normalize_stats``.
"""
if not self.use_gt or self.cfg.MODEL.SOURCE == "torchvision": return
super().print_stats(image_counter)
super().print_post_processing_stats()
print("Detection specific metrics:")
if self.cfg.DATA.TEST.LOAD_GT or self.cfg.DATA.TEST.USE_VAL_AS_TEST:
if self.cfg.TEST.STATS.PER_PATCH:
print("Detection - Test Precision (merge patches): {}".format(self.stats['d_precision_per_crop']))
print("Detection - Test Recall (merge patches): {}".format(self.stats['d_recall_per_crop']))
print("Detection - Test F1 (merge patches): {}".format(self.stats['d_f1_per_crop']))
if self.cfg.TEST.STATS.FULL_IMG:
print("Detection - Test Precision (per image): {}".format(self.stats['d_precision']))
print("Detection - Test Recall (per image): {}".format(self.stats['d_recall']))
print("Detection - Test F1 (per image): {}".format(self.stats['d_f1']))
def prepare_detection_data(self):
"""
Creates detection ground truth images to train the model based on the ground truth coordinates provided.
They will be saved in a separate folder in the root path of the ground truth.
"""
print("############################")
print("# PREPARE DETECTION DATA #")
print("############################")
original_test_mask_path = None
# Create selected channels for train data
if self.cfg.TRAIN.ENABLE or self.cfg.DATA.TEST.USE_VAL_AS_TEST:
create_mask = False
if not os.path.isdir(self.cfg.DATA.TRAIN.DETECTION_MASK_DIR):
print("You select to create detection masks from given .csv files but no file is detected in {}. "
"So let's prepare the data. Notice that, if you do not modify 'DATA.TRAIN.DETECTION_MASK_DIR' "
"path, this process will be done just once!".format(self.cfg.DATA.TRAIN.DETECTION_MASK_DIR))
create_mask = True
else:
if len(next(os.walk(self.cfg.DATA.TRAIN.DETECTION_MASK_DIR))[2]) != len(next(os.walk(self.cfg.DATA.TRAIN.GT_PATH))[2]):
print("Different number of files found in {} and {}. Trying to create the the rest again"
.format(self.cfg.DATA.TRAIN.GT_PATH,self.cfg.DATA.TRAIN.DETECTION_MASK_DIR))
create_mask = True
if create_mask:
create_detection_masks(self.cfg)
# Create selected channels for val data
if self.cfg.TRAIN.ENABLE and not self.cfg.DATA.VAL.FROM_TRAIN:
create_mask = False
if not os.path.isdir(self.cfg.DATA.VAL.DETECTION_MASK_DIR):
print("You select to create detection masks from given .csv files but no file is detected in {}. "
"So let's prepare the data. Notice that, if you do not modify 'DATA.VAL.DETECTION_MASK_DIR' "
"path, this process will be done just once!".format(self.cfg.DATA.VAL.DETECTION_MASK_DIR))
create_mask = True
else:
if len(next(os.walk(self.cfg.DATA.VAL.DETECTION_MASK_DIR))[2]) != len(next(os.walk(self.cfg.DATA.VAL.GT_PATH))[2]):
print("Different number of files found in {} and {}. Trying to create the the rest again"
.format(self.cfg.DATA.VAL.GT_PATH,self.cfg.DATA.VAL.DETECTION_MASK_DIR))
create_mask = True
if create_mask:
create_detection_masks(self.cfg, data_type='val')
# Create selected channels for test data once
if self.cfg.TEST.ENABLE and self.cfg.DATA.TEST.LOAD_GT and not self.cfg.DATA.TEST.USE_VAL_AS_TEST:
create_mask = False
if not os.path.isdir(self.cfg.DATA.TEST.DETECTION_MASK_DIR):
print("You select to create detection masks from given .csv files but no file is detected in {}. "
"So let's prepare the data. Notice that, if you do not modify 'DATA.TEST.DETECTION_MASK_DIR' "
"path, this process will be done just once!".format(self.cfg.DATA.TEST.DETECTION_MASK_DIR))
create_mask = True
else:
if len(next(os.walk(self.cfg.DATA.TEST.DETECTION_MASK_DIR))[2]) != len(next(os.walk(self.cfg.DATA.TEST.GT_PATH))[2]):
print("Different number of files found in {} and {}. Trying to create the the rest again"
.format(self.cfg.DATA.TEST.GT_PATH,self.cfg.DATA.TEST.DETECTION_MASK_DIR))
create_mask = True
if create_mask:
create_detection_masks(self.cfg, data_type='test')
opts = []
if self.cfg.TRAIN.ENABLE:
print("DATA.TRAIN.GT_PATH changed from {} to {}".format(self.cfg.DATA.TRAIN.GT_PATH, self.cfg.DATA.TRAIN.DETECTION_MASK_DIR))
opts.extend(['DATA.TRAIN.GT_PATH', self.cfg.DATA.TRAIN.DETECTION_MASK_DIR])
if not self.cfg.DATA.VAL.FROM_TRAIN:
print("DATA.VAL.GT_PATH changed from {} to {}".format(self.cfg.DATA.VAL.GT_PATH, self.cfg.DATA.VAL.DETECTION_MASK_DIR))
opts.extend(['DATA.VAL.GT_PATH', self.cfg.DATA.VAL.DETECTION_MASK_DIR])
if self.cfg.TEST.ENABLE and self.cfg.DATA.TEST.LOAD_GT:
print("DATA.TEST.GT_PATH changed from {} to {}".format(self.cfg.DATA.TEST.GT_PATH, self.cfg.DATA.TEST.DETECTION_MASK_DIR))
opts.extend(['DATA.TEST.GT_PATH', self.cfg.DATA.TEST.DETECTION_MASK_DIR])
original_test_mask_path = self.cfg.DATA.TEST.GT_PATH
self.cfg.merge_from_list(opts)
return original_test_mask_path