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mytools.py
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mytools.py
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#
# mytools.py: toolkit for PySDK samples
#
# Copyright DeGirum Corporation 2022
# All rights reserved
#
import sys, os, time, string, cv2, PIL
from contextlib import contextmanager
def in_notebook():
"""Returns `True` if the module is running in IPython kernel,
`False` if in IPython shell or other Python shell.
"""
return "ipykernel" in sys.modules
# list of possible inference options
inference_option_list = {
1: {
"desc": "DeGirum Cloud Platform",
"url": "DEGIRUM_CLOUD_SERVER_ADDRESS",
"url_default": "dgcps://cs.degirum.com",
"token": "DEGIRUM_CLOUD_TOKEN",
},
2: {
"desc": "AI server connected via P2P VPN",
"url": "P2P_VPN_SERVER_ADDRESS",
"url_default": None,
"token": "",
},
3: {
"desc": "AI server in your local network",
"url": "LOCAL_NETWORK_SERVER_ADDRESS",
"url_default": None,
"token": "",
},
4: {
"desc": "AI server running on this machine",
"url": "127.0.0.1",
"url_default": None,
"token": "",
},
5: {
"desc": "DeGirum Orca installed on this machine",
"url": None,
"url_default": None,
"token": "GITHUB_TOKEN",
},
}
def _reload_env(custom_file="env.ini"):
"""Reload environment variables from file
custom_file - name of the custom env file to try first; if it is None or does not exist, `.env` file is loaded
"""
from pathlib import Path
import dotenv
if not Path(custom_file).exists():
custom_file = None
dotenv.load_dotenv(
dotenv_path=custom_file, override=True
) # load environment variables from file
def connect_model_zoo(inference_option=1):
"""Connect to model zoo according to given inference option
Returns model zoo accessor object
"""
import degirum as dg # import DeGirum PySDK
def _get_var(var, default_val=None):
if var is not None and var.isupper(): # treat `var` as env. var. name
ret = os.getenv(var)
if ret is None:
if default_val is None:
raise Exception(
f"Please define environment variable {var} in .env file located in your CWD"
)
else:
ret = default_val
else: # treat `var` literally
ret = var
return ret
_reload_env() # reload environment variables from file
my_cfg = inference_option_list[inference_option]
my_url = _get_var(my_cfg["url"], my_cfg["url_default"])
my_token = _get_var(my_cfg["token"])
zoo = dg.connect_model_zoo(my_url, my_token) # connect to the model zoo
print(
f"Inference option = '{my_cfg['desc']}'{'' if my_url is None else ' at ' + my_url}"
)
return zoo
def import_optional_package(pkg_name, is_long=False):
"""Import package with given name.
Returns the package object.
Raises error message if the package is not installed"""
import importlib
if is_long:
print(f"Loading '{pkg_name}' package, be patient...")
try:
ret = importlib.import_module(pkg_name)
if is_long:
print(f"...done; '{pkg_name}' version: {ret.__version__}")
return ret
except ModuleNotFoundError as e:
print(
f"\n*** Error loading '{pkg_name}' package: {e}. May be it is not installed?\n"
)
return None
def import_fiftyone():
"""Import 'fiftyone' package for dataset management
Returns the package.
Prints error message if the package is not installed"""
return import_optional_package("fiftyone", is_long=True)
@contextmanager
def open_video_stream(camera_id=None):
"""Open OpenCV video stream from camera with given identifier.
camera_id - 0-based index for local cameras
or IP camera URL in the format "rtsp://<user>:<password>@<ip or hostname>"
Returns context manager yielding video stream object and closing it on exit
"""
if camera_id is None:
_reload_env() # reload environment variables from file
camera_id = os.getenv("CAMERA_ID")
if camera_id.isnumeric():
camera_id = int(camera_id)
if camera_id is None:
raise Exception(
"No camera ID specified. Either define 'CAMERA_ID' environment variable or pass as a parameter"
)
stream = cv2.VideoCapture(camera_id)
if not stream.isOpened():
raise Exception(f"Error opening '{camera_id}' video stream")
else:
print(f"Successfully opened video stream '{camera_id}'")
try:
yield stream
finally:
stream.release()
def video_source(stream, report_error=True):
"""Generator function, which returns video frames captured from given video stream.
Useful to pass to model batch_predict().
stream - video stream context manager object returned by open_video_stream()
report_error - when True, error is raised on stream end
Yields video frame captured from given video stream
"""
while True:
ret, frame = stream.read()
if not ret:
if report_error:
raise Exception(
"Fail to capture camera frame. May be camera was opened by another notebook?"
)
else:
break
yield frame
@contextmanager
def open_video_writer(fname, w, h, fps=30):
"""Create, open, and return OpenCV video stream writer
fname - filename to save video
w, h - frame width/height
"""
writer = cv2.VideoWriter() # create stream writer
if not writer.open(
str(fname), cv2.VideoWriter_fourcc("m", "p", "g", "4"), fps, (int(w), int(h))
):
raise Exception(f"Fail to open '{str(fname)}'")
try:
yield writer
finally:
writer.release()
def video2jpegs(
video_file, jpeg_path, *, jpeg_prefix="frame_", preprocessor=None
) -> int:
"""Decode video file into a set of jpeg images
video_file - filename of a video file
jpeg_path - directory path to store decoded jpeg files
jpeg_prefix - common prefix for jpeg file names
preprocessor - optional image preprocessing function to be applied to each frame before saving into file
Returns number of decoded frames
"""
from pathlib import Path
jpeg_path = Path(jpeg_path)
if not jpeg_path.exists(): # create directory for annotated images
jpeg_path.mkdir()
with open_video_stream(video_file) as stream: # open video stream form file
nframes = int(stream.get(cv2.CAP_PROP_FRAME_COUNT))
progress = Progress(nframes)
# decode video stream into files resized to model input size
fi = 0
for img in video_source(stream, report_error=False):
if preprocessor is not None:
img = preprocessor(img)
fname = str(jpeg_path / f"{jpeg_prefix}{fi:05d}.jpg")
cv2.imwrite(fname, img)
progress.step()
fi += 1
return fi
@contextmanager
def open_audio_stream(sampling_rate_hz, buffer_size):
"""Open PyAudio audio stream
sampling_rate_hz - desired sample rate in Hz
buffer_size - read buffer size
Returns context manager yielding audio stream object and closing it on exit
"""
import numpy as np, queue
pyaudio = import_optional_package("pyaudio")
audio = pyaudio.PyAudio()
result_queue = queue.Queue()
def callback(
in_data, # recorded data if input=True; else None
frame_count, # number of frames
time_info, # dictionary
status_flags,
): # PaCallbackFlags
result_queue.put(in_data)
return (None, pyaudio.paContinue)
stream = audio.open(
format=pyaudio.paInt16,
channels=1,
rate=int(sampling_rate_hz),
input=True,
frames_per_buffer=int(buffer_size),
stream_callback=callback,
)
stream.result_queue = result_queue
try:
yield stream
finally:
stream.stop_stream() # stop audio streaming
stream.close() # close audio stream
audio.terminate() # terminate audio library
def audio_source(stream, check_abort, non_blocking=False):
"""Generator function, which returns audio frames captured from given audio stream.
Useful to pass to model batch_predict().
stream - audio stream context manager object returned by open_audio_stream()
check_abort - check-for-abort function or lambda; stream will be terminated when it returns True
non_blocking - True for non-blocking mode (immediately yields None if a block is not captured yet)
False for blocking mode (waits for the end of the block capture and always yields captured block)
Yields audio waveform captured from given audio stream
"""
import numpy as np, queue
while not check_abort():
if non_blocking:
try:
block = stream.result_queue.get_nowait()
except queue.Empty:
block = None
else:
block = stream.result_queue.get()
yield None if block is None else np.frombuffer(block, dtype=np.int16)
def audio_overlapped_source(stream, check_abort, non_blocking=False):
"""Generator function, which returns audio frames captured from given audio stream with half-length overlap.
Useful to pass to model batch_predict().
stream - audio stream context manager object returned by open_audio_stream()
check_abort - check-for-abort function or lambda; stream will be terminated when it returns True
non_blocking - True for non-blocking mode (immediately yields None if a block is not captured yet)
False for blocking mode (waits for the end of the block capture and always yields captured block)
Yields audio waveform captured from given audio stream with half-length overlap.
"""
import numpy as np, queue
chunk_length = stream._frames_per_buffer
data = np.zeros(2 * chunk_length, dtype=np.int16)
while not check_abort():
if non_blocking:
try:
block = stream.result_queue.get_nowait()
except queue.Empty:
block = None
else:
block = stream.result_queue.get()
if block is None:
yield None
else:
data[:chunk_length] = data[chunk_length:]
data[chunk_length:] = np.frombuffer(block, dtype=np.int16)
yield data
class FPSMeter:
"""Simple FPS meter class"""
def __init__(self, avg_len=100):
"""Constructor
avg_len - number of samples to average
"""
self._avg_len = avg_len
self.reset()
def reset(self):
"""Reset accumulators"""
self._timestamp_ns = -1
self._duration_ns = -1
self._count = 0
def record(self):
"""Record timestamp and update average duration.
Returns current average FPS"""
t = time.time_ns()
if self._timestamp_ns > 0:
cur_dur_ns = t - self._timestamp_ns
self._count = min(self._count + 1, self._avg_len)
self._duration_ns = (
self._duration_ns * (self._count - 1) + cur_dur_ns
) / self._count
self._timestamp_ns = t
return self.fps()
def fps(self):
"""Return current average FPS"""
return 1e9 / self._duration_ns if self._duration_ns > 0 else 0
class Display:
"""Class to handle OpenCV image display"""
def __init__(self, capt="<image>", show_fps=True, show_embedded=False):
"""Constructor
show_fps - True to show FPS
capt - window title
"""
self._fps = FPSMeter() if show_fps else None
self._capt = capt
self._need_destroy = False
self._show_embedded = show_embedded
self._no_gui = not Display._check_gui()
def __enter__(self):
return self
def __exit__(self, exc_type, exc_val, exc_tb):
if self._need_destroy:
cv2.destroyWindow(self._capt) # close OpenCV window
return exc_type is KeyboardInterrupt # ignore KeyboardInterrupt errors
def crop(img, bbox):
"""Crop and return OpenCV image to given bbox"""
return img[int(bbox[1]) : int(bbox[3]), int(bbox[0]) : int(bbox[2])]
def put_text(
img,
text,
position,
text_color,
back_color=None,
font=cv2.FONT_HERSHEY_COMPLEX_SMALL,
):
"""Draw given text on given OpenCV image at given point with given color
img - numpy array with image
text - text to draw
position - text top left coordinate tuple (x,y)
text_color - text color (BGR)
back_color - background color (BGR) or None for transparent
font = font to use
"""
text_size = cv2.getTextSize(text, font, 1, 1)
text_w = text_size[0][0]
text_h = text_size[0][1] + text_size[1]
margin = int(text_h / 4)
bl_corner = (position[0], position[1] + text_h)
if back_color is not None:
tr_corner = (
bl_corner[0] + text_w + 2 * margin,
bl_corner[1] - text_h - 2 * margin,
)
cv2.rectangle(img, bl_corner, tr_corner, back_color, cv2.FILLED)
cv2.putText(
img,
text,
(bl_corner[0] + margin, bl_corner[1] - margin),
font,
1,
text_color,
)
def _check_gui():
"""Check if graphical display is supported
Returns False if not supported
"""
import os, platform
if platform.system() == "Linux":
return os.environ.get("DISPLAY") is not None
return True
def _show_fps(img, fps):
"""Helper method to display FPS"""
Display.put_text(img, f"{fps:5.1f} FPS", (1, 1), (0, 0, 0), (255, 255, 255))
def show(self, img):
"""Show OpenCV image
img - numpy array with valid OpenCV image
"""
if self._fps:
fps = self._fps.record()
if fps > 0:
Display._show_fps(img, fps)
if self._show_embedded or self._no_gui:
if in_notebook():
import IPython.display
IPython.display.display(PIL.Image.fromarray(img[..., ::-1]), clear=True)
else:
cv2.imshow(self._capt, img)
self._need_destroy = True
key = cv2.waitKey(1) & 0xFF
if key == ord("x") or key == ord("q"):
if self._fps:
self._fps.reset()
raise KeyboardInterrupt
class Timer:
"""Simple timer class"""
def __init__(self):
"""Constructor. Records start time."""
self._start_time = time.time_ns()
def __call__(self):
"""Call method.
Returns time elapsed (in seconds, since object construction)."""
return (time.time_ns() - self._start_time) * 1e-9
class Progress:
"""Simple progress indicator"""
def __init__(self, last_step=None, *, start_step=0, bar_len=15, speed_units="FPS"):
"""Constructor
last_step - last step
start_step - starting step
bar_len - progress bar length in symbols
"""
self._display_id = None
self._len = bar_len
self._last_step = last_step
self._start_step = start_step
self._time_to_refresh = lambda: time.time() - self._last_update_time > 0.5
self._speed_units = speed_units
self.reset()
def reset(self):
self._start_time = time.time()
self._step = self._start_step
self._percent = 0.0
self._last_updated_percent = self._percent
self._last_update_time = 0
self._tip_phase = 0
self._update()
def step(self, steps=1):
"""Update progress by given number of steps
steps - number of steps to advance
"""
assert (
self._last_step is not None
), "Progress indicator: to do stepping last step must be assigned on construction"
self._step += steps
self._percent = (
100 * (self._step - self._start_step) / (self._last_step - self._start_step)
)
if (
self._percent - self._last_updated_percent >= 100 / self._len
or self._percent >= 100
or self._time_to_refresh()
):
self._update()
@property
def step_range(self):
"""Get start-end step range (if defined)"""
if self._last_step is not None:
return (self._start_step, self._last_step)
else:
return None
@property
def percent(self):
return self._percent
@percent.setter
def percent(self, value):
v = float(value)
delta = abs(self._last_updated_percent - v)
self._percent = v
if self._last_step is not None:
self._step = round(
0.01 * self._percent * (self._last_step - self._start_step)
+ self._start_step
)
if delta >= 100 / self._len or self._time_to_refresh():
self._update()
def _update(self):
"""Update progress bar"""
self._last_updated_percent = self._percent
bars = int(self._percent / 100 * self._len)
elapsed_s = time.time() - self._start_time
tips = "−\\/"
tip = tips[self._tip_phase] if bars < self._len else ""
self._tip_phase = (self._tip_phase + 1) % len(tips)
prog_str = f"{round(self._percent):4d}% |{'█' * bars}{tip}{'-' * (self._len - bars - 1)}|"
if self._last_step is not None:
prog_str += f" {self._step}/{self._last_step}"
prog_str += f" [{elapsed_s:.1f}s elapsed"
if self._percent > 0 and self._percent <= 100:
remaining_est_s = elapsed_s * (100 - self._percent) / self._percent
prog_str += f", {remaining_est_s:.1f}s remaining"
if self._last_step is not None and elapsed_s > 0:
prog_str += f", {(self._step - self._start_step) / elapsed_s:.1f} {self._speed_units}]"
else:
prog_str += "]"
class printer(str):
def __repr__(self):
return self
prog_str = printer(prog_str)
if in_notebook():
import IPython.display
if self._display_id is None:
self._display_id = "dg_progress_" + str(time.time_ns())
IPython.display.display(prog_str, display_id=self._display_id)
else:
IPython.display.update_display(prog_str, display_id=self._display_id)
else:
print(prog_str, end="\r")
self._last_update_time = time.time()
def area(box):
"""
Computes bbox(es) area: is vectorized.
Parameters
----------
box : np.array
Box(es) in format (x0, y0, x1, y1)
Returns
-------
np.array
area(s)
"""
return (box[..., 2] - box[..., 0]) * (box[..., 3] - box[..., 1])
def intersection(boxA, boxB):
"""
Compute area of intersection of two boxes
Parameters
----------
boxA : np.array
First boxes
boxB : np.array
Second box
Returns
-------
float64
Area of intersection
"""
xA = max(boxA[..., 0], boxB[..., 0])
xB = min(boxA[..., 2], boxB[..., 2])
dx = xB - xA
if dx <= 0:
return 0.0
yA = max(boxA[..., 1], boxB[..., 1])
yB = min(boxA[..., 3], boxB[..., 3])
dy = yB - yA
if dy <= 0.0:
return 0.0
# compute the area of intersection rectangle
return dx * dy