stuff
This commit is contained in:
The Bears
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import numpy as np
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from functools import partial
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from hailo_platform import VDevice, HailoSchedulingAlgorithm, FormatType
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import cv2
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import numpy as np
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def resize_image(img_in, reshape_to_final=True):
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if not isinstance(img_in, np.ndarray):
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img_in = np.asarray(img_in)
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max_l = 640
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asp_rat = img_in.shape[0] / img_in.shape[1]
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if asp_rat < 1:
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output_size = [int(asp_rat * max_l), max_l]
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else:
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output_size = [max_l, int(max_l / asp_rat)]
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im_arr_not_pad = cv2.resize(img_in, output_size[::-1])
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pad_amt = [max_l, max_l] - np.asarray(im_arr_not_pad.shape[0:2])
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left_pad, top_pad = (pad_amt / 2).astype(np.int64)
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right_pad, bottom_pad = pad_amt - [left_pad, top_pad]
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im_pass = np.zeros(shape=(max_l, max_l, 3), dtype=np.uint8)
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im_pass[left_pad:(max_l - right_pad),
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top_pad:(max_l - bottom_pad)] = (im_arr_not_pad)
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data = im_pass
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if reshape_to_final:
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data = np.moveaxis(data, [2], [0])[None, :, :, :]
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return data
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def model_scoring_callback(completion_info, bindings, data):
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if completion_info.exception:
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# handle exception
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pass
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ff = bindings.output().get_buffer()
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timeout_ms = 1000
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params = VDevice.create_params()
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params.scheduling_algorithm = HailoSchedulingAlgorithm.ROUND_ROBIN
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import time
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# The vdevice is used as a context manager ("with" statement) to ensure it's released on time.
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with VDevice(params) as vdevice:
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# Create an infer model from an HEF:
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infer_model = vdevice.create_infer_model("yolov11l_inat.hef")
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# Configure the infer model and create bindings for it
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with infer_model.configure() as configured_infer_model:
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bindings = configured_infer_model.create_bindings()
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st = time.time()
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for i in range(1):
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# Set input and output buffers
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buffer = inp # np.zeros(infer_model.input().shape).astype(np.uint8)
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bindings.input().set_buffer(buffer)
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output_array = np.zeros([infer_model.output().shape[0]
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]).astype(np.float32)
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bindings.output().set_buffer(output_array)
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# Run synchronous inference and access the output buffers
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configured_infer_model.run([bindings], timeout_ms)
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buffer = bindings.output().get_buffer()
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# Run asynchronous inference
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job = configured_infer_model.run_async(
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[bindings],
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partial(example_callback, bindings=bindings, data=time.time()),
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)
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job.wait(timeout_ms)
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# %%
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import cv2
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import time
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import multiprocessing
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import numpy as np
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import ctypes
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import shutil
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from utils import *
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cameras = {
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"camera_sidefeeder": {
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'url': "rtsp://admin:marybear@192.168.1.157:554/h264Preview_01_sub",
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'resolution': (480, 640, 3)
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},
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"camera_driveway": {
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'url': "rtsp://admin:marybear@192.168.1.152:554/h264Preview_01_sub",
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'resolution': (480, 640, 3)
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},
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"camera_railing": {
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'url': "rtsp://admin:marybear@192.168.1.153:554/h264Preview_01_sub",
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'resolution': (512, 896, 3)
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},
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"camera_ptz_right": {
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'url': "rtsp://admin:marybear@192.168.1.155:554/h264Preview_01_sub",
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'resolution': (360, 640, 3)
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},
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"camera_wrenwatch": {
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'url': "rtsp://admin:marybear@192.168.1.158:554/h264Preview_01_sub",
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'resolution': (360, 640, 3)
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},
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"camera_backyard": {
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'url': "rtsp://admin:marybear@192.168.1.162:554/h264Preview_01_sub",
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'resolution': (432, 1536, 3),
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'split_into_two': True
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},
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}
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from functools import partial
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cameras = dict()
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for cam_name, details in cameras.items():
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rtsp_url = details['url']
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resolution = details['resolution']
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cameras[cam_name] = StreamManager( rtsp_url, resolution, cam_name)
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# %%
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details['cam_name'] = cam_name
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details['img_array'] = multiprocessing.Array(ctypes.c_uint8,
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int(array_len),
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lock=True)
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details['img_timestamp'] = multiprocessing.Value(ctypes.c_double)
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details['queue'] = multiprocessing.Queue()
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details['rtsp_url'] = format_ffmpeg_decode_url(details['url']).split(" ")
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details['process_func'] = partial(stream_wrapper,
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details['resolution'],
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details['rtsp_url'],
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camera_name=cam_name,
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queue=details['queue'],
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img_array=details['img_array'],
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img_timestamp=details['img_timestamp'])
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for cam_name, details in cameras.items():
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p = multiprocessing.Process(target = details['process_func'])
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details['process'] = p
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p.start()
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# %%
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img_array = details['img_array']
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img_timestamp = details['img_timestamp']
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import time
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with img_timestamp.get_lock():
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img_timestamp.value = -1
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details['queue'].put('get')
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for i in range(1000):
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val = img_timestamp.value
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print(val)
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if val > 0:
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print('Done')
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break
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time.sleep(0.001)
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# %%
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for cam_name, details in cameras.items():
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details['queue'].put('get')
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img_array = details['img_array']
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img_timestamp = details['img_timestamp']
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with img_array.get_lock(), img_timestamp.get_lock():
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reshaped_image = np.frombuffer(details['img_array'].get_obj(),
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dtype=np.uint8).reshape(
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details['resolution'])
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timestamp = img_timestamp.value
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img_timestamp.value = -1
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print('Writing for ' + cam_name + f' for {reshaped_image.shape}')
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cv2.imwrite('images/'+ cam_name + '.jpg', reshaped_image)
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# %%
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import asyncio
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img_scoring_queue = multiprocessing.Queue()
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for cam_name, details in cameras.items():
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p = multiprocessing.Process(target=rtsp_stream_manager,
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args=(cam_name, details['gst_pipeline_str'],
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details['queue'], details['img_array'],
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details['img_timestamp']))
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details['process'] = p
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asyncio.create_task(details['async_task'])
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shape = (512, 896, 3)
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asyncio.run(read_stream(shape, cmd))
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import datetime as dt
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for cam_name, details in cameras.items():
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details['process'].start()
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# %%
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for cam_name, details in cameras.items():
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details['queue'].put('restart')
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# %%
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for cam_name, details in cameras.items():
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details['queue'].put('get')
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if os.path.exists('images/'):
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shutil.rmtree('images/')
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os.makedirs('images/')
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def create_score_message(details, reshaped_image, timestamp):
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frames = list()
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msg = list()
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if details.get('split_into_two', False):
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split_point = int(reshaped_image.shape[1] / 2)
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left_frame = resize_image(reshaped_image[:, :split_point, :],
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reshape_to_final=False)
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right_frame = resize_image(reshaped_image[:, split_point:, :],
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reshape_to_final=False)
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left_frame = cv2.cvtColor(left_frame, cv2.COLOR_BGR2RGB)
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right_frame = cv2.cvtColor(right_frame, cv2.COLOR_BGR2RGB)
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msg.append({
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'camera_name': details['cam_name'] + '_left',
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'frame': left_frame,
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'image_timestamp': timestamp
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})
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msg.append({
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'camera_name': details['cam_name'] + '_right',
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'frame': right_frame,
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'image_timestamp': timestamp
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})
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else:
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frame = resize_image(reshaped_image, reshape_to_final=False)
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frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
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msg.append({
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'camera_name': details['cam_name'],
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'frame': frame,
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'image_timestamp': timestamp
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})
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return msg
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for x in range(img_scoring_queue.qsize()):
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qu = img_scoring_queue.get()
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print(qu['camera_name'], qu['frame'].shape)
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cv2.imwrite(str(x) + '.jpg', qu['frame'])
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2984
hailort.log
2984
hailort.log
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306
run_me.py
306
run_me.py
@@ -1,92 +1,15 @@
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import numpy as np
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from functools import partial
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from hailo_platform import VDevice, HailoSchedulingAlgorithm, FormatType
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from common_code.settings import get_logger
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from utils import StreamManager, run_model
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import multiprocessing as mp
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import cv2
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import numpy as np
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logger = get_logger('live_inference',file_path = '/var/log/live_inference.log', stdout=True)
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def resize_image(img_in, reshape_to_final=True):
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if not isinstance(img_in, np.ndarray):
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img_in = np.asarray(img_in)
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max_l = 640
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asp_rat = img_in.shape[0] / img_in.shape[1]
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if asp_rat < 1:
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output_size = [int(asp_rat * max_l), max_l]
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else:
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output_size = [max_l, int(max_l / asp_rat)]
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im_arr_not_pad = cv2.resize(img_in, output_size[::-1])
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pad_amt = [max_l, max_l] - np.asarray(im_arr_not_pad.shape[0:2])
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left_pad, top_pad = (pad_amt / 2).astype(np.int64)
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right_pad, bottom_pad = pad_amt - [left_pad, top_pad]
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im_pass = np.zeros(shape=(max_l, max_l, 3), dtype=np.uint8)
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im_pass[left_pad:(max_l - right_pad),
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top_pad:(max_l - bottom_pad)] = (im_arr_not_pad)
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data = im_pass
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if reshape_to_final:
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data = np.moveaxis(data, [2], [0])[None, :, :, :]
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return data
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def model_scoring_callback(completion_info, bindings, data):
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if completion_info.exception:
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# handle exception
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pass
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ff = bindings.output().get_buffer()
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timeout_ms = 1000
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params = VDevice.create_params()
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params.scheduling_algorithm = HailoSchedulingAlgorithm.ROUND_ROBIN
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import time
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# The vdevice is used as a context manager ("with" statement) to ensure it's released on time.
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with VDevice(params) as vdevice:
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# Create an infer model from an HEF:
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infer_model = vdevice.create_infer_model("yolov11l_inat.hef")
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# Configure the infer model and create bindings for it
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with infer_model.configure() as configured_infer_model:
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bindings = configured_infer_model.create_bindings()
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st = time.time()
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for i in range(1):
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# Set input and output buffers
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buffer = inp # np.zeros(infer_model.input().shape).astype(np.uint8)
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bindings.input().set_buffer(buffer)
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output_array = np.zeros([infer_model.output().shape[0]
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]).astype(np.float32)
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bindings.output().set_buffer(output_array)
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# Run synchronous inference and access the output buffers
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configured_infer_model.run([bindings], timeout_ms)
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buffer = bindings.output().get_buffer()
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# Run asynchronous inference
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job = configured_infer_model.run_async(
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[bindings],
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partial(example_callback, bindings=bindings, data=time.time()),
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)
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job.wait(timeout_ms)
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# %%
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import cv2
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import time
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import multiprocessing
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import numpy as np
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import ctypes
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import shutil
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from utils import *
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cameras = {
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all_cameras_config = {
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"camera_sidefeeder": {
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'url': "rtsp://admin:marybear@192.168.1.157:554/h264Preview_01_sub",
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'resolution': (480, 640, 3)
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'resolution': (360, 640, 3)
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},
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"camera_driveway": {
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'url': "rtsp://admin:marybear@192.168.1.152:554/h264Preview_01_sub",
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@@ -96,7 +19,7 @@ cameras = {
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'url': "rtsp://admin:marybear@192.168.1.153:554/h264Preview_01_sub",
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'resolution': (512, 896, 3)
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},
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"camera_ptz_right": {
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"camera_ptz": {
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'url': "rtsp://admin:marybear@192.168.1.155:554/h264Preview_01_sub",
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'resolution': (360, 640, 3)
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},
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@@ -111,145 +34,104 @@ cameras = {
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},
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}
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cameras_config = dict()
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k_c = 'camera_railing'
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#cameras_config[k_c] = all_cameras_config[k_c]
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cameras_config = all_cameras_config
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from functools import partial
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cameras = dict()
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for cam_name, details in cameras.items():
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rtsp_url = details['url']
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from random import random
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img_scoring_queue = mp.Queue(maxsize = len(cameras_config)*2)
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for cam_name, details in cameras_config.items():
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rtsp_url = details['url']
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if random() < -1:
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rtsp_url = 'rtsp://admin:marybear@192.168.1.124'
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print(f"Switching RTSP stream for {cam_name}")
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resolution = details['resolution']
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cameras[cam_name] = StreamManager( rtsp_url, resolution, cam_name)
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cameras[cam_name] = StreamManager(rtsp_url=rtsp_url,
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resolution=resolution,
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img_scoring_queue=img_scoring_queue,
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camera_name=cam_name,
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split_into_two=details.get(
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'split_into_two', False))
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cameras[cam_name].start_process()
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logger.info(f'Started StreamManager for {cam_name}')
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# %%
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details['cam_name'] = cam_name
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details['img_array'] = multiprocessing.Array(ctypes.c_uint8,
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int(array_len),
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lock=True)
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details['img_timestamp'] = multiprocessing.Value(ctypes.c_double)
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details['queue'] = multiprocessing.Queue()
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details['rtsp_url'] = format_ffmpeg_decode_url(details['url']).split(" ")
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details['process_func'] = partial(stream_wrapper,
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details['resolution'],
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details['rtsp_url'],
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camera_name=cam_name,
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queue=details['queue'],
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img_array=details['img_array'],
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img_timestamp=details['img_timestamp'])
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pp = mp.Process(target=run_model, args=(img_scoring_queue, ))
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pp.start()
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logger.info(f'Started model scoring process')
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for cam_name, details in cameras.items():
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p = multiprocessing.Process(target = details['process_func'])
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details['process'] = p
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p.start()
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# %%
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img_array = details['img_array']
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img_timestamp = details['img_timestamp']
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import time
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with img_timestamp.get_lock():
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img_timestamp.value = -1
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details['queue'].put('get')
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for i in range(1000):
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val = img_timestamp.value
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print(val)
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if val > 0:
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print('Done')
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break
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time.sleep(0.001)
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# %%
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for cam_name, details in cameras.items():
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details['queue'].put('get')
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img_array = details['img_array']
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img_timestamp = details['img_timestamp']
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with img_array.get_lock(), img_timestamp.get_lock():
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reshaped_image = np.frombuffer(details['img_array'].get_obj(),
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dtype=np.uint8).reshape(
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details['resolution'])
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timestamp = img_timestamp.value
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img_timestamp.value = -1
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print('Writing for ' + cam_name + f' for {reshaped_image.shape}')
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cv2.imwrite('images/'+ cam_name + '.jpg', reshaped_image)
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# %%
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import asyncio
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img_scoring_queue = multiprocessing.Queue()
|
||||
|
||||
for cam_name, details in cameras.items():
|
||||
p = multiprocessing.Process(target=rtsp_stream_manager,
|
||||
args=(cam_name, details['gst_pipeline_str'],
|
||||
details['queue'], details['img_array'],
|
||||
details['img_timestamp']))
|
||||
details['process'] = p
|
||||
|
||||
asyncio.create_task(details['async_task'])
|
||||
|
||||
shape = (512, 896, 3)
|
||||
|
||||
asyncio.run(read_stream(shape, cmd))
|
||||
|
||||
import datetime as dt
|
||||
|
||||
for cam_name, details in cameras.items():
|
||||
details['process'].start()
|
||||
# %%
|
||||
for cam_name, details in cameras.items():
|
||||
details['queue'].put('restart')
|
||||
# %%
|
||||
for cam_name, details in cameras.items():
|
||||
details['queue'].put('get')
|
||||
|
||||
if os.path.exists('images/'):
|
||||
shutil.rmtree('images/')
|
||||
|
||||
os.makedirs('images/')
|
||||
|
||||
|
||||
def create_score_message(details, reshaped_image, timestamp):
|
||||
frames = list()
|
||||
|
||||
msg = list()
|
||||
if details.get('split_into_two', False):
|
||||
split_point = int(reshaped_image.shape[1] / 2)
|
||||
left_frame = resize_image(reshaped_image[:, :split_point, :],
|
||||
reshape_to_final=False)
|
||||
right_frame = resize_image(reshaped_image[:, split_point:, :],
|
||||
reshape_to_final=False)
|
||||
|
||||
left_frame = cv2.cvtColor(left_frame, cv2.COLOR_BGR2RGB)
|
||||
right_frame = cv2.cvtColor(right_frame, cv2.COLOR_BGR2RGB)
|
||||
msg.append({
|
||||
'camera_name': details['cam_name'] + '_left',
|
||||
'frame': left_frame,
|
||||
'image_timestamp': timestamp
|
||||
})
|
||||
msg.append({
|
||||
'camera_name': details['cam_name'] + '_right',
|
||||
'frame': right_frame,
|
||||
'image_timestamp': timestamp
|
||||
})
|
||||
|
||||
else:
|
||||
frame = resize_image(reshaped_image, reshape_to_final=False)
|
||||
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
|
||||
msg.append({
|
||||
'camera_name': details['cam_name'],
|
||||
'frame': frame,
|
||||
'image_timestamp': timestamp
|
||||
})
|
||||
|
||||
return msg
|
||||
|
||||
|
||||
for x in range(img_scoring_queue.qsize()):
|
||||
qu = img_scoring_queue.get()
|
||||
print(qu['camera_name'], qu['frame'].shape)
|
||||
cv2.imwrite(str(x) + '.jpg', qu['frame'])
|
||||
|
||||
iters = 0
|
||||
while True:
|
||||
for cam_name, obj in cameras.items():
|
||||
logger.info(f'Asking {cam_name} process to score')
|
||||
obj.score_frame_in_queue( save_to_disk = True)
|
||||
# obj.score_frame_in_queue( save_to_disk = iters % 10 == 0)
|
||||
obj.health_check()
|
||||
|
||||
iters+=1
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
# # %%
|
||||
# from utils import species_list
|
||||
# import pickle
|
||||
# import json
|
||||
# with open('crap.p','rb') as fw:
|
||||
# dump = pickle.load(fw)
|
||||
|
||||
|
||||
# camera_name = dump[1]['camera_name']
|
||||
# timestamp = dump[1]['image_timestamp']
|
||||
# ff = dump[0]
|
||||
|
||||
# has_scores = {idx:x for idx,x in enumerate(dump[0]) if len(x) > 0}
|
||||
|
||||
# score_dict = {}
|
||||
|
||||
# score_dict['timestamp'] = timestamp.timestamp()
|
||||
# score_dict['scores'] = list()
|
||||
|
||||
# for idx, sc in has_scores.items():
|
||||
# for r in sc:
|
||||
# score_dict['scores'].append({'idx': idx, 'species': species_list[idx], 'boxes':r[0:4].tolist(), 'score': r[4].tolist()})
|
||||
|
||||
|
||||
# def round_floats(obj, decimals=4):
|
||||
# if isinstance(obj, float):
|
||||
# return round(obj, decimals)
|
||||
# elif isinstance(obj, dict):
|
||||
# return {k: round_floats(v, decimals) for k, v in obj.items()}
|
||||
# elif isinstance(obj, list):
|
||||
# return [round_floats(item, decimals) for item in obj]
|
||||
# return obj
|
||||
# json_str = json.dumps(round_floats(score_dict))
|
||||
|
||||
|
||||
# with open('/home/thebears/source/infer/scores/' + camera_name,'a') as ff:
|
||||
# ff.write(json_str)
|
||||
# ff.write('\n')
|
||||
|
||||
# import matplotlib.pyplot as plt
|
||||
# plt.imshow(dump[1]['frame'])
|
||||
# plt.show()
|
||||
|
||||
# # %%
|
||||
# for cam_name, cls in cameras.items():
|
||||
# ret = cls.capture_image()
|
||||
# cv2.imwrite('images/' + cam_name + '.jpg', ret['image'])
|
||||
|
||||
|
||||
298
utils.py
298
utils.py
@@ -1,112 +1,220 @@
|
||||
import asyncio
|
||||
import numpy as np
|
||||
from common_code.settings import LogColorize
|
||||
import concurrent.futures
|
||||
|
||||
import string
|
||||
from random import choices
|
||||
from urllib import parse
|
||||
from io import BytesIO
|
||||
import requests
|
||||
import cv2
|
||||
import queue
|
||||
|
||||
import logging
|
||||
import struct
|
||||
import re
|
||||
import pickle
|
||||
import datetime as dt
|
||||
from functools import partial
|
||||
import cv2
|
||||
import time
|
||||
import numpy
|
||||
import multiprocessing
|
||||
import threading
|
||||
import numpy as np
|
||||
import ctypes
|
||||
import shutil
|
||||
import hashlib
|
||||
from hailo_platform import VDevice, HailoSchedulingAlgorithm, FormatType
|
||||
import pickle
|
||||
import json
|
||||
import redis
|
||||
import os
|
||||
pfm = LogColorize.score_obj_det_embed
|
||||
|
||||
with open('/home/thebears/source/infer/species_list','r') as sl:
|
||||
species_list = [x for x in sl.read().split('\n') if len(x) > 0]
|
||||
|
||||
r = redis.Redis('localhost',port=6379, db=14)
|
||||
|
||||
logger = logging.getLogger('live_inference')
|
||||
|
||||
def resize_image(img_in, reshape_to_final=False):
|
||||
if not isinstance(img_in, np.ndarray):
|
||||
img_in = np.asarray(img_in)
|
||||
max_l = 640
|
||||
|
||||
asp_rat = img_in.shape[0] / img_in.shape[1]
|
||||
if asp_rat < 1:
|
||||
output_size = [int(asp_rat * max_l), max_l]
|
||||
else:
|
||||
output_size = [max_l, int(max_l / asp_rat)]
|
||||
|
||||
im_arr_not_pad = cv2.resize(img_in, output_size[::-1])
|
||||
pad_amt = [max_l, max_l] - np.asarray(im_arr_not_pad.shape[0:2])
|
||||
left_pad, top_pad = (pad_amt / 2).astype(np.int64)
|
||||
right_pad, bottom_pad = pad_amt - [left_pad, top_pad]
|
||||
|
||||
im_pass = np.zeros(shape=(max_l, max_l, 3), dtype=np.uint8)
|
||||
im_pass[left_pad:(max_l - right_pad),
|
||||
top_pad:(max_l - bottom_pad)] = (im_arr_not_pad)
|
||||
data = im_pass
|
||||
if reshape_to_final:
|
||||
data = np.moveaxis(data, [2], [0])[None, :, :, :]
|
||||
|
||||
return data
|
||||
|
||||
|
||||
class StreamManager():
|
||||
def __init__(self, rtsp_url, resolution, camera_name = 'N/A'):
|
||||
self.cam_name = cam_name
|
||||
def model_scoring_callback(completion_info, bindings, data):
|
||||
if completion_info.exception:
|
||||
pass
|
||||
ff = bindings.output().get_buffer()
|
||||
camera_name = data['camera_name']
|
||||
timestamp = data['image_timestamp']
|
||||
hash_value = data['image_hash']
|
||||
dump_model_results_to_json( camera_name, timestamp, ff, hash_value)
|
||||
|
||||
|
||||
self.array_len = np.prod(resolution)
|
||||
self.cam_name = cam_name
|
||||
self.img_array = multiprocessing.Array(ctypes.c_uint8,
|
||||
int(array_len),
|
||||
lock=True)
|
||||
self.img_timestamp = multiprocessing.Value(ctypes.c_double)
|
||||
self.queue = multiprocessing.Queue()
|
||||
self.rtsp_url = format_ffmpeg_decode_url(rtsp_url).split(" ")
|
||||
self.process_func = partial(stream_wrapper,
|
||||
self.resolution,
|
||||
self.rtsp_url,
|
||||
camera_name=cam_name,
|
||||
queue=self.queue,
|
||||
img_array=self.img_array,
|
||||
img_timestamp=self.img_timestamp))
|
||||
|
||||
|
||||
def get_next_bitmap( byte_stream, shape):
|
||||
numel = np.prod(shape)
|
||||
string_find = b'BM' + struct.pack('<I',numel+54)
|
||||
fcf = [m.start() for m in re.finditer(re.escape(string_find), byte_stream)]
|
||||
for start_index in fcf:
|
||||
end_index = start_index + 60+numel
|
||||
frame_data_with_header = byte_stream[start_index:end_index]
|
||||
header = frame_data_with_header[:60]
|
||||
frame = frame_data_with_header[60:]
|
||||
nf = np.frombuffer(frame, dtype=np.uint8)
|
||||
if len(nf) != numel:
|
||||
return None, None
|
||||
pic = np.reshape(nf, shape)
|
||||
pic = pic[::-1,:,:]
|
||||
if len(nf) == numel:
|
||||
return pic
|
||||
|
||||
|
||||
def stream_wrapper( shape, cmd, camera_name = None, queue = None, img_array = None, img_timestamp = None):
|
||||
print('Starting wrapper')
|
||||
func = read_stream( shape, cmd, camera_name = camera_name, queue = queue, img_array = img_array, img_timestamp = img_timestamp)
|
||||
asyncio.run(func)
|
||||
def round_floats(obj, decimals=4):
|
||||
if isinstance(obj, float):
|
||||
return round(obj, decimals)
|
||||
elif isinstance(obj, dict):
|
||||
return {k: round_floats(v, decimals) for k, v in obj.items()}
|
||||
elif isinstance(obj, list):
|
||||
return [round_floats(item, decimals) for item in obj]
|
||||
return obj
|
||||
|
||||
|
||||
|
||||
async def read_stream( shape, cmd, camera_name = None, queue = None, img_array = None, img_timestamp = None):
|
||||
print('Starting stream')
|
||||
byte_buffer = b''
|
||||
bytes_read = (np.prod(shape)+60)*2
|
||||
print(cmd)
|
||||
process = await asyncio.create_subprocess_exec(*cmd,
|
||||
stdout=asyncio.subprocess.PIPE,
|
||||
stderr=asyncio.subprocess.PIPE
|
||||
)
|
||||
try:
|
||||
def dump_model_results_to_json(camera_name, timestamp, output_array, hash_value):
|
||||
has_scores = {idx:x for idx,x in enumerate(output_array) if len(x) > 0}
|
||||
score_dict = {}
|
||||
score_dict['timestamp'] = timestamp
|
||||
score_dict['scores'] = list()
|
||||
score_dict['image_hash'] = hash_value
|
||||
for idx, sc in has_scores.items():
|
||||
for r in sc:
|
||||
score_dict['scores'].append({'idx': idx, 'species': species_list[idx], 'boxes':r[0:4].tolist(), 'score': r[4].tolist()})
|
||||
|
||||
|
||||
json_str = json.dumps(round_floats(score_dict))
|
||||
|
||||
|
||||
with open('/home/thebears/source/infer/scores/' + camera_name,'a') as ff:
|
||||
ff.write(json_str)
|
||||
ff.write('\n')
|
||||
|
||||
|
||||
|
||||
|
||||
def run_model(img_scoring_queue):
|
||||
timeout_ms = 1000
|
||||
logger.info('Starting model scoring process')
|
||||
params = VDevice.create_params()
|
||||
params.scheduling_algorithm = HailoSchedulingAlgorithm.ROUND_ROBIN
|
||||
with VDevice(params) as vdevice:
|
||||
infer_model = vdevice.create_infer_model("yolov11l_inat.hef")
|
||||
logger.info('Loaded model')
|
||||
with infer_model.configure() as configured_infer_model:
|
||||
bindings = configured_infer_model.create_bindings()
|
||||
while True:
|
||||
try:
|
||||
# Use get with timeout for multiprocessing queue
|
||||
res = img_scoring_queue.get(timeout=1.0)
|
||||
r.set('model_inference_heartbeat',time.time())
|
||||
inp = res['frame']
|
||||
|
||||
res_send = {'camera_name': res['camera_name'], 'image_timestamp': res['image_timestamp'], 'image_hash':res['image_hash']}
|
||||
logger.info(f'Running inference for {res_send}')
|
||||
r.set('model_inference_started',str(res_send))
|
||||
|
||||
bindings.input().set_buffer(inp)
|
||||
output_array = np.zeros([infer_model.output().shape[0]]).astype(np.float32)
|
||||
bindings.output().set_buffer(output_array)
|
||||
|
||||
configured_infer_model.run([bindings], timeout_ms)
|
||||
|
||||
job = configured_infer_model.run_async(
|
||||
[bindings],
|
||||
partial(model_scoring_callback, bindings=bindings, data=res_send),
|
||||
)
|
||||
r.set('model_inference_finished',str(res_send))
|
||||
try:
|
||||
job.wait(timeout_ms)
|
||||
except Exception as e:
|
||||
logger.error(str(e))
|
||||
except:
|
||||
# Handle both queue.Empty and multiprocessing timeout
|
||||
continue
|
||||
|
||||
class SnapManager():
|
||||
def __init__(self, ip, url_api, username, password, camera_name, msg_queue=None, img_scoring_queue=None, split_into_two=False, **kwargs):
|
||||
self.ip = ip
|
||||
self.url_api = url_api
|
||||
self.username = username
|
||||
self.password = password
|
||||
self.camera_name = camera_name
|
||||
self.split_into_two = split_into_two
|
||||
n self.msg_queue = msg_queue
|
||||
self.img_scoring_queue = img_scoring_queue
|
||||
logger.info(f"{self.camera_name}: initialized")
|
||||
|
||||
def format_image_for_model(self, image, timestamp):
|
||||
msg = list()
|
||||
if self.split_into_two:
|
||||
split_point = int(image.shape[1] / 2)
|
||||
left_frame = resize_image(image[:, :split_point, :])
|
||||
right_frame = resize_image(image[:, split_point:, :])
|
||||
|
||||
msg.append({
|
||||
'camera_name': self.camera_name + '_left',
|
||||
'frame': left_frame,
|
||||
'image_timestamp': timestamp,
|
||||
'image_hash': hashlib.sha1(left_frame.tobytes()).hexdigest()
|
||||
})
|
||||
msg.append({
|
||||
'camera_name': self.camera_name + '_right',
|
||||
'frame': right_frame,
|
||||
'image_timestamp': timestamp,
|
||||
'image_hash': hashlib.sha1(right_frame.tobytes()).hexdigest()
|
||||
})
|
||||
else:
|
||||
frame = resize_image(image)
|
||||
msg.append({
|
||||
'camera_name': self.camera_name,
|
||||
'frame': frame,
|
||||
'image_timestamp': timestamp,
|
||||
'image_hash': hashlib.sha1(frame.tobytes()).hexdigest()
|
||||
})
|
||||
return msg
|
||||
|
||||
def capture_and_prepare(self):
|
||||
img = get_snap(self.username, self.password, self.url_api, self.camera_name)
|
||||
if img is not None:
|
||||
timestamp = time.time()
|
||||
return self.format_image_for_model(img, timestamp)
|
||||
return []
|
||||
|
||||
|
||||
def run_forever(self):
|
||||
while True:
|
||||
# Read up to 65KB at a time
|
||||
chunk = await process.stdout.read(bytes_read)
|
||||
byte_buffer += chunk
|
||||
diff = len(byte_buffer) - bytes_read
|
||||
# print(len(byte_buffer))
|
||||
if diff > 0:
|
||||
byte_buffer = byte_buffer[diff::]
|
||||
|
||||
|
||||
if not queue.empty():
|
||||
msg = queue.get_nowait()
|
||||
print('got message! ' + str( msg))
|
||||
try:
|
||||
msg = self.msg_queue.get(timeout=0.1)
|
||||
if msg == 'exit':
|
||||
return
|
||||
break
|
||||
if msg == 'save_image':
|
||||
|
||||
if msg == 'get':
|
||||
print('doing get!')
|
||||
frame = get_next_bitmap(byte_buffer, shape)
|
||||
with img_timestamp.get_lock(), img_array.get_lock():
|
||||
if frame is None:
|
||||
print(f"Read empty frame for {camera_name}")
|
||||
img_array[:] = 0
|
||||
img_timestamp.value = 0
|
||||
else:
|
||||
print(f"Read frame for {camera_name} at {dt.datetime.now()}")
|
||||
img_array[:] = frame.flatten()[:]
|
||||
img_timestamp.value = time.time()
|
||||
logger.info(f'Processing capture for {self.camera_name}')
|
||||
model_msgs = self.capture_and_prepare()
|
||||
for model_msg in model_msgs:
|
||||
# Use put_nowait for multiprocessing queue to avoid blocking
|
||||
try:
|
||||
self.img_scoring_queue.put_nowait(model_msg)
|
||||
except:
|
||||
# Queue full, skip this message
|
||||
logger.warning(f"Model queue full, dropping message from {self.camera_name}")
|
||||
except queue.Empty:
|
||||
pass
|
||||
|
||||
|
||||
except asyncio.CancelledError:
|
||||
print('Cancelled Error')
|
||||
process.kill()
|
||||
await process.wait()
|
||||
|
||||
|
||||
|
||||
|
||||
def format_gst_url(rtsp_url):
|
||||
gst_pipeline = f"rtspsrc location={rtsp_url} latency=50 ! rtph264depay ! h264parse ! avdec_h264 ! videoconvert ! appsink max-buffers=1 drop=true"
|
||||
return gst_pipeline
|
||||
|
||||
def format_ffmpeg_decode_url(rtsp_url):
|
||||
cmd = f"ffmpeg -rtsp_transport tcp -i {rtsp_url} -f image2pipe -vcodec bmp -an pipe:1"
|
||||
return cmd
|
||||
def start_snap_manager(**kwargs):
|
||||
obj = SnapManager(**kwargs)
|
||||
obj.run_forever()
|
||||
|
||||
Reference in New Issue
Block a user