blakeblackshear.frigate/frigate/util/image.py
2024-07-09 11:49:08 -05:00

784 lines
23 KiB
Python

"""Utilities for creating and manipulating image frames."""
import datetime
import logging
import subprocess as sp
from abc import ABC, abstractmethod
from multiprocessing import shared_memory
from string import printable
from typing import AnyStr, Optional
import cv2
import numpy as np
from unidecode import unidecode
logger = logging.getLogger(__name__)
def transliterate_to_latin(text: str) -> str:
"""
Transliterate a given text to Latin.
This function uses the unidecode library to transliterate the input text to Latin.
It is useful for converting texts with diacritics or non-Latin characters to a
Latin equivalent.
Args:
text (str): The text to be transliterated.
Returns:
str: The transliterated text.
Example:
>>> transliterate_to_latin('frégate')
'fregate'
"""
return unidecode(text)
def draw_timestamp(
frame,
timestamp,
timestamp_format,
font_effect=None,
font_thickness=2,
font_color=(255, 255, 255),
position="tl",
):
time_to_show = datetime.datetime.fromtimestamp(timestamp).strftime(timestamp_format)
# calculate a dynamic font size
size = cv2.getTextSize(
time_to_show,
cv2.FONT_HERSHEY_SIMPLEX,
fontScale=1.0,
thickness=font_thickness,
)
text_width = size[0][0]
desired_size = max(150, 0.33 * frame.shape[1])
font_scale = desired_size / text_width
# calculate the actual size with the dynamic scale
size = cv2.getTextSize(
time_to_show,
cv2.FONT_HERSHEY_SIMPLEX,
fontScale=font_scale,
thickness=font_thickness,
)
image_width = frame.shape[1]
image_height = frame.shape[0]
text_width = size[0][0]
text_height = size[0][1]
line_height = text_height + size[1]
if position == "tl":
text_offset_x = 0
text_offset_y = 0 if 0 < line_height else 0 - (line_height + 8)
elif position == "tr":
text_offset_x = image_width - text_width
text_offset_y = 0 if 0 < line_height else 0 - (line_height + 8)
elif position == "bl":
text_offset_x = 0
text_offset_y = image_height - (line_height + 8)
elif position == "br":
text_offset_x = image_width - text_width
text_offset_y = image_height - (line_height + 8)
if font_effect == "solid":
# make the coords of the box with a small padding of two pixels
timestamp_box_coords = np.array(
[
[text_offset_x, text_offset_y],
[text_offset_x + text_width, text_offset_y],
[text_offset_x + text_width, text_offset_y + line_height + 8],
[text_offset_x, text_offset_y + line_height + 8],
]
)
cv2.fillPoly(
frame,
[timestamp_box_coords],
# inverse color of text for background for max. contrast
(255 - font_color[0], 255 - font_color[1], 255 - font_color[2]),
)
elif font_effect == "shadow":
cv2.putText(
frame,
time_to_show,
(text_offset_x + 3, text_offset_y + line_height),
cv2.FONT_HERSHEY_SIMPLEX,
fontScale=font_scale,
color=(255 - font_color[0], 255 - font_color[1], 255 - font_color[2]),
thickness=font_thickness,
)
cv2.putText(
frame,
time_to_show,
(text_offset_x, text_offset_y + line_height - 3),
cv2.FONT_HERSHEY_SIMPLEX,
fontScale=font_scale,
color=font_color,
thickness=font_thickness,
)
def draw_box_with_label(
frame,
x_min,
y_min,
x_max,
y_max,
label,
info,
thickness=2,
color=None,
position="ul",
):
if color is None:
color = (0, 0, 255)
try:
display_text = transliterate_to_latin("{}: {}".format(label, info))
except Exception:
display_text = "{}: {}".format(label, info)
cv2.rectangle(frame, (x_min, y_min), (x_max, y_max), color, thickness)
font_scale = 0.5
font = cv2.FONT_HERSHEY_SIMPLEX
# get the width and height of the text box
size = cv2.getTextSize(display_text, font, fontScale=font_scale, thickness=2)
text_width = size[0][0]
text_height = size[0][1]
line_height = text_height + size[1]
# set the text start position
if position == "ul":
text_offset_x = x_min
text_offset_y = 0 if y_min < line_height else y_min - (line_height + 8)
elif position == "ur":
text_offset_x = x_max - (text_width + 8)
text_offset_y = 0 if y_min < line_height else y_min - (line_height + 8)
elif position == "bl":
text_offset_x = x_min
text_offset_y = y_max
elif position == "br":
text_offset_x = x_max - (text_width + 8)
text_offset_y = y_max
# make the coords of the box with a small padding of two pixels
textbox_coords = (
(text_offset_x, text_offset_y),
(text_offset_x + text_width + 2, text_offset_y + line_height),
)
cv2.rectangle(frame, textbox_coords[0], textbox_coords[1], color, cv2.FILLED)
cv2.putText(
frame,
display_text,
(text_offset_x, text_offset_y + line_height - 3),
font,
fontScale=font_scale,
color=(0, 0, 0),
thickness=2,
)
def is_label_printable(label) -> bool:
"""Check if label is printable."""
return not bool(set(label) - set(printable))
def calculate_region(frame_shape, xmin, ymin, xmax, ymax, model_size, multiplier=2):
# size is the longest edge and divisible by 4
size = int((max(xmax - xmin, ymax - ymin) * multiplier) // 4 * 4)
# dont go any smaller than the model_size
if size < model_size:
size = model_size
# x_offset is midpoint of bounding box minus half the size
x_offset = int((xmax - xmin) / 2.0 + xmin - size / 2.0)
# if outside the image
if x_offset < 0:
x_offset = 0
elif x_offset > (frame_shape[1] - size):
x_offset = max(0, (frame_shape[1] - size))
# y_offset is midpoint of bounding box minus half the size
y_offset = int((ymax - ymin) / 2.0 + ymin - size / 2.0)
# # if outside the image
if y_offset < 0:
y_offset = 0
elif y_offset > (frame_shape[0] - size):
y_offset = max(0, (frame_shape[0] - size))
return (x_offset, y_offset, x_offset + size, y_offset + size)
def calculate_16_9_crop(frame_shape, xmin, ymin, xmax, ymax, multiplier=1.25):
min_size = 200
# size is the longest edge and divisible by 4
x_size = int((xmax - xmin) * multiplier)
if x_size < min_size:
x_size = min_size
y_size = int((ymax - ymin) * multiplier)
if y_size < min_size:
y_size = min_size
if frame_shape[1] / frame_shape[0] > 16 / 9 and x_size / y_size > 4:
return None
# calculate 16x9 using height
aspect_y_size = int(9 / 16 * x_size)
# if 16:9 by height is too small
if aspect_y_size < y_size or aspect_y_size > frame_shape[0]:
x_size = int((16 / 9) * y_size) // 4 * 4
if x_size / y_size > 1.8:
return None
else:
y_size = aspect_y_size // 4 * 4
# x_offset is midpoint of bounding box minus half the size
x_offset = int((xmax - xmin) / 2.0 + xmin - x_size / 2.0)
# if outside the image
if x_offset < 0:
x_offset = 0
elif x_offset > (frame_shape[1] - x_size):
x_offset = max(0, (frame_shape[1] - x_size))
# y_offset is midpoint of bounding box minus half the size
y_offset = int((ymax - ymin) / 2.0 + ymin - y_size / 2.0)
# # if outside the image
if y_offset < 0:
y_offset = 0
elif y_offset > (frame_shape[0] - y_size):
y_offset = max(0, (frame_shape[0] - y_size))
return (x_offset, y_offset, x_offset + x_size, y_offset + y_size)
def get_yuv_crop(frame_shape, crop):
# crop should be (x1,y1,x2,y2)
frame_height = frame_shape[0] // 3 * 2
frame_width = frame_shape[1]
# compute the width/height of the uv channels
uv_width = frame_width // 2 # width of the uv channels
uv_height = frame_height // 4 # height of the uv channels
# compute the offset for upper left corner of the uv channels
uv_x_offset = crop[0] // 2 # x offset of the uv channels
uv_y_offset = crop[1] // 4 # y offset of the uv channels
# compute the width/height of the uv crops
uv_crop_width = (crop[2] - crop[0]) // 2 # width of the cropped uv channels
uv_crop_height = (crop[3] - crop[1]) // 4 # height of the cropped uv channels
# ensure crop dimensions are multiples of 2 and 4
y = (crop[0], crop[1], crop[0] + uv_crop_width * 2, crop[1] + uv_crop_height * 4)
u1 = (
0 + uv_x_offset,
frame_height + uv_y_offset,
0 + uv_x_offset + uv_crop_width,
frame_height + uv_y_offset + uv_crop_height,
)
u2 = (
uv_width + uv_x_offset,
frame_height + uv_y_offset,
uv_width + uv_x_offset + uv_crop_width,
frame_height + uv_y_offset + uv_crop_height,
)
v1 = (
0 + uv_x_offset,
frame_height + uv_height + uv_y_offset,
0 + uv_x_offset + uv_crop_width,
frame_height + uv_height + uv_y_offset + uv_crop_height,
)
v2 = (
uv_width + uv_x_offset,
frame_height + uv_height + uv_y_offset,
uv_width + uv_x_offset + uv_crop_width,
frame_height + uv_height + uv_y_offset + uv_crop_height,
)
return y, u1, u2, v1, v2
def yuv_crop_and_resize(frame, region, height=None):
# Crops and resizes a YUV frame while maintaining aspect ratio
# https://stackoverflow.com/a/57022634
height = frame.shape[0] // 3 * 2
width = frame.shape[1]
# get the crop box if the region extends beyond the frame
crop_x1 = max(0, region[0])
crop_y1 = max(0, region[1])
# ensure these are a multiple of 4
crop_x2 = min(width, region[2])
crop_y2 = min(height, region[3])
crop_box = (crop_x1, crop_y1, crop_x2, crop_y2)
y, u1, u2, v1, v2 = get_yuv_crop(frame.shape, crop_box)
# if the region starts outside the frame, indent the start point in the cropped frame
y_channel_x_offset = abs(min(0, region[0]))
y_channel_y_offset = abs(min(0, region[1]))
uv_channel_x_offset = y_channel_x_offset // 2
uv_channel_y_offset = y_channel_y_offset // 4
# create the yuv region frame
# make sure the size is a multiple of 4
# TODO: this should be based on the size after resize now
size = (region[3] - region[1]) // 4 * 4
yuv_cropped_frame = np.zeros((size + size // 2, size), np.uint8)
# fill in black
yuv_cropped_frame[:] = 128
yuv_cropped_frame[0:size, 0:size] = 16
# copy the y channel
yuv_cropped_frame[
y_channel_y_offset : y_channel_y_offset + y[3] - y[1],
y_channel_x_offset : y_channel_x_offset + y[2] - y[0],
] = frame[y[1] : y[3], y[0] : y[2]]
uv_crop_width = u1[2] - u1[0]
uv_crop_height = u1[3] - u1[1]
# copy u1
yuv_cropped_frame[
size + uv_channel_y_offset : size + uv_channel_y_offset + uv_crop_height,
0 + uv_channel_x_offset : 0 + uv_channel_x_offset + uv_crop_width,
] = frame[u1[1] : u1[3], u1[0] : u1[2]]
# copy u2
yuv_cropped_frame[
size + uv_channel_y_offset : size + uv_channel_y_offset + uv_crop_height,
size // 2 + uv_channel_x_offset : size // 2
+ uv_channel_x_offset
+ uv_crop_width,
] = frame[u2[1] : u2[3], u2[0] : u2[2]]
# copy v1
yuv_cropped_frame[
size + size // 4 + uv_channel_y_offset : size
+ size // 4
+ uv_channel_y_offset
+ uv_crop_height,
0 + uv_channel_x_offset : 0 + uv_channel_x_offset + uv_crop_width,
] = frame[v1[1] : v1[3], v1[0] : v1[2]]
# copy v2
yuv_cropped_frame[
size + size // 4 + uv_channel_y_offset : size
+ size // 4
+ uv_channel_y_offset
+ uv_crop_height,
size // 2 + uv_channel_x_offset : size // 2
+ uv_channel_x_offset
+ uv_crop_width,
] = frame[v2[1] : v2[3], v2[0] : v2[2]]
return yuv_cropped_frame
def yuv_to_3_channel_yuv(yuv_frame):
height = yuv_frame.shape[0] // 3 * 2
width = yuv_frame.shape[1]
# flatten the image into array
yuv_data = yuv_frame.ravel()
# create a numpy array to hold all the 3 channel yuv data
all_yuv_data = np.empty((height, width, 3), dtype=np.uint8)
y_count = height * width
uv_count = y_count // 4
# copy the y_channel
all_yuv_data[:, :, 0] = yuv_data[0:y_count].reshape((height, width))
# copy the u channel doubling each dimension
all_yuv_data[:, :, 1] = np.repeat(
np.reshape(
np.repeat(yuv_data[y_count : y_count + uv_count], repeats=2, axis=0),
(height // 2, width),
),
repeats=2,
axis=0,
)
# copy the v channel doubling each dimension
all_yuv_data[:, :, 2] = np.repeat(
np.reshape(
np.repeat(
yuv_data[y_count + uv_count : y_count + uv_count + uv_count],
repeats=2,
axis=0,
),
(height // 2, width),
),
repeats=2,
axis=0,
)
return all_yuv_data
def copy_yuv_to_position(
destination_frame,
destination_offset,
destination_shape,
source_frame=None,
source_channel_dim=None,
interpolation=cv2.INTER_LINEAR,
):
# get the coordinates of the channels for this position in the layout
y, u1, u2, v1, v2 = get_yuv_crop(
destination_frame.shape,
(
destination_offset[1],
destination_offset[0],
destination_offset[1] + destination_shape[1],
destination_offset[0] + destination_shape[0],
),
)
# clear y
destination_frame[
y[1] : y[3],
y[0] : y[2],
] = 16
# clear u1
destination_frame[u1[1] : u1[3], u1[0] : u1[2]] = 128
# clear u2
destination_frame[u2[1] : u2[3], u2[0] : u2[2]] = 128
# clear v1
destination_frame[v1[1] : v1[3], v1[0] : v1[2]] = 128
# clear v2
destination_frame[v2[1] : v2[3], v2[0] : v2[2]] = 128
if source_frame is not None:
# calculate the resized frame, maintaining the aspect ratio
source_aspect_ratio = source_frame.shape[1] / (source_frame.shape[0] // 3 * 2)
dest_aspect_ratio = destination_shape[1] / destination_shape[0]
if source_aspect_ratio <= dest_aspect_ratio:
y_resize_height = int(destination_shape[0] // 4 * 4)
y_resize_width = int((y_resize_height * source_aspect_ratio) // 4 * 4)
else:
y_resize_width = int(destination_shape[1] // 4 * 4)
y_resize_height = int((y_resize_width / source_aspect_ratio) // 4 * 4)
uv_resize_width = int(y_resize_width // 2)
uv_resize_height = int(y_resize_height // 4)
y_y_offset = int((destination_shape[0] - y_resize_height) / 4 // 4 * 4)
y_x_offset = int((destination_shape[1] - y_resize_width) / 2 // 4 * 4)
uv_y_offset = y_y_offset // 4
uv_x_offset = y_x_offset // 2
# resize/copy y channel
destination_frame[
y[1] + y_y_offset : y[1] + y_y_offset + y_resize_height,
y[0] + y_x_offset : y[0] + y_x_offset + y_resize_width,
] = cv2.resize(
source_frame[
source_channel_dim["y"][1] : source_channel_dim["y"][3],
source_channel_dim["y"][0] : source_channel_dim["y"][2],
],
dsize=(y_resize_width, y_resize_height),
interpolation=interpolation,
)
# resize/copy u1
destination_frame[
u1[1] + uv_y_offset : u1[1] + uv_y_offset + uv_resize_height,
u1[0] + uv_x_offset : u1[0] + uv_x_offset + uv_resize_width,
] = cv2.resize(
source_frame[
source_channel_dim["u1"][1] : source_channel_dim["u1"][3],
source_channel_dim["u1"][0] : source_channel_dim["u1"][2],
],
dsize=(uv_resize_width, uv_resize_height),
interpolation=interpolation,
)
# resize/copy u2
destination_frame[
u2[1] + uv_y_offset : u2[1] + uv_y_offset + uv_resize_height,
u2[0] + uv_x_offset : u2[0] + uv_x_offset + uv_resize_width,
] = cv2.resize(
source_frame[
source_channel_dim["u2"][1] : source_channel_dim["u2"][3],
source_channel_dim["u2"][0] : source_channel_dim["u2"][2],
],
dsize=(uv_resize_width, uv_resize_height),
interpolation=interpolation,
)
# resize/copy v1
destination_frame[
v1[1] + uv_y_offset : v1[1] + uv_y_offset + uv_resize_height,
v1[0] + uv_x_offset : v1[0] + uv_x_offset + uv_resize_width,
] = cv2.resize(
source_frame[
source_channel_dim["v1"][1] : source_channel_dim["v1"][3],
source_channel_dim["v1"][0] : source_channel_dim["v1"][2],
],
dsize=(uv_resize_width, uv_resize_height),
interpolation=interpolation,
)
# resize/copy v2
destination_frame[
v2[1] + uv_y_offset : v2[1] + uv_y_offset + uv_resize_height,
v2[0] + uv_x_offset : v2[0] + uv_x_offset + uv_resize_width,
] = cv2.resize(
source_frame[
source_channel_dim["v2"][1] : source_channel_dim["v2"][3],
source_channel_dim["v2"][0] : source_channel_dim["v2"][2],
],
dsize=(uv_resize_width, uv_resize_height),
interpolation=interpolation,
)
def yuv_region_2_yuv(frame, region):
try:
# TODO: does this copy the numpy array?
yuv_cropped_frame = yuv_crop_and_resize(frame, region)
return yuv_to_3_channel_yuv(yuv_cropped_frame)
except:
print(f"frame.shape: {frame.shape}")
print(f"region: {region}")
raise
def yuv_region_2_rgb(frame, region):
try:
# TODO: does this copy the numpy array?
yuv_cropped_frame = yuv_crop_and_resize(frame, region)
return cv2.cvtColor(yuv_cropped_frame, cv2.COLOR_YUV2RGB_I420)
except:
print(f"frame.shape: {frame.shape}")
print(f"region: {region}")
raise
def yuv_region_2_bgr(frame, region):
try:
yuv_cropped_frame = yuv_crop_and_resize(frame, region)
return cv2.cvtColor(yuv_cropped_frame, cv2.COLOR_YUV2BGR_I420)
except:
print(f"frame.shape: {frame.shape}")
print(f"region: {region}")
raise
def intersection(box_a, box_b) -> Optional[list[int]]:
"""Return intersection box or None if boxes do not intersect."""
if (
box_a[2] < box_b[0]
or box_a[0] > box_b[2]
or box_a[1] > box_b[3]
or box_a[3] < box_b[1]
):
return None
return (
max(box_a[0], box_b[0]),
max(box_a[1], box_b[1]),
min(box_a[2], box_b[2]),
min(box_a[3], box_b[3]),
)
def area(box):
return (box[2] - box[0] + 1) * (box[3] - box[1] + 1)
def intersection_over_union(box_a, box_b):
# determine the (x, y)-coordinates of the intersection rectangle
intersect = intersection(box_a, box_b)
if intersect is None:
return 0.0
# compute the area of intersection rectangle
inter_area = max(0, intersect[2] - intersect[0] + 1) * max(
0, intersect[3] - intersect[1] + 1
)
if inter_area == 0:
return 0.0
# compute the area of both the prediction and ground-truth
# rectangles
box_a_area = (box_a[2] - box_a[0] + 1) * (box_a[3] - box_a[1] + 1)
box_b_area = (box_b[2] - box_b[0] + 1) * (box_b[3] - box_b[1] + 1)
# compute the intersection over union by taking the intersection
# area and dividing it by the sum of prediction + ground-truth
# areas - the intersection area
iou = inter_area / float(box_a_area + box_b_area - inter_area)
# return the intersection over union value
return iou
def clipped(obj, frame_shape):
# if the object is within 5 pixels of the region border, and the region is not on the edge
# consider the object to be clipped
box = obj[2]
region = obj[5]
if (
(region[0] > 5 and box[0] - region[0] <= 5)
or (region[1] > 5 and box[1] - region[1] <= 5)
or (frame_shape[1] - region[2] > 5 and region[2] - box[2] <= 5)
or (frame_shape[0] - region[3] > 5 and region[3] - box[3] <= 5)
):
return True
else:
return False
class FrameManager(ABC):
@abstractmethod
def create(self, name, size) -> AnyStr:
pass
@abstractmethod
def get(self, name, timeout_ms=0):
pass
@abstractmethod
def close(self, name):
pass
@abstractmethod
def delete(self, name):
pass
class DictFrameManager(FrameManager):
def __init__(self):
self.frames = {}
def create(self, name, size) -> AnyStr:
mem = bytearray(size)
self.frames[name] = mem
return mem
def get(self, name, shape):
mem = self.frames[name]
return np.ndarray(shape, dtype=np.uint8, buffer=mem)
def close(self, name):
pass
def delete(self, name):
del self.frames[name]
class SharedMemoryFrameManager(FrameManager):
def __init__(self):
self.shm_store = {}
def create(self, name, size) -> AnyStr:
shm = shared_memory.SharedMemory(name=name, create=True, size=size)
self.shm_store[name] = shm
return shm.buf
def get(self, name, shape):
if name in self.shm_store:
shm = self.shm_store[name]
else:
shm = shared_memory.SharedMemory(name=name)
self.shm_store[name] = shm
return np.ndarray(shape, dtype=np.uint8, buffer=shm.buf)
def close(self, name):
if name in self.shm_store:
self.shm_store[name].close()
del self.shm_store[name]
def delete(self, name):
if name in self.shm_store:
self.shm_store[name].close()
self.shm_store[name].unlink()
del self.shm_store[name]
def create_mask(frame_shape, mask):
mask_img = np.zeros(frame_shape, np.uint8)
mask_img[:] = 255
if isinstance(mask, list):
for m in mask:
add_mask(m, mask_img)
elif isinstance(mask, str):
add_mask(mask, mask_img)
return mask_img
def add_mask(mask: str, mask_img: np.ndarray):
points = mask.split(",")
# masks and zones are saved as relative coordinates
# we know if any points are > 1 then it is using the
# old native resolution coordinates
if any(x > "1.0" for x in points):
raise Exception("add mask expects relative coordinates only")
contour = np.array(
[
[
int(float(points[i]) * mask_img.shape[1]),
int(float(points[i + 1]) * mask_img.shape[0]),
]
for i in range(0, len(points), 2)
]
)
cv2.fillPoly(mask_img, pts=[contour], color=(0))
def get_image_from_recording(
file_path: str, relative_frame_time: float
) -> Optional[any]:
"""retrieve a frame from given time in recording file."""
ffmpeg_cmd = [
"ffmpeg",
"-hide_banner",
"-loglevel",
"warning",
"-ss",
f"00:00:{relative_frame_time}",
"-i",
file_path,
"-frames:v",
"1",
"-c:v",
"png",
"-f",
"image2pipe",
"-",
]
process = sp.run(
ffmpeg_cmd,
capture_output=True,
)
if process.returncode == 0:
return process.stdout
else:
return None