213 lines
7.4 KiB
Python
213 lines
7.4 KiB
Python
import time
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import numpy as np
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from django.conf import settings
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from django.utils import timezone
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from .algorithms import ProcessingError, average_images, decode_image, histogram, histogram_payload, normalize_to_uint8, process_image, verify_registration
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from .models import ImageSession, ImageState, ProcessingJob
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from .registry import apply_registered_operation
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from .storage import load_image_array, payload_for_image, save_image_array
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from .tasks import run_batch_job
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def image_session_create(*, uploaded_file=None, image_base64=None):
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if uploaded_file and uploaded_file.size > settings.MAX_UPLOAD_MB * 1024 * 1024:
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raise ProcessingError(f"Upload exceeds {settings.MAX_UPLOAD_MB} MB.")
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image = decode_image(uploaded_file=uploaded_file, base64_image=image_base64)
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relative_path = save_image_array(image, "original")
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hist = histogram(image)
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session = ImageSession.objects.create(
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original_image=relative_path,
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width=image.shape[1],
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height=image.shape[0],
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channels=image.shape[2] if image.ndim == 3 else 1,
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color_mode="RGB" if image.ndim == 3 else "L",
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original_histogram=hist,
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expires_at=timezone.now() + timezone.timedelta(hours=settings.IMAGE_SESSION_TTL_HOURS),
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)
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state = image_state_create(
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session=session,
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parent=None,
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image=image,
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operation="upload",
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params={},
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label="S0 Upload",
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prefix="state-upload",
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)
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payload = {
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"session_id": str(session.id),
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"active_state_id": str(state.id),
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"width": session.width,
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"height": session.height,
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"channels": session.channels,
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"color_mode": session.color_mode,
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"original_histogram": hist,
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"histogram": state.histogram,
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"expires_at": session.expires_at.isoformat(),
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"states": [image_state_payload(state=state, include_image=True)],
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}
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payload.update(payload_for_image(image, relative_path))
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return payload
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def image_state_create(*, session, parent, image, operation, params, label=None, prefix="state"):
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relative_path = save_image_array(image, prefix)
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sequence = session.states.count()
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state = ImageState.objects.create(
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session=session,
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parent=parent,
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sequence=sequence,
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label=label or f"S{sequence} {operation}",
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operation=operation,
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params=params or {},
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image=relative_path,
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width=image.shape[1],
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height=image.shape[0],
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channels=image.shape[2] if image.ndim == 3 else 1,
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color_mode="RGB" if image.ndim == 3 else "L",
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histogram=histogram_payload(image),
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)
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return state
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def image_state_payload(*, state, include_image=True):
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payload = {
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"state_id": str(state.id),
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"session_id": str(state.session_id),
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"parent_state_id": str(state.parent_id) if state.parent_id else None,
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"sequence": state.sequence,
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"label": state.label,
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"operation": state.operation,
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"params": state.params,
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"width": state.width,
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"height": state.height,
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"channels": state.channels,
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"color_mode": state.color_mode,
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"histogram": state.histogram,
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"created_at": state.created_at.isoformat(),
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}
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if include_image:
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image = load_image_array(state.image)
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payload.update(payload_for_image(image, state.image))
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else:
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payload["image_path"] = state.image
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payload["image_url"] = f"{settings.MEDIA_URL}{state.image}"
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return payload
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def image_states_payload(*, states):
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return [image_state_payload(state=state, include_image=True) for state in states]
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def image_state_apply_operation(*, state, operation, params):
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if state.session.expired:
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raise ProcessingError("Image session has expired.")
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source = load_image_array(state.image)
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result = apply_registered_operation(source, operation, params or {})
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new_state = image_state_create(
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session=state.session,
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parent=state,
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image=result,
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operation=operation,
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params=params or {},
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label=None,
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prefix=f"state-{operation}",
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)
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return image_state_payload(state=new_state, include_image=True)
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def combine_states(*, states, operation, params=None):
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params = params or {}
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if len(states) < 2:
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raise ProcessingError("At least two states are required.")
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session = states[0].session
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if any(state.session_id != session.id for state in states):
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raise ProcessingError("All states must belong to the same session.")
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images = [load_image_array(state.image) for state in states]
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verify_registration(images)
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if operation == "average":
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result = average_images(images)
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elif operation == "add":
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result = np_clip_sum(images)
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elif operation == "subtract":
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result = normalize_to_uint8(images[0].astype("float32") - images[1].astype("float32"))
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elif operation == "dot_product":
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result = normalize_to_uint8(np.prod([image.astype("float32") / 255.0 for image in images], axis=0))
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elif operation == "and":
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result = images[0].copy()
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for image in images[1:]:
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result = result & image
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elif operation == "or":
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result = images[0].copy()
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for image in images[1:]:
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result = result | image
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else:
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raise ProcessingError(f"Unsupported combine operation '{operation}'.")
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new_state = image_state_create(
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session=session,
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parent=states[0],
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image=result,
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operation=f"combine_{operation}",
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params={**params, "state_ids": [str(state.id) for state in states]},
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label=None,
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prefix=f"state-combine-{operation}",
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)
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return image_state_payload(state=new_state, include_image=True)
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def np_clip_sum(images):
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total = np.zeros_like(images[0], dtype="float32")
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for image in images:
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total += image.astype("float32")
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return np.clip(total, 0, 255).astype("uint8")
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def image_session_process(*, session, operation, params):
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if session.expired:
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raise ProcessingError("Image session has expired.")
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started_at = time.perf_counter()
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source = load_image_array(session.original_image)
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result = process_image(source, operation, params)
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relative_path = save_image_array(result, f"processed-{operation}")
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hist = histogram(result)
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session.processed_image = relative_path
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session.processed_histogram = hist
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session.save(update_fields=["processed_image", "processed_histogram"])
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payload = {
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"session_id": str(session.id),
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"operation": operation,
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"params": params,
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"processed_histogram": hist,
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"elapsed_ms": round((time.perf_counter() - started_at) * 1000, 2),
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}
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payload.update(payload_for_image(result, relative_path))
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return payload
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def batch_job_create(*, operation, session_ids, params=None):
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job = ProcessingJob.objects.create(operation=operation, params=params or {})
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run_batch_job.delay(str(job.id), operation, [str(session_id) for session_id in session_ids])
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return job
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def processing_job_payload(*, job):
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payload = {
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"job_id": str(job.id),
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"operation": job.operation,
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"status": job.status,
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"progress": job.progress,
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"error": job.error,
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"result_histogram": job.result_histogram,
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}
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if job.result_image:
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image = load_image_array(job.result_image)
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payload.update(payload_for_image(image, job.result_image))
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return payload
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