mirror of
https://github.com/azaion/annotations.git
synced 2026-04-22 22:36:31 +00:00
Alex Bezdieniezhnykh
291 lines
11 KiB
Cython
291 lines
11 KiB
Cython
import json
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import mimetypes
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import os
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import subprocess
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import sys
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import time
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import cv2
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import numpy as np
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cimport constants
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from remote_command cimport RemoteCommand
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from annotation cimport Detection, Annotation
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from ai_config cimport AIRecognitionConfig
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from hardware_service cimport HardwareService
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from security cimport Security
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if HardwareService.has_nvidia_gpu():
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from tensorrt_engine import TensorRTEngine
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else:
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from onnx_engine import OnnxEngine
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cdef class Inference:
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def __init__(self, api_client, on_annotation):
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self.api_client = api_client
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self.on_annotation = on_annotation
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self.stop_signal = False
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self.model_input = None
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self.model_width = 0
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self.model_height = 0
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self.engine = None
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self.is_building_engine = False
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cdef build_tensor_engine(self):
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is_nvidia = HardwareService.has_nvidia_gpu()
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if not is_nvidia:
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return
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engine_filename = TensorRTEngine.get_engine_filename(0)
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key = Security.get_model_encryption_key()
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models_dir = constants.MODELS_FOLDER
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if not os.path.exists(os.path.join(<str> models_dir, f'{engine_filename}.big')):
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#TODO: Check cdn on engine exists, if there is, download
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self.is_building_engine = True
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time.sleep(8) # prevent simultaneously loading dll and models
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onnx_model = self.api_client.load_big_small_resource(constants.AI_ONNX_MODEL_FILE, models_dir, key)
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model_bytes = TensorRTEngine.convert_from_onnx(onnx_model)
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self.api_client.upload_big_small_resource(model_bytes, <str> engine_filename, models_dir, key)
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print('uploaded ')
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self.is_building_engine = False
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else:
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print('tensor rt engine is here, no need to build')
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cdef init_ai(self):
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if self.engine is not None:
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return
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is_nvidia = HardwareService.has_nvidia_gpu()
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key = Security.get_model_encryption_key()
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models_dir = constants.MODELS_FOLDER
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if is_nvidia:
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while self.is_building_engine:
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time.sleep(1)
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engine_filename = TensorRTEngine.get_engine_filename(0)
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model_bytes = self.api_client.load_big_small_resource(engine_filename, models_dir, key)
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self.engine = TensorRTEngine(model_bytes)
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else:
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model_bytes = self.api_client.load_big_small_resource(constants.AI_ONNX_MODEL_FILE, models_dir, key)
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self.engine = OnnxEngine(model_bytes)
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self.model_height, self.model_width = self.engine.get_input_shape()
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cdef preprocess(self, frames):
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blobs = [cv2.dnn.blobFromImage(frame,
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scalefactor=1.0 / 255.0,
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size=(self.model_width, self.model_height),
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mean=(0, 0, 0),
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swapRB=True,
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crop=False)
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for frame in frames]
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return np.vstack(blobs)
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cdef postprocess(self, output, ai_config):
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cdef list[Detection] detections = []
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cdef int ann_index
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cdef float x1, y1, x2, y2, conf, cx, cy, w, h
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cdef int class_id
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cdef list[list[Detection]] results = []
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try:
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for ann_index in range(len(output[0])):
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detections.clear()
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for det in output[0][ann_index]:
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if det[4] == 0: # if confidence is 0 then valid points are over.
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break
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x1 = det[0] / self.model_width
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y1 = det[1] / self.model_height
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x2 = det[2] / self.model_width
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y2 = det[3] / self.model_height
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conf = round(det[4], 2)
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class_id = int(det[5])
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x = (x1 + x2) / 2
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y = (y1 + y2) / 2
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w = x2 - x1
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h = y2 - y1
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if conf >= ai_config.probability_threshold:
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detections.append(Detection(x, y, w, h, class_id, conf))
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filtered_detections = self.remove_overlapping_detections(detections)
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results.append(filtered_detections)
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return results
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except Exception as e:
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raise RuntimeError(f"Failed to postprocess: {str(e)}")
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cdef remove_overlapping_detections(self, list[Detection] detections):
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cdef Detection det1, det2
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filtered_output = []
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filtered_out_indexes = []
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for det1_index in range(len(detections)):
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if det1_index in filtered_out_indexes:
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continue
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det1 = detections[det1_index]
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res = det1_index
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for det2_index in range(det1_index + 1, len(detections)):
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det2 = detections[det2_index]
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if det1.overlaps(det2):
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if det1.confidence > det2.confidence or (
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det1.confidence == det2.confidence and det1.cls < det2.cls): # det1 has higher confidence or lower class_id
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filtered_out_indexes.append(det2_index)
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else:
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filtered_out_indexes.append(res)
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res = det2_index
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filtered_output.append(detections[res])
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filtered_out_indexes.append(res)
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return filtered_output
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cdef bint is_video(self, str filepath):
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mime_type, _ = mimetypes.guess_type(<str>filepath)
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return mime_type and mime_type.startswith("video")
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cdef split_list_extend(self, lst, chunk_size):
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chunks = [lst[i:i + chunk_size] for i in range(0, len(lst), chunk_size)]
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# If the last chunk is smaller than the desired chunk_size, extend it by duplicating its last element.
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last_chunk = chunks[len(chunks) - 1]
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if len(last_chunk) < chunk_size:
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last_elem = last_chunk[len(last_chunk)-1]
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while len(last_chunk) < chunk_size:
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last_chunk.append(last_elem)
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return chunks
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cdef run_inference(self, RemoteCommand cmd):
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cdef list[str] videos = []
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cdef list[str] images = []
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cdef AIRecognitionConfig ai_config = AIRecognitionConfig.from_msgpack(cmd.data)
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if ai_config is None:
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raise Exception('ai recognition config is empty')
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self.stop_signal = False
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self.init_ai()
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print(ai_config.paths)
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for m in ai_config.paths:
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if self.is_video(m):
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videos.append(m)
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else:
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images.append(m)
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# images first, it's faster
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if len(images) > 0:
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for chunk in self.split_list_extend(images, self.engine.get_batch_size()):
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print(f'run inference on {" ".join(chunk)}...')
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self._process_images(cmd, ai_config, chunk)
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if len(videos) > 0:
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for v in videos:
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print(f'run inference on {v}...')
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self._process_video(cmd, ai_config, v)
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cdef _process_video(self, RemoteCommand cmd, AIRecognitionConfig ai_config, str video_name):
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cdef int frame_count = 0
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cdef list batch_frames = []
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cdef list[int] batch_timestamps = []
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self._previous_annotation = None
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v_input = cv2.VideoCapture(<str>video_name)
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while v_input.isOpened() and not self.stop_signal:
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ret, frame = v_input.read()
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if not ret or frame is None:
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break
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frame_count += 1
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if frame_count % ai_config.frame_period_recognition == 0:
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batch_frames.append(frame)
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batch_timestamps.append(int(v_input.get(cv2.CAP_PROP_POS_MSEC)))
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if len(batch_frames) == self.engine.get_batch_size():
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input_blob = self.preprocess(batch_frames)
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outputs = self.engine.run(input_blob)
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list_detections = self.postprocess(outputs, ai_config)
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for i in range(len(list_detections)):
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detections = list_detections[i]
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annotation = Annotation(video_name, batch_timestamps[i], detections)
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if self.is_valid_annotation(annotation, ai_config):
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_, image = cv2.imencode('.jpg', batch_frames[i])
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annotation.image = image.tobytes()
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self._previous_annotation = annotation
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print(annotation)
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self.on_annotation(cmd, annotation)
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batch_frames.clear()
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batch_timestamps.clear()
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v_input.release()
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cdef _process_images(self, RemoteCommand cmd, AIRecognitionConfig ai_config, list[str] image_paths):
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cdef list frames = []
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cdef list timestamps = []
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self._previous_annotation = None
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for image in image_paths:
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frame = cv2.imread(image)
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frames.append(frame)
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timestamps.append(0)
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input_blob = self.preprocess(frames)
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outputs = self.engine.run(input_blob)
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list_detections = self.postprocess(outputs, ai_config)
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for i in range(len(list_detections)):
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detections = list_detections[i]
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annotation = Annotation(image_paths[i], timestamps[i], detections)
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_, image = cv2.imencode('.jpg', frames[i])
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annotation.image = image.tobytes()
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self.on_annotation(cmd, annotation)
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cdef stop(self):
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self.stop_signal = True
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cdef bint is_valid_annotation(self, Annotation annotation, AIRecognitionConfig ai_config):
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# No detections, invalid
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if not annotation.detections:
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return False
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# First valid annotation, always accept
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if self._previous_annotation is None:
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return True
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# Enough time has passed since last annotation
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if annotation.time >= self._previous_annotation.time + <long>(ai_config.frame_recognition_seconds * 1000):
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return True
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# More objects detected than before
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if len(annotation.detections) > len(self._previous_annotation.detections):
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return True
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cdef:
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Detection current_det, prev_det
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double dx, dy, distance_sq, min_distance_sq
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Detection closest_det
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# Check each detection against previous frame
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for current_det in annotation.detections:
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min_distance_sq = 1e18 # Initialize with large value
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closest_det = None
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# Find the closest detection in previous frame
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for prev_det in self._previous_annotation.detections:
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dx = current_det.x - prev_det.x
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dy = current_det.y - prev_det.y
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distance_sq = dx * dx + dy * dy
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if distance_sq < min_distance_sq:
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min_distance_sq = distance_sq
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closest_det = prev_det
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# Check if beyond tracking distance
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if min_distance_sq > ai_config.tracking_distance_confidence:
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return True
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# Check probability increase
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if current_det.confidence >= closest_det.confidence + ai_config.tracking_probability_increase:
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return True
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return False
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