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import os
import cv2
import time
import torch
import imageio
import tifffile
import numpy as np
import slidingwindow
import rasterio as rio
import geopandas as gpd
from shapely.geometry import Polygon
from rasterio import mask as riomask
from torch.utils.data import DataLoader
from SemanticModel.visualization import generate_color_mapping
from SemanticModel.image_preprocessing import get_validation_augmentations
from SemanticModel.data_loader import InferenceDataset, StreamingDataset
from SemanticModel.utilities import calc_image_size, convert_coordinates
class PredictionPipeline:
def __init__(self, model_config, device=None):
self.config = model_config
self.device = device or torch.device('cuda' if torch.cuda.is_available() else 'cpu')
self.classes = ['background'] + model_config.classes if model_config.background_flag else model_config.classes
self.colors = generate_color_mapping(len(self.classes))
self.model = model_config.model.to(self.device)
self.model.eval()
def _preprocess_image(self, image_path, target_size=None):
"""Preprocesses single image for prediction."""
image = cv2.imread(image_path)
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
height, width = image.shape[:2]
target_size = target_size or max(height, width)
test_height, test_width = calc_image_size(image, target_size)
augmentation = get_validation_augmentations(test_width, test_height)
image = augmentation(image=image)['image']
image = self.config.preprocessing(image=image)['image']
return image, (height, width)
def predict_single_image(self, image_path, target_size=None, output_dir=None,
format='integer', save_output=True):
"""Generates prediction for a single image."""
image, original_dims = self._preprocess_image(image_path, target_size)
x_tensor = torch.from_numpy(image).to(self.device).unsqueeze(0)
with torch.no_grad():
prediction = self.model.predict(x_tensor)
if self.config.n_classes > 1:
prediction = np.argmax(prediction.squeeze().cpu().numpy(), axis=0)
else:
prediction = prediction.squeeze().cpu().numpy().round()
# Resize to original dimensions if needed
if prediction.shape[:2] != original_dims:
prediction = cv2.resize(prediction, original_dims[::-1],
interpolation=cv2.INTER_NEAREST)
prediction = self._format_prediction(prediction, format)
if save_output:
self._save_prediction(prediction, image_path, output_dir, format)
return prediction
def predict_directory(self, input_dir, target_size=None, output_dir=None,
fixed_size=True, format='integer'):
"""Generates predictions for all images in directory."""
output_dir = output_dir or os.path.join(input_dir, 'predictions')
os.makedirs(output_dir, exist_ok=True)
dataset = InferenceDataset(
input_dir,
classes=self.classes,
augmentation=get_validation_augmentations(
target_size, target_size, fixed_size=fixed_size
) if target_size else None,
preprocessing=self.config.preprocessing
)
total_images = len(dataset)
start_time = time.time()
for idx in range(total_images):
if (idx + 1) % 10 == 0 or idx == total_images - 1:
elapsed = time.time() - start_time
print(f'\rProcessed {idx+1}/{total_images} images in {elapsed:.1f}s',
end='')
image, height, width = dataset[idx]
filename = dataset.filenames[idx]
x_tensor = torch.from_numpy(image).to(self.device).unsqueeze(0)
with torch.no_grad():
prediction = self.model.predict(x_tensor)
if self.config.n_classes > 1:
prediction = np.argmax(prediction.squeeze().cpu().numpy(), axis=0)
else:
prediction = prediction.squeeze().cpu().numpy().round()
if prediction.shape != (height, width):
prediction = cv2.resize(prediction, (width, height),
interpolation=cv2.INTER_NEAREST)
prediction = self._format_prediction(prediction, format)
self._save_prediction(prediction, filename, output_dir, format)
print(f'\nPredictions saved to: {output_dir}')
return output_dir
def predict_raster(self, raster_path, tile_size=1024, overlap=0.175,
boundary_path=None, output_path=None, format='integer'):
"""Processes large raster images using tiling approach."""
print('Loading raster...')
with rio.open(raster_path) as src:
raster = src.read()
raster = np.moveaxis(raster, 0, 2)[:,:,:3]
profile = src.profile
transform = src.transform
if boundary_path:
boundary = gpd.read_file(boundary_path)
boundary = boundary.to_crs(profile['crs'])
boundary_geom = boundary.iloc[0].geometry
tiles = slidingwindow.generate(
raster,
slidingwindow.DimOrder.HeightWidthChannel,
tile_size,
overlap
)
pred_raster = np.zeros_like(raster[:,:,0], dtype='uint8')
confidence = np.zeros_like(pred_raster, dtype=np.float32)
aug = get_validation_augmentations(tile_size, tile_size, fixed_size=False)
for idx, tile in enumerate(tiles):
if (idx + 1) % 10 == 0 or idx == len(tiles) - 1:
print(f'\rProcessed {idx+1}/{len(tiles)} tiles', end='')
bounds = tile.indices()
tile_image = raster[bounds[0], bounds[1]]
if boundary_path:
corners = [
convert_coordinates(transform, bounds[1].start, bounds[0].start),
convert_coordinates(transform, bounds[1].stop, bounds[0].start),
convert_coordinates(transform, bounds[1].stop, bounds[0].stop),
convert_coordinates(transform, bounds[1].start, bounds[0].stop)
]
if not Polygon(corners).intersects(boundary_geom):
continue
processed = aug(image=tile_image)['image']
processed = self.config.preprocessing(image=processed)['image']
x_tensor = torch.from_numpy(processed).to(self.device).unsqueeze(0)
with torch.no_grad():
prediction = self.model.predict(x_tensor)
prediction = prediction.squeeze().cpu().numpy()
if self.config.n_classes > 1:
tile_pred = np.argmax(prediction, axis=0)
tile_conf = np.max(prediction, axis=0)
else:
tile_conf = np.abs(prediction - 0.5)
tile_pred = prediction.round()
if tile_pred.shape != tile_image.shape[:2]:
tile_pred = cv2.resize(tile_pred, tile_image.shape[:2][::-1],
interpolation=cv2.INTER_NEAREST)
tile_conf = cv2.resize(tile_conf, tile_image.shape[:2][::-1],
interpolation=cv2.INTER_LINEAR)
# Update prediction and confidence maps
existing_conf = confidence[bounds[0], bounds[1]]
existing_pred = pred_raster[bounds[0], bounds[1]]
mask = existing_conf < tile_conf
existing_pred[mask] = tile_pred[mask]
existing_conf[mask] = tile_conf[mask]
pred_raster[bounds[0], bounds[1]] = existing_pred
confidence[bounds[0], bounds[1]] = existing_conf
pred_raster = self._format_prediction(pred_raster, format)
if output_path or boundary_path:
self._save_raster_prediction(
pred_raster, raster_path, output_path,
profile, boundary_geom if boundary_path else None
)
return pred_raster, profile
def _format_prediction(self, prediction, format):
"""Formats prediction according to specified output type."""
if format == 'integer':
return prediction.astype('uint8')
elif format == 'color':
return self._apply_color_mapping(prediction)
else:
raise ValueError(f"Unsupported format: {format}")
def _save_prediction(self, prediction, source_path, output_dir, format):
"""Saves prediction to disk."""
filename = os.path.splitext(os.path.basename(source_path))[0]
output_path = os.path.join(output_dir, f"{filename}_pred.png")
cv2.imwrite(output_path, prediction)
def _save_raster_prediction(self, prediction, source_path, output_path,
profile, boundary=None):
"""Saves raster prediction with geospatial information."""
output_path = output_path or source_path.replace(
os.path.splitext(source_path)[1], '_predicted.tif'
)
profile.update(
dtype='uint8',
count=3 if prediction.ndim == 3 else 1
)
with rio.open(output_path, 'w', **profile) as dst:
if prediction.ndim == 3:
for i in range(3):
dst.write(prediction[:,:,i], i+1)
else:
dst.write(prediction, 1)
if boundary:
with rio.open(output_path) as src:
cropped, transform = riomask.mask(src, [boundary], crop=True)
profile.update(
height=cropped.shape[1],
width=cropped.shape[2],
transform=transform
)
os.remove(output_path)
with rio.open(output_path, 'w', **profile) as dst:
dst.write(cropped)
print(f'\nPrediction saved to: {output_path}')
def predict_video_frames(self, input_dir, target_size=None, output_dir=None):
"""Processes video frames with specialized visualization."""
output_dir = output_dir or os.path.join(input_dir, 'predictions')
os.makedirs(output_dir, exist_ok=True)
dataset = StreamingDataset(
input_dir,
classes=self.classes,
augmentation=get_validation_augmentations(
target_size, target_size
) if target_size else None,
preprocessing=self.config.preprocessing
)
image = cv2.imread(dataset.image_paths[0])
height, width = image.shape[:2]
white = 255 * np.ones((height, width))
black = np.zeros_like(white)
red = np.dstack((white, black, black))
blue = np.dstack((black, black, white))
# Pre-compute rotated versions
rotated_red = np.rot90(red)
rotated_blue = np.rot90(blue)
total_frames = len(dataset)
start_time = time.time()
for idx in range(total_frames):
if (idx + 1) % 10 == 0 or idx == total_frames - 1:
elapsed = time.time() - start_time
print(f'\rProcessed {idx+1}/{total_frames} frames in {elapsed:.1f}s', end='')
frame, height, width = dataset[idx]
filename = dataset.filenames[idx]
x_tensor = torch.from_numpy(frame).to(self.device).unsqueeze(0)
with torch.no_grad():
prediction = self.model.predict(x_tensor)
if self.config.n_classes > 1:
prediction = np.argmax(prediction.squeeze().cpu().numpy(), axis=0)
masks = [prediction == i for i in range(1, self.config.n_classes)]
else:
prediction = prediction.squeeze().cpu().numpy().round()
masks = [prediction == 1]
if prediction.shape != (height, width):
prediction = cv2.resize(prediction, (width, height),
interpolation=cv2.INTER_NEAREST)
original = cv2.imread(os.path.join(input_dir, filename))
original = cv2.cvtColor(original, cv2.COLOR_BGR2RGB)
try:
for i, mask in enumerate(masks):
color = red if i == 0 else blue
rotated_color = rotated_red if i == 0 else rotated_blue
try:
original[mask,:] = 0.45*original[mask,:] + 0.55*color[mask,:]
except:
original[mask,:] = 0.45*original[mask,:] + 0.55*rotated_color[mask,:]
except:
print(f"\nWarning: Error processing frame {filename}")
continue
output_path = os.path.join(output_dir, filename)
imageio.imwrite(output_path, original, quality=100)
print(f'\nProcessed frames saved to: {output_dir}')
return output_dir
def _apply_color_mapping(self, prediction):
"""Applies color mapping to prediction."""
height, width = prediction.shape
colored = np.zeros((height, width, 3), dtype='uint8')
for i, class_name in enumerate(self.classes):
if class_name.lower() == 'background':
continue
color = self.colors[i]
colored[prediction == i] = color
return colored |