diff --git a/.github/workflows/dockerbuild.yml b/.github/workflows/dockerbuild.yml
index a5fea70..bf17efd 100644
--- a/.github/workflows/dockerbuild.yml
+++ b/.github/workflows/dockerbuild.yml
@@ -39,8 +39,9 @@ jobs:
- name: Build Images
env:
# Every folder in the repo that has a Dockerfile within it, comma separated
- DOCKERFOLDERS: "edge-detect/ai,tracker,piaware,pan-tilt-pi,notebook-server,egi,axis-ptz"
+ DOCKERFOLDERS: "tracker,piaware,pan-tilt-pi,notebook-server,egi,axis-ptz"
PROJECT_NAME: "skyscan"
+ REPO_NAME: "${{ secrets.DOCKER_NAMESPACE }}"
run: |
IFS=","
read -ra ARR <<< "$DOCKERFOLDERS"
@@ -52,7 +53,7 @@ jobs:
echo "Building $folder"
echo $SUBNAME
docker buildx build "$folder" --push \
- --tag iqtlabs/$PROJECT_NAME-$SUBNAME:latest \
- --tag iqtlabs/$PROJECT_NAME-$SUBNAME:${{ steps.get_tag.outputs.IMAGE_TAG }} \
+ --tag $REPO_NAME/$PROJECT_NAME-$SUBNAME:latest \
+ --tag $REPO_NAME/$PROJECT_NAME-$SUBNAME:${{ steps.get_tag.outputs.IMAGE_TAG }} \
--platform linux/arm64,linux/amd64
done
diff --git a/axis-ptz/camera.py b/axis-ptz/camera.py
index e28c5c9..cb7a982 100755
--- a/axis-ptz/camera.py
+++ b/axis-ptz/camera.py
@@ -80,16 +80,50 @@
camera_yaw = 0
currentPlane = None
-
+camera_altitude = None
camera_latitude = None
camera_longitude = None
-camera_altitude = None
+
camera_lead = None
include_age = strtobool(os.getenv("INCLUDE_AGE", "True"))
def calculate_bearing_correction(b):
return (b + cameraBearingCorrection) % 360
+def _format_file_save_filepath(file_extension: str = None):
+ """
+ A method for formatting the filepath of an image based off of the current state of global variables.
+ For use in JPEG, BMP, and JSON saving.
+ Args:
+ file_extension: The desired file extension with leading dot (.jpg, .bmp)
+ Returns:
+ A String object representing the filepath without the filetype extension.
+ """
+ captureDir = None
+
+ if args.flat_file_structure:
+ captureDir = "capture"
+ else:
+ captureDir = "capture{}".format(currentPlane["type"])
+ try:
+ os.makedirs(captureDir)
+ except OSError as e:
+ if e.errno != errno.EEXIST:
+ raise # This was not a "directory exist" error..
+ filepath = "{}/{}_{}_{}_{}_{}".format(
+ captureDir,
+ currentPlane["icao24"],
+ int(bearing),
+ int(elevation),
+ int(distance3d),
+ datetime.now().strftime("%Y-%m-%d-%H-%M-%S"),
+ )
+
+ if file_extension is not None:
+ filepath = filepath + str(file_extension)
+
+ return str(filepath)
+
# Copied from VaPix/Sensecam to customize the folder structure for saving pictures
def get_jpeg_request(): # 5.2.4.1
@@ -139,25 +173,7 @@ def get_jpeg_request(): # 5.2.4.1
disk_time = datetime.now()
if resp.status_code == 200:
- captureDir = None
-
- if args.flat_file_structure:
- captureDir = "capture/"
- else:
- captureDir = "capture/{}".format(currentPlane["type"])
- try:
- os.makedirs(captureDir)
- except OSError as e:
- if e.errno != errno.EEXIST:
- raise # This was not a "directory exist" error..
- filename = "{}/{}_{}_{}_{}_{}.jpg".format(
- captureDir,
- currentPlane["icao24"],
- int(bearing),
- int(elevation),
- int(distance3d),
- datetime.now().strftime("%Y-%m-%d-%H-%M-%S"),
- )
+ filename = _format_file_save_filepath(file_extension=".jpg")
# Original
with open(filename, "wb") as var:
@@ -224,20 +240,7 @@ def get_bmp_request(): # 5.2.4.1
)
if resp.status_code == 200:
- captureDir = "capture/{}".format(currentPlane["type"])
- try:
- os.makedirs(captureDir)
- except OSError as e:
- if e.errno != errno.EEXIST:
- raise # This was not a "directory exist" error..
- filename = "{}/{}_{}_{}_{}_{}.bmp".format(
- captureDir,
- currentPlane["icao24"],
- int(bearing),
- int(elevation),
- int(distance3d),
- datetime.now().strftime("%Y-%m-%d-%H-%M-%S"),
- )
+ filename = _format_file_save_filepath(file_extension=".bmp")
with open(filename, "wb") as var:
var.write(resp.content)
@@ -522,8 +525,40 @@ def calculateCameraPositionA():
)
cameraPan = calculate_bearing_correction(cameraPan)
+def get_json_request():
+ """
+ A method to save the metadata of the currently-tracking aircraft and the camera to a JSON file alongside BMP and
+ JPEG requests.
+ Args:
+ None
+ Returns:
+ A dictionary containing the contents os the JSON metadata file.
+ """
+ image_filepath = _format_file_save_filepath(file_extension=".jpg")
+
+ file_content_dictionary = {
+ "timestamp": datetime.now().strftime("%Y-%m-%d-%H-%M-%S"),
+ "imagefile": image_filepath,
+ "camera": {
+ "bearing": bearing,
+ "zoom": cameraZoom,
+ "pan": cameraPan,
+ "tilt": cameraTilt,
+ "lat": camera_latitude,
+ "long": camera_longitude,
+ "alt": camera_altitude
+ },
+ "aircraft": {
+ "lat": currentPlane["lat"],
+ "long": currentPlane["lon"],
+ "alt": currentPlane["altitude"]
+ }
+ }
+
+ return file_content_dictionary
-def moveCamera(ip, username, password):
+
+def moveCamera(ip, username, password, mqtt_client):
movePeriod = 100 # milliseconds
moveTimeout = datetime.now()
@@ -582,6 +617,13 @@ def moveCamera(ip, username, password):
if captureTimeout <= datetime.now():
time.sleep(cameraDelay)
get_jpeg_request()
+ capture_metadata = get_json_request()
+ mqtt_client.publish(
+ "skyscan/captures/data",
+ json.dumps(capture_metadata),
+ 0,
+ False
+ )
captureTimeout = captureTimeout + timedelta(
milliseconds=capturePeriod
)
@@ -774,6 +816,7 @@ def main():
global cameraConfig
global flight_topic
global object_topic
+ global logging_directory
global Active
parser = argparse.ArgumentParser(description="An MQTT based camera controller")
@@ -836,7 +879,19 @@ def main():
help="The zoom setting for the camera (0-9999)",
default=9999,
)
- parser.add_argument("-v", "--verbose", action="store_true", help="Verbose output")
+ parser.add_argument(
+ "-l",
+ "--log-directory",
+ type=str,
+ help="The directory for the camera to write capture logs to.",
+ default="/flash/processed/log"
+ )
+ parser.add_argument(
+ "-v",
+ "--verbose",
+ action="store_true",
+ help="Verbose output"
+ )
parser.add_argument(
"-f",
"--flat-file-structure",
@@ -897,15 +952,8 @@ def main():
camera_lead = args.camera_lead
# cameraConfig = vapix_config.CameraConfiguration(args.axis_ip, args.axis_username, args.axis_password)
- cameraMove = threading.Thread(
- target=moveCamera,
- args=[args.axis_ip, args.axis_username, args.axis_password],
- daemon=True,
- )
- cameraMove.start()
- # Sleep for a bit so we're not hammering the HAT with updates
- delay = 0.005
- time.sleep(delay)
+ logging_directory = args.log_directory
+
flight_topic = args.mqtt_flight_topic
object_topic = args.mqtt_object_topic
print(
@@ -933,6 +981,16 @@ def main():
False,
)
+ cameraMove = threading.Thread(
+ target=moveCamera,
+ args=[args.axis_ip, args.axis_username, args.axis_password, client],
+ daemon=True,
+ )
+ cameraMove.start()
+ # Sleep for a bit so we're not hammering the HAT with updates
+ delay = 0.005
+ time.sleep(delay)
+
#############################################
## Main Loop ##
#############################################
diff --git a/edge-detect/README.md b/edge-detect/README.md
deleted file mode 100644
index ebad753..0000000
--- a/edge-detect/README.md
+++ /dev/null
@@ -1,15 +0,0 @@
-# Aircraft Image Detection System #
-
-## Edge Detection Overview ##
-This edge detection system is designed to digest images captured by the SkyScan system and determine whether there is an aircraft fully in frame or not. A lack of aircraft in the image can occur if the aircraft the system was tracking when the image was taken was occluded (e.g., behind a building, tree, etc.) or too distant.
-
-## Installation ##
-The edge detection system uses docker compose and runs two services, one to move files out of the file structure generated by the SkyScan system into a central location (the `filemover` service) and the other to perform aircraft localization and detection (the `ai` service).
-
-In order to properly run the localization service, a weights file (commonly `weights.pt` or `localizer.pt`) must be placed in the `/weights` directory, which will then be mounted inside of the container.
-
-## Operation ##
-
-Both services can be launched using the command `docker compose up` if run in this `edge-detect` directory. The ai service will then begin taking raw images from `/flash/unprocessed` (placed there by the filemover service) or another volume specified in its place in the Dockerfile included in the `ai` folder. It then sorts them into `plane` and `noplane` folders also located, with the current configuration, under `/flash`.
-
-Additionally, the location of planes within each image is saved to JSON files under `/flash/log`.
\ No newline at end of file
diff --git a/edge-detect/ai/Dockerfile b/edge-detect/ai/Dockerfile
deleted file mode 100644
index c9a1c6a..0000000
--- a/edge-detect/ai/Dockerfile
+++ /dev/null
@@ -1,35 +0,0 @@
-FROM python:3.9.15-bullseye
-LABEL org.label-schema.schema-version 1.0
-LABEL org.label-schema.vendor IQT Labs
-LABEL org.label-schema.name SkyScan Edge AI
-
-# Dependencies
-RUN apt-get update && apt-get upgrade --assume-yes && apt-get install -y zip htop screen libgl1-mesa-glx
-RUN python --version
-
-# YOLOv7
-RUN git clone https://github.com/WongKinYiu/yolov7.git /yolov7
-WORKDIR /yolov7
-RUN sed -i '/thop/d' requirements.txt
-RUN sed -i '/torchvision/d' requirements.txt
-RUN sed -i '/torch/d' requirements.txt
-
-RUN pip install torch==1.8.1 -f https://download.pytorch.org/whl/torch/
-RUN pip install torchvision==0.9.1 -f https://download.pytorch.org/whl/torchvision/
-RUN pip install -r requirements.txt
-
-# SkyScan Edge AI
-ADD sort.py ./
-ADD datasets2.py utils/
-RUN mkdir /data
-
-# Run
-CMD ["bash", "-c", "python sort.py \
---weights ../data/weights/localizer.pt \
---agnostic-nms --nosave --conf 0.25 --img-size 640 --device cpu \
---source-dir ../data/tosort \
---plane-dir ../data/plane \
---noplane-dir ../data/noplane \
---log-dir ../data/log \
---save-json" ]
-
diff --git a/edge-detect/ai/LICENSE.md b/edge-detect/ai/LICENSE.md
deleted file mode 100644
index f288702..0000000
--- a/edge-detect/ai/LICENSE.md
+++ /dev/null
@@ -1,674 +0,0 @@
- GNU GENERAL PUBLIC LICENSE
- Version 3, 29 June 2007
-
- Copyright (C) 2007 Free Software Foundation, Inc.
- Everyone is permitted to copy and distribute verbatim copies
- of this license document, but changing it is not allowed.
-
- Preamble
-
- The GNU General Public License is a free, copyleft license for
-software and other kinds of works.
-
- The licenses for most software and other practical works are designed
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-share and change all versions of a program--to make sure it remains free
-software for all its users. We, the Free Software Foundation, use the
-GNU General Public License for most of our software; it applies also to
-any other work released this way by its authors. You can apply it to
-your programs, too.
-
- When we speak of free software, we are referring to freedom, not
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-have the freedom to distribute copies of free software (and charge for
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-you modify it: responsibilities to respect the freedom of others.
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diff --git a/edge-detect/ai/README.md b/edge-detect/ai/README.md
deleted file mode 100644
index 81f385d..0000000
--- a/edge-detect/ai/README.md
+++ /dev/null
@@ -1 +0,0 @@
-The files datasets2.py and sort.py are derived from https://github.com/WongKinYiu/yolov7 and are being released under the GNU General Public License v3.0. (The remainder of this repo is released under the Apache License 2.0.)
diff --git a/edge-detect/ai/datasets2.py b/edge-detect/ai/datasets2.py
deleted file mode 100644
index 911e76d..0000000
--- a/edge-detect/ai/datasets2.py
+++ /dev/null
@@ -1,1361 +0,0 @@
-# Dataset utils and dataloaders
-
-import glob
-import logging
-import math
-import os
-import random
-import shutil
-import time
-from itertools import repeat
-from multiprocessing.pool import ThreadPool
-from pathlib import Path
-from threading import Thread
-
-import cv2
-import numpy as np
-import torch
-import torch.nn.functional as F
-from PIL import Image, ExifTags
-from torch.utils.data import Dataset
-from tqdm import tqdm
-
-import pickle
-from copy import deepcopy
-#from pycocotools import mask as maskUtils
-from torchvision.utils import save_image
-from torchvision.ops import roi_pool, roi_align, ps_roi_pool, ps_roi_align
-
-from utils.general import check_requirements, xyxy2xywh, xywh2xyxy, xywhn2xyxy, xyn2xy, segment2box, segments2boxes, \
- resample_segments, clean_str
-from utils.torch_utils import torch_distributed_zero_first
-
-# Parameters
-help_url = 'https://github.com/ultralytics/yolov5/wiki/Train-Custom-Data'
-img_formats = ['bmp', 'jpg', 'jpeg', 'png', 'tif', 'tiff', 'dng', 'webp', 'mpo'] # acceptable image suffixes
-vid_formats = ['mov', 'avi', 'mp4', 'mpg', 'mpeg', 'm4v', 'wmv', 'mkv'] # acceptable video suffixes
-logger = logging.getLogger(__name__)
-
-# Get orientation exif tag
-for orientation in ExifTags.TAGS.keys():
- if ExifTags.TAGS[orientation] == 'Orientation':
- break
-
-
-def get_hash(files):
- # Returns a single hash value of a list of files
- return sum(os.path.getsize(f) for f in files if os.path.isfile(f))
-
-
-def exif_size(img):
- # Returns exif-corrected PIL size
- s = img.size # (width, height)
- try:
- rotation = dict(img._getexif().items())[orientation]
- if rotation == 6: # rotation 270
- s = (s[1], s[0])
- elif rotation == 8: # rotation 90
- s = (s[1], s[0])
- except:
- pass
-
- return s
-
-
-def create_dataloader(path, imgsz, batch_size, stride, opt, hyp=None, augment=False, cache=False, pad=0.0, rect=False,
- rank=-1, world_size=1, workers=8, image_weights=False, quad=False, prefix=''):
- # Make sure only the first process in DDP process the dataset first, and the following others can use the cache
- with torch_distributed_zero_first(rank):
- dataset = LoadImagesAndLabels(path, imgsz, batch_size,
- augment=augment, # augment images
- hyp=hyp, # augmentation hyperparameters
- rect=rect, # rectangular training
- cache_images=cache,
- single_cls=opt.single_cls,
- stride=int(stride),
- pad=pad,
- image_weights=image_weights,
- prefix=prefix)
-
- batch_size = min(batch_size, len(dataset))
- nw = min([os.cpu_count() // world_size, batch_size if batch_size > 1 else 0, workers]) # number of workers
- sampler = torch.utils.data.distributed.DistributedSampler(dataset) if rank != -1 else None
- loader = torch.utils.data.DataLoader if image_weights else InfiniteDataLoader
- # Use torch.utils.data.DataLoader() if dataset.properties will update during training else InfiniteDataLoader()
- dataloader = loader(dataset,
- batch_size=batch_size,
- num_workers=nw,
- sampler=sampler,
- pin_memory=True,
- collate_fn=LoadImagesAndLabels.collate_fn4 if quad else LoadImagesAndLabels.collate_fn)
- return dataloader, dataset
-
-
-class InfiniteDataLoader(torch.utils.data.dataloader.DataLoader):
- """ Dataloader that reuses workers
-
- Uses same syntax as vanilla DataLoader
- """
-
- def __init__(self, *args, **kwargs):
- super().__init__(*args, **kwargs)
- object.__setattr__(self, 'batch_sampler', _RepeatSampler(self.batch_sampler))
- self.iterator = super().__iter__()
-
- def __len__(self):
- return len(self.batch_sampler.sampler)
-
- def __iter__(self):
- for i in range(len(self)):
- yield next(self.iterator)
-
-
-class _RepeatSampler(object):
- """ Sampler that repeats forever
-
- Args:
- sampler (Sampler)
- """
-
- def __init__(self, sampler):
- self.sampler = sampler
-
- def __iter__(self):
- while True:
- yield from iter(self.sampler)
-
-
-class LoadImages: # for inference
- def __init__(self, path, img_size=640, stride=32):
- p = str(Path(path).absolute()) # os-agnostic absolute path
- if '*' in p:
- files = sorted(glob.glob(p, recursive=True)) # glob
- elif os.path.isdir(p):
- files = sorted(glob.glob(os.path.join(p, '*.*'))) # dir
- elif os.path.isfile(p):
- files = [p] # files
- else:
- raise Exception(f'ERROR: {p} does not exist')
-
- images = [x for x in files if x.split('.')[-1].lower() in img_formats]
- videos = [x for x in files if x.split('.')[-1].lower() in vid_formats]
- ni, nv = len(images), len(videos)
-
- self.img_size = img_size
- self.stride = stride
- self.files = images + videos
- self.nf = ni + nv # number of files
- self.video_flag = [False] * ni + [True] * nv
- self.mode = 'image'
- if any(videos):
- self.new_video(videos[0]) # new video
- else:
- self.cap = None
- assert self.nf > 0, f'No images or videos found in {p}. ' \
- f'Supported formats are:\nimages: {img_formats}\nvideos: {vid_formats}'
-
- def __iter__(self):
- self.count = 0
- return self
-
- def __next__(self):
- if self.count == self.nf:
- raise StopIteration
- path = self.files[self.count]
-
- if self.video_flag[self.count]:
- # Read video
- self.mode = 'video'
- ret_val, img0 = self.cap.read()
- if not ret_val:
- self.count += 1
- self.cap.release()
- if self.count == self.nf: # last video
- raise StopIteration
- else:
- path = self.files[self.count]
- self.new_video(path)
- ret_val, img0 = self.cap.read()
-
- self.frame += 1
- print(f'video {self.count + 1}/{self.nf} ({self.frame}/{self.nframes}) {path}: ', end='')
-
- else:
- # Read image
- self.count += 1
- img0 = cv2.imread(path) # BGR
- assert img0 is not None, 'Image Not Found ' + path
- #print(f'image {self.count}/{self.nf} {path}: ', end='')
-
- # Padded resize
- img = letterbox(img0, self.img_size, stride=self.stride)[0]
-
- # Convert
- img = img[:, :, ::-1].transpose(2, 0, 1) # BGR to RGB, to 3x416x416
- img = np.ascontiguousarray(img)
-
- return path, img, img0, self.cap
-
- def new_video(self, path):
- self.frame = 0
- self.cap = cv2.VideoCapture(path)
- self.nframes = int(self.cap.get(cv2.CAP_PROP_FRAME_COUNT))
-
- def __len__(self):
- return self.nf # number of files
-
-
-class LoadWebcam: # for inference
- def __init__(self, pipe='0', img_size=640, stride=32):
- self.img_size = img_size
- self.stride = stride
-
- if pipe.isnumeric():
- pipe = eval(pipe) # local camera
- # pipe = 'rtsp://192.168.1.64/1' # IP camera
- # pipe = 'rtsp://username:password@192.168.1.64/1' # IP camera with login
- # pipe = 'http://wmccpinetop.axiscam.net/mjpg/video.mjpg' # IP golf camera
-
- self.pipe = pipe
- self.cap = cv2.VideoCapture(pipe) # video capture object
- self.cap.set(cv2.CAP_PROP_BUFFERSIZE, 3) # set buffer size
-
- def __iter__(self):
- self.count = -1
- return self
-
- def __next__(self):
- self.count += 1
- if cv2.waitKey(1) == ord('q'): # q to quit
- self.cap.release()
- cv2.destroyAllWindows()
- raise StopIteration
-
- # Read frame
- if self.pipe == 0: # local camera
- ret_val, img0 = self.cap.read()
- img0 = cv2.flip(img0, 1) # flip left-right
- else: # IP camera
- n = 0
- while True:
- n += 1
- self.cap.grab()
- if n % 30 == 0: # skip frames
- ret_val, img0 = self.cap.retrieve()
- if ret_val:
- break
-
- # Print
- assert ret_val, f'Camera Error {self.pipe}'
- img_path = 'webcam.jpg'
- print(f'webcam {self.count}: ', end='')
-
- # Padded resize
- img = letterbox(img0, self.img_size, stride=self.stride)[0]
-
- # Convert
- img = img[:, :, ::-1].transpose(2, 0, 1) # BGR to RGB, to 3x416x416
- img = np.ascontiguousarray(img)
-
- return img_path, img, img0, None
-
- def __len__(self):
- return 0
-
-
-class LoadFromDir: # for inference
- def __init__(self, path, img_size=640, stride=32, log_dir="/data/log"):
- self.path = path
- self.img_size = img_size
- self.stride = stride
- self.mode = 'image'
- self.log_dir = log_dir
-
- def __iter__(self):
- self.count = 0
- return self
-
- def __next__(self):
- # Check for files in folder, waiting if necessary
- img0 = None
- img_path = None
- while True:
- if os.path.isdir(self.path):
- img_paths = glob.glob(os.path.join(self.path, '*'))
- if len(img_paths) > 0:
- img_path = min(img_paths, key=os.path.getctime)
- img0 = cv2.imread(img_path)
- if os.path.isdir(img_path) or img0 is None:
- print("Found non-file object ... moving to logs")
- base_folder = os.path.basename(img_path)
- shutil.move(img_path, os.path.join(self.log_dir, base_folder))
- else:
- break
- time.sleep(0.5)
-
- # Process image
- self.count += 1
- img = letterbox(img0, self.img_size, stride=self.stride)[0] # Size
- img = img[:, :, ::-1].transpose(2, 0, 1) # BGR to RGB, channel first
- img = np.ascontiguousarray(img)
- return img_path, img, img0, None
-
- def __len__(self):
- return 0
-
-
-class LoadStreams: # multiple IP or RTSP cameras
- def __init__(self, sources='streams.txt', img_size=640, stride=32):
- self.mode = 'stream'
- self.img_size = img_size
- self.stride = stride
-
- if os.path.isfile(sources):
- with open(sources, 'r') as f:
- sources = [x.strip() for x in f.read().strip().splitlines() if len(x.strip())]
- else:
- sources = [sources]
-
- n = len(sources)
- self.imgs = [None] * n
- self.sources = [clean_str(x) for x in sources] # clean source names for later
- for i, s in enumerate(sources):
- # Start the thread to read frames from the video stream
- print(f'{i + 1}/{n}: {s}... ', end='')
- url = eval(s) if s.isnumeric() else s
- if 'youtube.com/' in str(url) or 'youtu.be/' in str(url): # if source is YouTube video
- check_requirements(('pafy', 'youtube_dl'))
- import pafy
- url = pafy.new(url).getbest(preftype="mp4").url
- cap = cv2.VideoCapture(url)
- assert cap.isOpened(), f'Failed to open {s}'
- w = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
- h = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
- self.fps = cap.get(cv2.CAP_PROP_FPS) % 100
-
- _, self.imgs[i] = cap.read() # guarantee first frame
- thread = Thread(target=self.update, args=([i, cap]), daemon=True)
- print(f' success ({w}x{h} at {self.fps:.2f} FPS).')
- thread.start()
- print('') # newline
-
- # check for common shapes
- s = np.stack([letterbox(x, self.img_size, stride=self.stride)[0].shape for x in self.imgs], 0) # shapes
- self.rect = np.unique(s, axis=0).shape[0] == 1 # rect inference if all shapes equal
- if not self.rect:
- print('WARNING: Different stream shapes detected. For optimal performance supply similarly-shaped streams.')
-
- def update(self, index, cap):
- # Read next stream frame in a daemon thread
- n = 0
- while cap.isOpened():
- n += 1
- # _, self.imgs[index] = cap.read()
- cap.grab()
- if n == 4: # read every 4th frame
- success, im = cap.retrieve()
- self.imgs[index] = im if success else self.imgs[index] * 0
- n = 0
- time.sleep(1 / self.fps) # wait time
-
- def __iter__(self):
- self.count = -1
- return self
-
- def __next__(self):
- self.count += 1
- img0 = self.imgs.copy()
- if cv2.waitKey(1) == ord('q'): # q to quit
- cv2.destroyAllWindows()
- raise StopIteration
-
- # Letterbox
- img = [letterbox(x, self.img_size, auto=self.rect, stride=self.stride)[0] for x in img0]
-
- # Stack
- img = np.stack(img, 0)
-
- # Convert
- img = img[:, :, :, ::-1].transpose(0, 3, 1, 2) # BGR to RGB, to bsx3x416x416
- img = np.ascontiguousarray(img)
-
- return self.sources, img, img0, None
-
- def __len__(self):
- return 0 # 1E12 frames = 32 streams at 30 FPS for 30 years
-
-
-def img2label_paths(img_paths):
- # Define label paths as a function of image paths
- sa, sb = os.sep + 'images' + os.sep, os.sep + 'labels' + os.sep # /images/, /labels/ substrings
- return ['txt'.join(x.replace(sa, sb, 1).rsplit(x.split('.')[-1], 1)) for x in img_paths]
-
-
-class LoadImagesAndLabels(Dataset): # for training/testing
- def __init__(self, path, img_size=640, batch_size=16, augment=False, hyp=None, rect=False, image_weights=False,
- cache_images=False, single_cls=False, stride=32, pad=0.0, prefix=''):
- self.img_size = img_size
- self.augment = augment
- self.hyp = hyp
- self.image_weights = image_weights
- self.rect = False if image_weights else rect
- self.mosaic = self.augment and not self.rect # load 4 images at a time into a mosaic (only during training)
- self.mosaic_border = [-img_size // 2, -img_size // 2]
- self.stride = stride
- self.path = path
- #self.albumentations = Albumentations() if augment else None
-
- try:
- f = [] # image files
- for p in path if isinstance(path, list) else [path]:
- p = Path(p) # os-agnostic
- if p.is_dir(): # dir
- f += glob.glob(str(p / '**' / '*.*'), recursive=True)
- # f = list(p.rglob('**/*.*')) # pathlib
- elif p.is_file(): # file
- with open(p, 'r') as t:
- t = t.read().strip().splitlines()
- parent = str(p.parent) + os.sep
- f += [x.replace('./', parent) if x.startswith('./') else x for x in t] # local to global path
- # f += [p.parent / x.lstrip(os.sep) for x in t] # local to global path (pathlib)
- else:
- raise Exception(f'{prefix}{p} does not exist')
- self.img_files = sorted([x.replace('/', os.sep) for x in f if x.split('.')[-1].lower() in img_formats])
- # self.img_files = sorted([x for x in f if x.suffix[1:].lower() in img_formats]) # pathlib
- assert self.img_files, f'{prefix}No images found'
- except Exception as e:
- raise Exception(f'{prefix}Error loading data from {path}: {e}\nSee {help_url}')
-
- # Check cache
- self.label_files = img2label_paths(self.img_files) # labels
- cache_path = (p if p.is_file() else Path(self.label_files[0]).parent).with_suffix('.cache') # cached labels
- if cache_path.is_file():
- cache, exists = torch.load(cache_path), True # load
- #if cache['hash'] != get_hash(self.label_files + self.img_files) or 'version' not in cache: # changed
- # cache, exists = self.cache_labels(cache_path, prefix), False # re-cache
- else:
- cache, exists = self.cache_labels(cache_path, prefix), False # cache
-
- # Display cache
- nf, nm, ne, nc, n = cache.pop('results') # found, missing, empty, corrupted, total
- if exists:
- d = f"Scanning '{cache_path}' images and labels... {nf} found, {nm} missing, {ne} empty, {nc} corrupted"
- tqdm(None, desc=prefix + d, total=n, initial=n) # display cache results
- assert nf > 0 or not augment, f'{prefix}No labels in {cache_path}. Can not train without labels. See {help_url}'
-
- # Read cache
- cache.pop('hash') # remove hash
- cache.pop('version') # remove version
- labels, shapes, self.segments = zip(*cache.values())
- self.labels = list(labels)
- self.shapes = np.array(shapes, dtype=np.float64)
- self.img_files = list(cache.keys()) # update
- self.label_files = img2label_paths(cache.keys()) # update
- if single_cls:
- for x in self.labels:
- x[:, 0] = 0
-
- n = len(shapes) # number of images
- bi = np.floor(np.arange(n) / batch_size).astype(np.int) # batch index
- nb = bi[-1] + 1 # number of batches
- self.batch = bi # batch index of image
- self.n = n
- self.indices = range(n)
-
- # Rectangular Training
- if self.rect:
- # Sort by aspect ratio
- s = self.shapes # wh
- ar = s[:, 1] / s[:, 0] # aspect ratio
- irect = ar.argsort()
- self.img_files = [self.img_files[i] for i in irect]
- self.label_files = [self.label_files[i] for i in irect]
- self.labels = [self.labels[i] for i in irect]
- self.shapes = s[irect] # wh
- ar = ar[irect]
-
- # Set training image shapes
- shapes = [[1, 1]] * nb
- for i in range(nb):
- ari = ar[bi == i]
- mini, maxi = ari.min(), ari.max()
- if maxi < 1:
- shapes[i] = [maxi, 1]
- elif mini > 1:
- shapes[i] = [1, 1 / mini]
-
- self.batch_shapes = np.ceil(np.array(shapes) * img_size / stride + pad).astype(np.int) * stride
-
- # Cache images into memory for faster training (WARNING: large datasets may exceed system RAM)
- self.imgs = [None] * n
- if cache_images:
- if cache_images == 'disk':
- self.im_cache_dir = Path(Path(self.img_files[0]).parent.as_posix() + '_npy')
- self.img_npy = [self.im_cache_dir / Path(f).with_suffix('.npy').name for f in self.img_files]
- self.im_cache_dir.mkdir(parents=True, exist_ok=True)
- gb = 0 # Gigabytes of cached images
- self.img_hw0, self.img_hw = [None] * n, [None] * n
- results = ThreadPool(8).imap(lambda x: load_image(*x), zip(repeat(self), range(n)))
- pbar = tqdm(enumerate(results), total=n)
- for i, x in pbar:
- if cache_images == 'disk':
- if not self.img_npy[i].exists():
- np.save(self.img_npy[i].as_posix(), x[0])
- gb += self.img_npy[i].stat().st_size
- else:
- self.imgs[i], self.img_hw0[i], self.img_hw[i] = x
- gb += self.imgs[i].nbytes
- pbar.desc = f'{prefix}Caching images ({gb / 1E9:.1f}GB)'
- pbar.close()
-
- def cache_labels(self, path=Path('./labels.cache'), prefix=''):
- # Cache dataset labels, check images and read shapes
- x = {} # dict
- nm, nf, ne, nc = 0, 0, 0, 0 # number missing, found, empty, duplicate
- pbar = tqdm(zip(self.img_files, self.label_files), desc='Scanning images', total=len(self.img_files))
- for i, (im_file, lb_file) in enumerate(pbar):
- try:
- # verify images
- im = Image.open(im_file)
- im.verify() # PIL verify
- shape = exif_size(im) # image size
- segments = [] # instance segments
- assert (shape[0] > 9) & (shape[1] > 9), f'image size {shape} <10 pixels'
- assert im.format.lower() in img_formats, f'invalid image format {im.format}'
-
- # verify labels
- if os.path.isfile(lb_file):
- nf += 1 # label found
- with open(lb_file, 'r') as f:
- l = [x.split() for x in f.read().strip().splitlines()]
- if any([len(x) > 8 for x in l]): # is segment
- classes = np.array([x[0] for x in l], dtype=np.float32)
- segments = [np.array(x[1:], dtype=np.float32).reshape(-1, 2) for x in l] # (cls, xy1...)
- l = np.concatenate((classes.reshape(-1, 1), segments2boxes(segments)), 1) # (cls, xywh)
- l = np.array(l, dtype=np.float32)
- if len(l):
- assert l.shape[1] == 5, 'labels require 5 columns each'
- assert (l >= 0).all(), 'negative labels'
- assert (l[:, 1:] <= 1).all(), 'non-normalized or out of bounds coordinate labels'
- assert np.unique(l, axis=0).shape[0] == l.shape[0], 'duplicate labels'
- else:
- ne += 1 # label empty
- l = np.zeros((0, 5), dtype=np.float32)
- else:
- nm += 1 # label missing
- l = np.zeros((0, 5), dtype=np.float32)
- x[im_file] = [l, shape, segments]
- except Exception as e:
- nc += 1
- print(f'{prefix}WARNING: Ignoring corrupted image and/or label {im_file}: {e}')
-
- pbar.desc = f"{prefix}Scanning '{path.parent / path.stem}' images and labels... " \
- f"{nf} found, {nm} missing, {ne} empty, {nc} corrupted"
- pbar.close()
-
- if nf == 0:
- print(f'{prefix}WARNING: No labels found in {path}. See {help_url}')
-
- x['hash'] = get_hash(self.label_files + self.img_files)
- x['results'] = nf, nm, ne, nc, i + 1
- x['version'] = 0.1 # cache version
- torch.save(x, path) # save for next time
- logging.info(f'{prefix}New cache created: {path}')
- return x
-
- def __len__(self):
- return len(self.img_files)
-
- # def __iter__(self):
- # self.count = -1
- # print('ran dataset iter')
- # #self.shuffled_vector = np.random.permutation(self.nF) if self.augment else np.arange(self.nF)
- # return self
-
- def __getitem__(self, index):
- index = self.indices[index] # linear, shuffled, or image_weights
-
- hyp = self.hyp
- mosaic = self.mosaic and random.random() < hyp['mosaic']
- if mosaic:
- # Load mosaic
- if random.random() < 0.8:
- img, labels = load_mosaic(self, index)
- else:
- img, labels = load_mosaic9(self, index)
- shapes = None
-
- # MixUp https://arxiv.org/pdf/1710.09412.pdf
- if random.random() < hyp['mixup']:
- if random.random() < 0.8:
- img2, labels2 = load_mosaic(self, random.randint(0, len(self.labels) - 1))
- else:
- img2, labels2 = load_mosaic9(self, random.randint(0, len(self.labels) - 1))
- r = np.random.beta(8.0, 8.0) # mixup ratio, alpha=beta=8.0
- img = (img * r + img2 * (1 - r)).astype(np.uint8)
- labels = np.concatenate((labels, labels2), 0)
-
- else:
- # Load image
- img, (h0, w0), (h, w) = load_image(self, index)
-
- # Letterbox
- shape = self.batch_shapes[self.batch[index]] if self.rect else self.img_size # final letterboxed shape
- img, ratio, pad = letterbox(img, shape, auto=False, scaleup=self.augment)
- shapes = (h0, w0), ((h / h0, w / w0), pad) # for COCO mAP rescaling
-
- labels = self.labels[index].copy()
- if labels.size: # normalized xywh to pixel xyxy format
- labels[:, 1:] = xywhn2xyxy(labels[:, 1:], ratio[0] * w, ratio[1] * h, padw=pad[0], padh=pad[1])
-
- if self.augment:
- # Augment imagespace
- if not mosaic:
- img, labels = random_perspective(img, labels,
- degrees=hyp['degrees'],
- translate=hyp['translate'],
- scale=hyp['scale'],
- shear=hyp['shear'],
- perspective=hyp['perspective'])
-
-
- #img, labels = self.albumentations(img, labels)
-
- # Augment colorspace
- augment_hsv(img, hgain=hyp['hsv_h'], sgain=hyp['hsv_s'], vgain=hyp['hsv_v'])
-
- # Apply cutouts
- # if random.random() < 0.9:
- # labels = cutout(img, labels)
-
- if random.random() < hyp['paste_in']:
- sample_labels, sample_images, sample_masks = [], [], []
- while len(sample_labels) < 30:
- sample_labels_, sample_images_, sample_masks_ = load_samples(self, random.randint(0, len(self.labels) - 1))
- sample_labels += sample_labels_
- sample_images += sample_images_
- sample_masks += sample_masks_
- #print(len(sample_labels))
- if len(sample_labels) == 0:
- break
- labels = pastein(img, labels, sample_labels, sample_images, sample_masks)
-
- nL = len(labels) # number of labels
- if nL:
- labels[:, 1:5] = xyxy2xywh(labels[:, 1:5]) # convert xyxy to xywh
- labels[:, [2, 4]] /= img.shape[0] # normalized height 0-1
- labels[:, [1, 3]] /= img.shape[1] # normalized width 0-1
-
- if self.augment:
- # flip up-down
- if random.random() < hyp['flipud']:
- img = np.flipud(img)
- if nL:
- labels[:, 2] = 1 - labels[:, 2]
-
- # flip left-right
- if random.random() < hyp['fliplr']:
- img = np.fliplr(img)
- if nL:
- labels[:, 1] = 1 - labels[:, 1]
-
- labels_out = torch.zeros((nL, 6))
- if nL:
- labels_out[:, 1:] = torch.from_numpy(labels)
-
- # Convert
- img = img[:, :, ::-1].transpose(2, 0, 1) # BGR to RGB, to 3x416x416
- img = np.ascontiguousarray(img)
-
- return torch.from_numpy(img), labels_out, self.img_files[index], shapes
-
- @staticmethod
- def collate_fn(batch):
- img, label, path, shapes = zip(*batch) # transposed
- for i, l in enumerate(label):
- l[:, 0] = i # add target image index for build_targets()
- return torch.stack(img, 0), torch.cat(label, 0), path, shapes
-
- @staticmethod
- def collate_fn4(batch):
- img, label, path, shapes = zip(*batch) # transposed
- n = len(shapes) // 4
- img4, label4, path4, shapes4 = [], [], path[:n], shapes[:n]
-
- ho = torch.tensor([[0., 0, 0, 1, 0, 0]])
- wo = torch.tensor([[0., 0, 1, 0, 0, 0]])
- s = torch.tensor([[1, 1, .5, .5, .5, .5]]) # scale
- for i in range(n): # zidane torch.zeros(16,3,720,1280) # BCHW
- i *= 4
- if random.random() < 0.5:
- im = F.interpolate(img[i].unsqueeze(0).float(), scale_factor=2., mode='bilinear', align_corners=False)[
- 0].type(img[i].type())
- l = label[i]
- else:
- im = torch.cat((torch.cat((img[i], img[i + 1]), 1), torch.cat((img[i + 2], img[i + 3]), 1)), 2)
- l = torch.cat((label[i], label[i + 1] + ho, label[i + 2] + wo, label[i + 3] + ho + wo), 0) * s
- img4.append(im)
- label4.append(l)
-
- for i, l in enumerate(label4):
- l[:, 0] = i # add target image index for build_targets()
-
- return torch.stack(img4, 0), torch.cat(label4, 0), path4, shapes4
-
-
-# Ancillary functions --------------------------------------------------------------------------------------------------
-def load_image(self, index):
- # loads 1 image from dataset, returns img, original hw, resized hw
- img = self.imgs[index]
- if img is None: # not cached
- path = self.img_files[index]
- img = cv2.imread(path) # BGR
- assert img is not None, 'Image Not Found ' + path
- h0, w0 = img.shape[:2] # orig hw
- r = self.img_size / max(h0, w0) # resize image to img_size
- if r != 1: # always resize down, only resize up if training with augmentation
- interp = cv2.INTER_AREA if r < 1 and not self.augment else cv2.INTER_LINEAR
- img = cv2.resize(img, (int(w0 * r), int(h0 * r)), interpolation=interp)
- return img, (h0, w0), img.shape[:2] # img, hw_original, hw_resized
- else:
- return self.imgs[index], self.img_hw0[index], self.img_hw[index] # img, hw_original, hw_resized
-
-
-def augment_hsv(img, hgain=0.5, sgain=0.5, vgain=0.5):
- r = np.random.uniform(-1, 1, 3) * [hgain, sgain, vgain] + 1 # random gains
- hue, sat, val = cv2.split(cv2.cvtColor(img, cv2.COLOR_BGR2HSV))
- dtype = img.dtype # uint8
-
- x = np.arange(0, 256, dtype=np.int16)
- lut_hue = ((x * r[0]) % 180).astype(dtype)
- lut_sat = np.clip(x * r[1], 0, 255).astype(dtype)
- lut_val = np.clip(x * r[2], 0, 255).astype(dtype)
-
- img_hsv = cv2.merge((cv2.LUT(hue, lut_hue), cv2.LUT(sat, lut_sat), cv2.LUT(val, lut_val))).astype(dtype)
- cv2.cvtColor(img_hsv, cv2.COLOR_HSV2BGR, dst=img) # no return needed
-
-
-def hist_equalize(img, clahe=True, bgr=False):
- # Equalize histogram on BGR image 'img' with img.shape(n,m,3) and range 0-255
- yuv = cv2.cvtColor(img, cv2.COLOR_BGR2YUV if bgr else cv2.COLOR_RGB2YUV)
- if clahe:
- c = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8, 8))
- yuv[:, :, 0] = c.apply(yuv[:, :, 0])
- else:
- yuv[:, :, 0] = cv2.equalizeHist(yuv[:, :, 0]) # equalize Y channel histogram
- return cv2.cvtColor(yuv, cv2.COLOR_YUV2BGR if bgr else cv2.COLOR_YUV2RGB) # convert YUV image to RGB
-
-
-def load_mosaic(self, index):
- # loads images in a 4-mosaic
-
- labels4, segments4 = [], []
- s = self.img_size
- yc, xc = [int(random.uniform(-x, 2 * s + x)) for x in self.mosaic_border] # mosaic center x, y
- indices = [index] + random.choices(self.indices, k=3) # 3 additional image indices
- for i, index in enumerate(indices):
- # Load image
- img, _, (h, w) = load_image(self, index)
-
- # place img in img4
- if i == 0: # top left
- img4 = np.full((s * 2, s * 2, img.shape[2]), 114, dtype=np.uint8) # base image with 4 tiles
- x1a, y1a, x2a, y2a = max(xc - w, 0), max(yc - h, 0), xc, yc # xmin, ymin, xmax, ymax (large image)
- x1b, y1b, x2b, y2b = w - (x2a - x1a), h - (y2a - y1a), w, h # xmin, ymin, xmax, ymax (small image)
- elif i == 1: # top right
- x1a, y1a, x2a, y2a = xc, max(yc - h, 0), min(xc + w, s * 2), yc
- x1b, y1b, x2b, y2b = 0, h - (y2a - y1a), min(w, x2a - x1a), h
- elif i == 2: # bottom left
- x1a, y1a, x2a, y2a = max(xc - w, 0), yc, xc, min(s * 2, yc + h)
- x1b, y1b, x2b, y2b = w - (x2a - x1a), 0, w, min(y2a - y1a, h)
- elif i == 3: # bottom right
- x1a, y1a, x2a, y2a = xc, yc, min(xc + w, s * 2), min(s * 2, yc + h)
- x1b, y1b, x2b, y2b = 0, 0, min(w, x2a - x1a), min(y2a - y1a, h)
-
- img4[y1a:y2a, x1a:x2a] = img[y1b:y2b, x1b:x2b] # img4[ymin:ymax, xmin:xmax]
- padw = x1a - x1b
- padh = y1a - y1b
-
- # Labels
- labels, segments = self.labels[index].copy(), self.segments[index].copy()
- if labels.size:
- labels[:, 1:] = xywhn2xyxy(labels[:, 1:], w, h, padw, padh) # normalized xywh to pixel xyxy format
- segments = [xyn2xy(x, w, h, padw, padh) for x in segments]
- labels4.append(labels)
- segments4.extend(segments)
-
- # Concat/clip labels
- labels4 = np.concatenate(labels4, 0)
- for x in (labels4[:, 1:], *segments4):
- np.clip(x, 0, 2 * s, out=x) # clip when using random_perspective()
- # img4, labels4 = replicate(img4, labels4) # replicate
-
- # Augment
- #img4, labels4, segments4 = remove_background(img4, labels4, segments4)
- #sample_segments(img4, labels4, segments4, probability=self.hyp['copy_paste'])
- img4, labels4, segments4 = copy_paste(img4, labels4, segments4, probability=self.hyp['copy_paste'])
- img4, labels4 = random_perspective(img4, labels4, segments4,
- degrees=self.hyp['degrees'],
- translate=self.hyp['translate'],
- scale=self.hyp['scale'],
- shear=self.hyp['shear'],
- perspective=self.hyp['perspective'],
- border=self.mosaic_border) # border to remove
-
- return img4, labels4
-
-
-def load_mosaic9(self, index):
- # loads images in a 9-mosaic
-
- labels9, segments9 = [], []
- s = self.img_size
- indices = [index] + random.choices(self.indices, k=8) # 8 additional image indices
- for i, index in enumerate(indices):
- # Load image
- img, _, (h, w) = load_image(self, index)
-
- # place img in img9
- if i == 0: # center
- img9 = np.full((s * 3, s * 3, img.shape[2]), 114, dtype=np.uint8) # base image with 4 tiles
- h0, w0 = h, w
- c = s, s, s + w, s + h # xmin, ymin, xmax, ymax (base) coordinates
- elif i == 1: # top
- c = s, s - h, s + w, s
- elif i == 2: # top right
- c = s + wp, s - h, s + wp + w, s
- elif i == 3: # right
- c = s + w0, s, s + w0 + w, s + h
- elif i == 4: # bottom right
- c = s + w0, s + hp, s + w0 + w, s + hp + h
- elif i == 5: # bottom
- c = s + w0 - w, s + h0, s + w0, s + h0 + h
- elif i == 6: # bottom left
- c = s + w0 - wp - w, s + h0, s + w0 - wp, s + h0 + h
- elif i == 7: # left
- c = s - w, s + h0 - h, s, s + h0
- elif i == 8: # top left
- c = s - w, s + h0 - hp - h, s, s + h0 - hp
-
- padx, pady = c[:2]
- x1, y1, x2, y2 = [max(x, 0) for x in c] # allocate coords
-
- # Labels
- labels, segments = self.labels[index].copy(), self.segments[index].copy()
- if labels.size:
- labels[:, 1:] = xywhn2xyxy(labels[:, 1:], w, h, padx, pady) # normalized xywh to pixel xyxy format
- segments = [xyn2xy(x, w, h, padx, pady) for x in segments]
- labels9.append(labels)
- segments9.extend(segments)
-
- # Image
- img9[y1:y2, x1:x2] = img[y1 - pady:, x1 - padx:] # img9[ymin:ymax, xmin:xmax]
- hp, wp = h, w # height, width previous
-
- # Offset
- yc, xc = [int(random.uniform(0, s)) for _ in self.mosaic_border] # mosaic center x, y
- img9 = img9[yc:yc + 2 * s, xc:xc + 2 * s]
-
- # Concat/clip labels
- labels9 = np.concatenate(labels9, 0)
- labels9[:, [1, 3]] -= xc
- labels9[:, [2, 4]] -= yc
- c = np.array([xc, yc]) # centers
- segments9 = [x - c for x in segments9]
-
- for x in (labels9[:, 1:], *segments9):
- np.clip(x, 0, 2 * s, out=x) # clip when using random_perspective()
- # img9, labels9 = replicate(img9, labels9) # replicate
-
- # Augment
- #img9, labels9, segments9 = remove_background(img9, labels9, segments9)
- img9, labels9, segments9 = copy_paste(img9, labels9, segments9, probability=self.hyp['copy_paste'])
- img9, labels9 = random_perspective(img9, labels9, segments9,
- degrees=self.hyp['degrees'],
- translate=self.hyp['translate'],
- scale=self.hyp['scale'],
- shear=self.hyp['shear'],
- perspective=self.hyp['perspective'],
- border=self.mosaic_border) # border to remove
-
- return img9, labels9
-
-
-def load_samples(self, index):
- # loads images in a 4-mosaic
-
- labels4, segments4 = [], []
- s = self.img_size
- yc, xc = [int(random.uniform(-x, 2 * s + x)) for x in self.mosaic_border] # mosaic center x, y
- indices = [index] + random.choices(self.indices, k=3) # 3 additional image indices
- for i, index in enumerate(indices):
- # Load image
- img, _, (h, w) = load_image(self, index)
-
- # place img in img4
- if i == 0: # top left
- img4 = np.full((s * 2, s * 2, img.shape[2]), 114, dtype=np.uint8) # base image with 4 tiles
- x1a, y1a, x2a, y2a = max(xc - w, 0), max(yc - h, 0), xc, yc # xmin, ymin, xmax, ymax (large image)
- x1b, y1b, x2b, y2b = w - (x2a - x1a), h - (y2a - y1a), w, h # xmin, ymin, xmax, ymax (small image)
- elif i == 1: # top right
- x1a, y1a, x2a, y2a = xc, max(yc - h, 0), min(xc + w, s * 2), yc
- x1b, y1b, x2b, y2b = 0, h - (y2a - y1a), min(w, x2a - x1a), h
- elif i == 2: # bottom left
- x1a, y1a, x2a, y2a = max(xc - w, 0), yc, xc, min(s * 2, yc + h)
- x1b, y1b, x2b, y2b = w - (x2a - x1a), 0, w, min(y2a - y1a, h)
- elif i == 3: # bottom right
- x1a, y1a, x2a, y2a = xc, yc, min(xc + w, s * 2), min(s * 2, yc + h)
- x1b, y1b, x2b, y2b = 0, 0, min(w, x2a - x1a), min(y2a - y1a, h)
-
- img4[y1a:y2a, x1a:x2a] = img[y1b:y2b, x1b:x2b] # img4[ymin:ymax, xmin:xmax]
- padw = x1a - x1b
- padh = y1a - y1b
-
- # Labels
- labels, segments = self.labels[index].copy(), self.segments[index].copy()
- if labels.size:
- labels[:, 1:] = xywhn2xyxy(labels[:, 1:], w, h, padw, padh) # normalized xywh to pixel xyxy format
- segments = [xyn2xy(x, w, h, padw, padh) for x in segments]
- labels4.append(labels)
- segments4.extend(segments)
-
- # Concat/clip labels
- labels4 = np.concatenate(labels4, 0)
- for x in (labels4[:, 1:], *segments4):
- np.clip(x, 0, 2 * s, out=x) # clip when using random_perspective()
- # img4, labels4 = replicate(img4, labels4) # replicate
-
- # Augment
- #img4, labels4, segments4 = remove_background(img4, labels4, segments4)
- sample_labels, sample_images, sample_masks = sample_segments(img4, labels4, segments4, probability=0.5)
-
- return sample_labels, sample_images, sample_masks
-
-
-def copy_paste(img, labels, segments, probability=0.5):
- # Implement Copy-Paste augmentation https://arxiv.org/abs/2012.07177, labels as nx5 np.array(cls, xyxy)
- n = len(segments)
- if probability and n:
- h, w, c = img.shape # height, width, channels
- im_new = np.zeros(img.shape, np.uint8)
- for j in random.sample(range(n), k=round(probability * n)):
- l, s = labels[j], segments[j]
- box = w - l[3], l[2], w - l[1], l[4]
- ioa = bbox_ioa(box, labels[:, 1:5]) # intersection over area
- if (ioa < 0.30).all(): # allow 30% obscuration of existing labels
- labels = np.concatenate((labels, [[l[0], *box]]), 0)
- segments.append(np.concatenate((w - s[:, 0:1], s[:, 1:2]), 1))
- cv2.drawContours(im_new, [segments[j].astype(np.int32)], -1, (255, 255, 255), cv2.FILLED)
-
- result = cv2.bitwise_and(src1=img, src2=im_new)
- result = cv2.flip(result, 1) # augment segments (flip left-right)
- i = result > 0 # pixels to replace
- # i[:, :] = result.max(2).reshape(h, w, 1) # act over ch
- img[i] = result[i] # cv2.imwrite('debug.jpg', img) # debug
-
- return img, labels, segments
-
-
-def remove_background(img, labels, segments):
- # Implement Copy-Paste augmentation https://arxiv.org/abs/2012.07177, labels as nx5 np.array(cls, xyxy)
- n = len(segments)
- h, w, c = img.shape # height, width, channels
- im_new = np.zeros(img.shape, np.uint8)
- img_new = np.ones(img.shape, np.uint8) * 114
- for j in range(n):
- cv2.drawContours(im_new, [segments[j].astype(np.int32)], -1, (255, 255, 255), cv2.FILLED)
-
- result = cv2.bitwise_and(src1=img, src2=im_new)
-
- i = result > 0 # pixels to replace
- img_new[i] = result[i] # cv2.imwrite('debug.jpg', img) # debug
-
- return img_new, labels, segments
-
-
-def sample_segments(img, labels, segments, probability=0.5):
- # Implement Copy-Paste augmentation https://arxiv.org/abs/2012.07177, labels as nx5 np.array(cls, xyxy)
- n = len(segments)
- sample_labels = []
- sample_images = []
- sample_masks = []
- if probability and n:
- h, w, c = img.shape # height, width, channels
- for j in random.sample(range(n), k=round(probability * n)):
- l, s = labels[j], segments[j]
- box = l[1].astype(int).clip(0,w-1), l[2].astype(int).clip(0,h-1), l[3].astype(int).clip(0,w-1), l[4].astype(int).clip(0,h-1)
-
- #print(box)
- if (box[2] <= box[0]) or (box[3] <= box[1]):
- continue
-
- sample_labels.append(l[0])
-
- mask = np.zeros(img.shape, np.uint8)
-
- cv2.drawContours(mask, [segments[j].astype(np.int32)], -1, (255, 255, 255), cv2.FILLED)
- sample_masks.append(mask[box[1]:box[3],box[0]:box[2],:])
-
- result = cv2.bitwise_and(src1=img, src2=mask)
- i = result > 0 # pixels to replace
- mask[i] = result[i] # cv2.imwrite('debug.jpg', img) # debug
- #print(box)
- sample_images.append(mask[box[1]:box[3],box[0]:box[2],:])
-
- return sample_labels, sample_images, sample_masks
-
-
-def replicate(img, labels):
- # Replicate labels
- h, w = img.shape[:2]
- boxes = labels[:, 1:].astype(int)
- x1, y1, x2, y2 = boxes.T
- s = ((x2 - x1) + (y2 - y1)) / 2 # side length (pixels)
- for i in s.argsort()[:round(s.size * 0.5)]: # smallest indices
- x1b, y1b, x2b, y2b = boxes[i]
- bh, bw = y2b - y1b, x2b - x1b
- yc, xc = int(random.uniform(0, h - bh)), int(random.uniform(0, w - bw)) # offset x, y
- x1a, y1a, x2a, y2a = [xc, yc, xc + bw, yc + bh]
- img[y1a:y2a, x1a:x2a] = img[y1b:y2b, x1b:x2b] # img4[ymin:ymax, xmin:xmax]
- labels = np.append(labels, [[labels[i, 0], x1a, y1a, x2a, y2a]], axis=0)
-
- return img, labels
-
-
-def letterbox(img, new_shape=(640, 640), color=(114, 114, 114), auto=True, scaleFill=False, scaleup=True, stride=32):
- # Resize and pad image while meeting stride-multiple constraints
- shape = img.shape[:2] # current shape [height, width]
- if isinstance(new_shape, int):
- new_shape = (new_shape, new_shape)
-
- # Scale ratio (new / old)
- r = min(new_shape[0] / shape[0], new_shape[1] / shape[1])
- if not scaleup: # only scale down, do not scale up (for better test mAP)
- r = min(r, 1.0)
-
- # Compute padding
- ratio = r, r # width, height ratios
- new_unpad = int(round(shape[1] * r)), int(round(shape[0] * r))
- dw, dh = new_shape[1] - new_unpad[0], new_shape[0] - new_unpad[1] # wh padding
- if auto: # minimum rectangle
- dw, dh = np.mod(dw, stride), np.mod(dh, stride) # wh padding
- elif scaleFill: # stretch
- dw, dh = 0.0, 0.0
- new_unpad = (new_shape[1], new_shape[0])
- ratio = new_shape[1] / shape[1], new_shape[0] / shape[0] # width, height ratios
-
- dw /= 2 # divide padding into 2 sides
- dh /= 2
-
- if shape[::-1] != new_unpad: # resize
- img = cv2.resize(img, new_unpad, interpolation=cv2.INTER_LINEAR)
- top, bottom = int(round(dh - 0.1)), int(round(dh + 0.1))
- left, right = int(round(dw - 0.1)), int(round(dw + 0.1))
- img = cv2.copyMakeBorder(img, top, bottom, left, right, cv2.BORDER_CONSTANT, value=color) # add border
- return img, ratio, (dw, dh)
-
-
-def random_perspective(img, targets=(), segments=(), degrees=10, translate=.1, scale=.1, shear=10, perspective=0.0,
- border=(0, 0)):
- # torchvision.transforms.RandomAffine(degrees=(-10, 10), translate=(.1, .1), scale=(.9, 1.1), shear=(-10, 10))
- # targets = [cls, xyxy]
-
- height = img.shape[0] + border[0] * 2 # shape(h,w,c)
- width = img.shape[1] + border[1] * 2
-
- # Center
- C = np.eye(3)
- C[0, 2] = -img.shape[1] / 2 # x translation (pixels)
- C[1, 2] = -img.shape[0] / 2 # y translation (pixels)
-
- # Perspective
- P = np.eye(3)
- P[2, 0] = random.uniform(-perspective, perspective) # x perspective (about y)
- P[2, 1] = random.uniform(-perspective, perspective) # y perspective (about x)
-
- # Rotation and Scale
- R = np.eye(3)
- a = random.uniform(-degrees, degrees)
- # a += random.choice([-180, -90, 0, 90]) # add 90deg rotations to small rotations
- s = random.uniform(1 - scale, 1.1 + scale)
- # s = 2 ** random.uniform(-scale, scale)
- R[:2] = cv2.getRotationMatrix2D(angle=a, center=(0, 0), scale=s)
-
- # Shear
- S = np.eye(3)
- S[0, 1] = math.tan(random.uniform(-shear, shear) * math.pi / 180) # x shear (deg)
- S[1, 0] = math.tan(random.uniform(-shear, shear) * math.pi / 180) # y shear (deg)
-
- # Translation
- T = np.eye(3)
- T[0, 2] = random.uniform(0.5 - translate, 0.5 + translate) * width # x translation (pixels)
- T[1, 2] = random.uniform(0.5 - translate, 0.5 + translate) * height # y translation (pixels)
-
- # Combined rotation matrix
- M = T @ S @ R @ P @ C # order of operations (right to left) is IMPORTANT
- if (border[0] != 0) or (border[1] != 0) or (M != np.eye(3)).any(): # image changed
- if perspective:
- img = cv2.warpPerspective(img, M, dsize=(width, height), borderValue=(114, 114, 114))
- else: # affine
- img = cv2.warpAffine(img, M[:2], dsize=(width, height), borderValue=(114, 114, 114))
-
- # Visualize
- # import matplotlib.pyplot as plt
- # ax = plt.subplots(1, 2, figsize=(12, 6))[1].ravel()
- # ax[0].imshow(img[:, :, ::-1]) # base
- # ax[1].imshow(img2[:, :, ::-1]) # warped
-
- # Transform label coordinates
- n = len(targets)
- if n:
- use_segments = any(x.any() for x in segments)
- new = np.zeros((n, 4))
- if use_segments: # warp segments
- segments = resample_segments(segments) # upsample
- for i, segment in enumerate(segments):
- xy = np.ones((len(segment), 3))
- xy[:, :2] = segment
- xy = xy @ M.T # transform
- xy = xy[:, :2] / xy[:, 2:3] if perspective else xy[:, :2] # perspective rescale or affine
-
- # clip
- new[i] = segment2box(xy, width, height)
-
- else: # warp boxes
- xy = np.ones((n * 4, 3))
- xy[:, :2] = targets[:, [1, 2, 3, 4, 1, 4, 3, 2]].reshape(n * 4, 2) # x1y1, x2y2, x1y2, x2y1
- xy = xy @ M.T # transform
- xy = (xy[:, :2] / xy[:, 2:3] if perspective else xy[:, :2]).reshape(n, 8) # perspective rescale or affine
-
- # create new boxes
- x = xy[:, [0, 2, 4, 6]]
- y = xy[:, [1, 3, 5, 7]]
- new = np.concatenate((x.min(1), y.min(1), x.max(1), y.max(1))).reshape(4, n).T
-
- # clip
- new[:, [0, 2]] = new[:, [0, 2]].clip(0, width)
- new[:, [1, 3]] = new[:, [1, 3]].clip(0, height)
-
- # filter candidates
- i = box_candidates(box1=targets[:, 1:5].T * s, box2=new.T, area_thr=0.01 if use_segments else 0.10)
- targets = targets[i]
- targets[:, 1:5] = new[i]
-
- return img, targets
-
-
-def box_candidates(box1, box2, wh_thr=2, ar_thr=20, area_thr=0.1, eps=1e-16): # box1(4,n), box2(4,n)
- # Compute candidate boxes: box1 before augment, box2 after augment, wh_thr (pixels), aspect_ratio_thr, area_ratio
- w1, h1 = box1[2] - box1[0], box1[3] - box1[1]
- w2, h2 = box2[2] - box2[0], box2[3] - box2[1]
- ar = np.maximum(w2 / (h2 + eps), h2 / (w2 + eps)) # aspect ratio
- return (w2 > wh_thr) & (h2 > wh_thr) & (w2 * h2 / (w1 * h1 + eps) > area_thr) & (ar < ar_thr) # candidates
-
-
-def bbox_ioa(box1, box2):
- # Returns the intersection over box2 area given box1, box2. box1 is 4, box2 is nx4. boxes are x1y1x2y2
- box2 = box2.transpose()
-
- # Get the coordinates of bounding boxes
- b1_x1, b1_y1, b1_x2, b1_y2 = box1[0], box1[1], box1[2], box1[3]
- b2_x1, b2_y1, b2_x2, b2_y2 = box2[0], box2[1], box2[2], box2[3]
-
- # Intersection area
- inter_area = (np.minimum(b1_x2, b2_x2) - np.maximum(b1_x1, b2_x1)).clip(0) * \
- (np.minimum(b1_y2, b2_y2) - np.maximum(b1_y1, b2_y1)).clip(0)
-
- # box2 area
- box2_area = (b2_x2 - b2_x1) * (b2_y2 - b2_y1) + 1e-16
-
- # Intersection over box2 area
- return inter_area / box2_area
-
-
-def cutout(image, labels):
- # Applies image cutout augmentation https://arxiv.org/abs/1708.04552
- h, w = image.shape[:2]
-
- # create random masks
- scales = [0.5] * 1 + [0.25] * 2 + [0.125] * 4 + [0.0625] * 8 + [0.03125] * 16 # image size fraction
- for s in scales:
- mask_h = random.randint(1, int(h * s))
- mask_w = random.randint(1, int(w * s))
-
- # box
- xmin = max(0, random.randint(0, w) - mask_w // 2)
- ymin = max(0, random.randint(0, h) - mask_h // 2)
- xmax = min(w, xmin + mask_w)
- ymax = min(h, ymin + mask_h)
-
- # apply random color mask
- image[ymin:ymax, xmin:xmax] = [random.randint(64, 191) for _ in range(3)]
-
- # return unobscured labels
- if len(labels) and s > 0.03:
- box = np.array([xmin, ymin, xmax, ymax], dtype=np.float32)
- ioa = bbox_ioa(box, labels[:, 1:5]) # intersection over area
- labels = labels[ioa < 0.60] # remove >60% obscured labels
-
- return labels
-
-
-def pastein(image, labels, sample_labels, sample_images, sample_masks):
- # Applies image cutout augmentation https://arxiv.org/abs/1708.04552
- h, w = image.shape[:2]
-
- # create random masks
- scales = [0.75] * 2 + [0.5] * 4 + [0.25] * 4 + [0.125] * 4 + [0.0625] * 6 # image size fraction
- for s in scales:
- if random.random() < 0.2:
- continue
- mask_h = random.randint(1, int(h * s))
- mask_w = random.randint(1, int(w * s))
-
- # box
- xmin = max(0, random.randint(0, w) - mask_w // 2)
- ymin = max(0, random.randint(0, h) - mask_h // 2)
- xmax = min(w, xmin + mask_w)
- ymax = min(h, ymin + mask_h)
-
- box = np.array([xmin, ymin, xmax, ymax], dtype=np.float32)
- if len(labels):
- ioa = bbox_ioa(box, labels[:, 1:5]) # intersection over area
- else:
- ioa = np.zeros(1)
-
- if (ioa < 0.30).all() and len(sample_labels) and (xmax > xmin+20) and (ymax > ymin+20): # allow 30% obscuration of existing labels
- sel_ind = random.randint(0, len(sample_labels)-1)
- #print(len(sample_labels))
- #print(sel_ind)
- #print((xmax-xmin, ymax-ymin))
- #print(image[ymin:ymax, xmin:xmax].shape)
- #print([[sample_labels[sel_ind], *box]])
- #print(labels.shape)
- hs, ws, cs = sample_images[sel_ind].shape
- r_scale = min((ymax-ymin)/hs, (xmax-xmin)/ws)
- r_w = int(ws*r_scale)
- r_h = int(hs*r_scale)
-
- if (r_w > 10) and (r_h > 10):
- r_mask = cv2.resize(sample_masks[sel_ind], (r_w, r_h))
- r_image = cv2.resize(sample_images[sel_ind], (r_w, r_h))
- temp_crop = image[ymin:ymin+r_h, xmin:xmin+r_w]
- m_ind = r_mask > 0
- if m_ind.astype(np.int).sum() > 60:
- temp_crop[m_ind] = r_image[m_ind]
- #print(sample_labels[sel_ind])
- #print(sample_images[sel_ind].shape)
- #print(temp_crop.shape)
- box = np.array([xmin, ymin, xmin+r_w, ymin+r_h], dtype=np.float32)
- if len(labels):
- labels = np.concatenate((labels, [[sample_labels[sel_ind], *box]]), 0)
- else:
- labels = np.array([[sample_labels[sel_ind], *box]])
-
- image[ymin:ymin+r_h, xmin:xmin+r_w] = temp_crop
-
- return labels
-
-class Albumentations:
- # YOLOv5 Albumentations class (optional, only used if package is installed)
- def __init__(self):
- self.transform = None
- import albumentations as A
-
- self.transform = A.Compose([
- A.CLAHE(p=0.01),
- A.RandomBrightnessContrast(brightness_limit=0.2, contrast_limit=0.2, p=0.01),
- A.RandomGamma(gamma_limit=[80, 120], p=0.01),
- A.Blur(p=0.01),
- A.MedianBlur(p=0.01),
- A.ToGray(p=0.01),
- A.ImageCompression(quality_lower=75, p=0.01),],
- bbox_params=A.BboxParams(format='pascal_voc', label_fields=['class_labels']))
-
- #logging.info(colorstr('albumentations: ') + ', '.join(f'{x}' for x in self.transform.transforms if x.p))
-
- def __call__(self, im, labels, p=1.0):
- if self.transform and random.random() < p:
- new = self.transform(image=im, bboxes=labels[:, 1:], class_labels=labels[:, 0]) # transformed
- im, labels = new['image'], np.array([[c, *b] for c, b in zip(new['class_labels'], new['bboxes'])])
- return im, labels
-
-
-def create_folder(path='./new'):
- # Create folder
- if os.path.exists(path):
- shutil.rmtree(path) # delete output folder
- os.makedirs(path) # make new output folder
-
-
-def flatten_recursive(path='../coco'):
- # Flatten a recursive directory by bringing all files to top level
- new_path = Path(path + '_flat')
- create_folder(new_path)
- for file in tqdm(glob.glob(str(Path(path)) + '/**/*.*', recursive=True)):
- shutil.copyfile(file, new_path / Path(file).name)
-
-
-def extract_boxes(path='../coco/'): # from utils.datasets import *; extract_boxes('../coco128')
- # Convert detection dataset into classification dataset, with one directory per class
-
- path = Path(path) # images dir
- shutil.rmtree(path / 'classifier') if (path / 'classifier').is_dir() else None # remove existing
- files = list(path.rglob('*.*'))
- n = len(files) # number of files
- for im_file in tqdm(files, total=n):
- if im_file.suffix[1:] in img_formats:
- # image
- im = cv2.imread(str(im_file))[..., ::-1] # BGR to RGB
- h, w = im.shape[:2]
-
- # labels
- lb_file = Path(img2label_paths([str(im_file)])[0])
- if Path(lb_file).exists():
- with open(lb_file, 'r') as f:
- lb = np.array([x.split() for x in f.read().strip().splitlines()], dtype=np.float32) # labels
-
- for j, x in enumerate(lb):
- c = int(x[0]) # class
- f = (path / 'classifier') / f'{c}' / f'{path.stem}_{im_file.stem}_{j}.jpg' # new filename
- if not f.parent.is_dir():
- f.parent.mkdir(parents=True)
-
- b = x[1:] * [w, h, w, h] # box
- # b[2:] = b[2:].max() # rectangle to square
- b[2:] = b[2:] * 1.2 + 3 # pad
- b = xywh2xyxy(b.reshape(-1, 4)).ravel().astype(np.int)
-
- b[[0, 2]] = np.clip(b[[0, 2]], 0, w) # clip boxes outside of image
- b[[1, 3]] = np.clip(b[[1, 3]], 0, h)
- assert cv2.imwrite(str(f), im[b[1]:b[3], b[0]:b[2]]), f'box failure in {f}'
-
-
-def autosplit(path='../coco', weights=(0.9, 0.1, 0.0), annotated_only=False):
- """ Autosplit a dataset into train/val/test splits and save path/autosplit_*.txt files
- Usage: from utils.datasets import *; autosplit('../coco')
- Arguments
- path: Path to images directory
- weights: Train, val, test weights (list)
- annotated_only: Only use images with an annotated txt file
- """
- path = Path(path) # images dir
- files = sum([list(path.rglob(f"*.{img_ext}")) for img_ext in img_formats], []) # image files only
- n = len(files) # number of files
- indices = random.choices([0, 1, 2], weights=weights, k=n) # assign each image to a split
-
- txt = ['autosplit_train.txt', 'autosplit_val.txt', 'autosplit_test.txt'] # 3 txt files
- [(path / x).unlink() for x in txt if (path / x).exists()] # remove existing
-
- print(f'Autosplitting images from {path}' + ', using *.txt labeled images only' * annotated_only)
- for i, img in tqdm(zip(indices, files), total=n):
- if not annotated_only or Path(img2label_paths([str(img)])[0]).exists(): # check label
- with open(path / txt[i], 'a') as f:
- f.write(str(img) + '\n') # add image to txt file
-
-
-def load_segmentations(self, index):
- key = '/work/handsomejw66/coco17/' + self.img_files[index]
- #print(key)
- # /work/handsomejw66/coco17/
- return self.segs[key]
diff --git a/edge-detect/ai/sort.py b/edge-detect/ai/sort.py
deleted file mode 100644
index 5ead40b..0000000
--- a/edge-detect/ai/sort.py
+++ /dev/null
@@ -1,245 +0,0 @@
-import argparse
-import time
-import os
-import shutil
-from pathlib import Path
-import json
-
-import cv2
-import torch
-import torch.backends.cudnn as cudnn
-from numpy import random
-
-from models.experimental import attempt_load
-from utils.datasets import LoadStreams, LoadImages
-from utils.datasets2 import LoadFromDir
-from utils.general import check_img_size, check_requirements, check_imshow, non_max_suppression, apply_classifier, \
- scale_coords, xyxy2xywh, strip_optimizer, set_logging, increment_path
-from utils.plots import plot_one_box
-from utils.torch_utils import select_device, load_classifier, time_synchronized, TracedModel
-
-
-def dirsort(save_img=False):
- source, weights, view_img, save_txt, save_json, imgsz, trace = opt.source_dir, opt.weights, opt.view_img, opt.save_txt, opt.save_json, opt.img_size, not opt.no_trace
- save_img = not opt.nosave and not source.endswith('.txt') # save inference images
- webcam = source.isnumeric() or source.endswith('.txt') or source.lower().startswith(
- ('rtsp://', 'rtmp://', 'http://', 'https://'))
-
- # Directories
- plane_dir, noplane_dir, log_dir = opt.plane_dir, opt.noplane_dir, opt.log_dir
- Path(source).mkdir(parents=True, exist_ok=True)
- Path(plane_dir).mkdir(parents=True, exist_ok=True)
- Path(noplane_dir).mkdir(parents=True, exist_ok=True)
- Path(log_dir).mkdir(parents=True, exist_ok=True)
-
- # Initialize
- set_logging()
- device = select_device(opt.device)
- half = device.type != 'cpu' # half precision only supported on CUDA
-
- # Load model
- model = attempt_load(weights, map_location=device) # load FP32 model
- stride = int(model.stride.max()) # model stride
- imgsz = check_img_size(imgsz, s=stride) # check img_size
-
- if trace:
- model = TracedModel(model, device, opt.img_size)
-
- if half:
- model.half() # to FP16
-
- # Second-stage classifier
- classify = False
- if classify:
- modelc = load_classifier(name='resnet101', n=2) # initialize
- modelc.load_state_dict(torch.load('weights/resnet101.pt', map_location=device)['model']).to(device).eval()
-
- # Set Dataloader
- watch_dir = True
- vid_path, vid_writer = None, None
- if watch_dir:
- dataset = LoadFromDir(source, img_size=imgsz, stride=stride, log_dir=opt.log_dir)
- elif webcam:
- view_img = check_imshow()
- cudnn.benchmark = True # set True to speed up constant image size inference
- dataset = LoadStreams(source, img_size=imgsz, stride=stride)
- else:
- dataset = LoadImages(source, img_size=imgsz, stride=stride)
-
- # Get names and colors
- names = model.module.names if hasattr(model, 'module') else model.names
- colors = [[random.randint(0, 255) for _ in range(3)] for _ in names]
-
- # Run inference
- if device.type != 'cpu':
- model(torch.zeros(1, 3, imgsz, imgsz).to(device).type_as(next(model.parameters()))) # run once
- old_img_w = old_img_h = imgsz
- old_img_b = 1
-
- t0 = time.time()
- for path, img, im0s, vid_cap in dataset:
- img = torch.from_numpy(img).to(device)
- img = img.half() if half else img.float() # uint8 to fp16/32
- img /= 255.0 # 0 - 255 to 0.0 - 1.0
- if img.ndimension() == 3:
- img = img.unsqueeze(0)
-
- # Warmup
- if device.type != 'cpu' and (old_img_b != img.shape[0] or old_img_h != img.shape[2] or old_img_w != img.shape[3]):
- old_img_b = img.shape[0]
- old_img_h = img.shape[2]
- old_img_w = img.shape[3]
- for i in range(3):
- model(img, augment=opt.augment)[0]
-
- # Inference
- t1 = time_synchronized()
- pred = model(img, augment=opt.augment)[0]
- t2 = time_synchronized()
-
- # Apply NMS
- pred = non_max_suppression(pred, opt.conf_thres, opt.iou_thres, classes=opt.classes, agnostic=opt.agnostic_nms)
- t3 = time_synchronized()
-
- # Apply Classifier
- if classify:
- pred = apply_classifier(pred, modelc, img, im0s)
-
- # Process detections
- for i, det in enumerate(pred): # detections per image
- if webcam: # batch_size >= 1
- p, s, im0, frame = path[i], '%g: ' % i, im0s[i].copy(), dataset.count
- else:
- p, s, im0, frame = path, '', im0s, getattr(dataset, 'frame', 0)
-
- p = Path(p) # to Path
- save_path = str(log_dir / Path('images') / p.name) # img.jpg
- txt_path = str(log_dir / Path('labels') / p.stem) + ('' if dataset.mode == 'image' else f'_{frame}') # img.txt
- gn = torch.tensor(im0.shape)[[1, 0, 1, 0]] # normalization gain whwh
- if len(det):
- # Rescale boxes from img_size to im0 size
- det[:, :4] = scale_coords(img.shape[2:], det[:, :4], im0.shape).round()
-
- # Print results
- for c in det[:, -1].unique():
- n = (det[:, -1] == c).sum() # detections per class
- s += f"{n} {names[int(c)]}{'s' * (n > 1)}, " # add to string
-
- # Write results
- for *xyxy, conf, cls in reversed(det):
- if save_txt: # Write to file
- xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist() # normalized xywh
- line = (cls, *xywh, conf) if opt.save_conf else (cls, *xywh) # label format
- with open(txt_path + '.txt', 'a') as f:
- f.write(('%g ' * len(line)).rstrip() % line + '\n')
-
- if save_img or view_img: # Add bbox to image
- label = f'{names[int(cls)]} {conf:.2f}'
- plot_one_box(xyxy, im0, label=label, color=colors[int(cls)], line_thickness=1)
-
- # Print time (inference + NMS)
- print(f'{s}Done. ({(1E3 * (t2 - t1)):.1f}ms) Inference, ({(1E3 * (t3 - t2)):.1f}ms) NMS')
-
- # Stream results
- if view_img:
- cv2.imshow(str(p), im0)
- cv2.waitKey(1) # 1 millisecond
-
- # Save results (image with detections)
- if save_img:
- if dataset.mode == 'image':
- write_flag = cv2.imwrite(save_path, im0)
- if not write_flag:
- print('! Image write error')
- print(f" The image with the result is saved in: {save_path}")
- else: # 'video' or 'stream'
- if vid_path != save_path: # new video
- vid_path = save_path
- if isinstance(vid_writer, cv2.VideoWriter):
- vid_writer.release() # release previous video writer
- if vid_cap: # video
- fps = vid_cap.get(cv2.CAP_PROP_FPS)
- w = int(vid_cap.get(cv2.CAP_PROP_FRAME_WIDTH))
- h = int(vid_cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
- else: # stream
- fps, w, h = 30, im0.shape[1], im0.shape[0]
- save_path += '.mp4'
- vid_writer = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*'mp4v'), fps, (w, h))
- vid_writer.write(im0)
-
- # Move image file to destination folder based on model output
- if pred[0].shape[0] > 0:
- print('plane')
- shutil.move(path, os.path.join(plane_dir, os.path.basename(path)))
- else:
- shutil.move(path, os.path.join(noplane_dir, os.path.basename(path)))
- print('noplane')
-
- if save_json:
- struct = []
- for rowidx in range(pred[0].shape[0]):
- row = pred[0][rowidx, :]
- entry = {}
- entry['bbox'] = row[:4].numpy().tolist()
- entry['score'] = row[4].numpy().item()
- entry['category_id'] = row[5].int().numpy().item()
- struct.append(entry)
- path = os.path.join(
- log_dir, os.path.splitext(os.path.basename(path))[0] + '.json')
- if len(struct) > 0:
- json.dump(struct, open(path, 'w'))
-
- if save_txt or save_img:
- s = f"\n{len(list(save_dir.glob('labels/*.txt')))} labels saved to {save_dir / 'labels'}" if save_txt else ''
- #print(f"Results saved to {save_dir}{s}")
-
- print(f'Done. ({time.time() - t0:.3f}s)')
-
-
-if __name__ == '__main__':
- parser = argparse.ArgumentParser()
- parser.add_argument('--weights', nargs='+', type=str, default='yolov7.pt', help='model.pt path(s)')
-
- parser.add_argument('--source-dir', type=str,
- default='/data/edge_sandbox/tosort',
- help='Folder of images to sort')
- parser.add_argument('--plane-dir', type=str,
- default='/data/edge_sandbox/plane',
- help='Folder to save images of aircraft')
- parser.add_argument('--noplane-dir', type=str,
- default='/data/edge_sandbox/noplane',
- help='Folder to save images lacking aircraft')
- parser.add_argument('--log-dir', type=str,
- default='/data/edge_sandbox/log',
- help='Folder to store log information')
-
- #parser.add_argument('--source', type=str, default='inference/images', help='source') # file/folder, 0 for webcam
- parser.add_argument('--img-size', type=int, default=640, help='inference size (pixels)')
- parser.add_argument('--conf-thres', type=float, default=0.25, help='object confidence threshold')
- parser.add_argument('--iou-thres', type=float, default=0.45, help='IOU threshold for NMS')
- parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
- parser.add_argument('--view-img', action='store_true', help='display results')
- parser.add_argument('--save-txt', action='store_true', help='save results to *.txt')
- parser.add_argument('--save-img', action='store_true', help='save image with bounding boxes')
- parser.add_argument('--save-json', action='store_true', help='save results to *.json')
- parser.add_argument('--save-conf', action='store_true', help='save confidences in --save-txt labels')
- parser.add_argument('--nosave', action='store_true', help='do not save images/videos')
- parser.add_argument('--classes', nargs='+', type=int, help='filter by class: --class 0, or --class 0 2 3')
- parser.add_argument('--agnostic-nms', action='store_true', help='class-agnostic NMS')
- parser.add_argument('--augment', action='store_true', help='augmented inference')
- parser.add_argument('--update', action='store_true', help='update all models')
- #parser.add_argument('--project', default='runs/detect', help='save results to project/name')
- #parser.add_argument('--name', default='exp', help='save results to project/name')
- #parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment')
- parser.add_argument('--no-trace', action='store_true', help='don`t trace model')
- opt = parser.parse_args()
- print(opt)
- #check_requirements(exclude=('pycocotools', 'thop'))
-
- with torch.no_grad():
- if opt.update: # update all models (to fix SourceChangeWarning)
- for opt.weights in ['yolov7.pt']:
- dirsort()
- strip_optimizer(opt.weights)
- else:
- dirsort()
diff --git a/edge-detect/docker-compose.yml b/edge-detect/docker-compose.yml
deleted file mode 100644
index ee829c2..0000000
--- a/edge-detect/docker-compose.yml
+++ /dev/null
@@ -1,15 +0,0 @@
-version: '3.7'
-
-services:
- skyscan-edge:
- build: ./ai/
- command: "python sort.py --weights ../data/weights/localizer.pt --agnostic-nms --nosave --conf 0.25 --img-size 640 --device cpu --source-dir ../data/tosort --plane-dir ../data/plane --noplane-dir ../data/noplane --log-dir ../data/log --save-json & python processFiles.py"
- environment:
- - "HOSTNAME=${HOSTNAME}"
- volumes:
- - ./weights:/data/weights/
- - /flash/raw:/data/tosort
- - /flash/processed/plane:/data/plane
- - /flash/processed/noplane:/data/noplane
- - /flash/processed/log:/data/log
- restart: unless-stopped