817 lines
32 KiB
Python
Executable File
817 lines
32 KiB
Python
Executable File
import glob
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import math
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import os
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import random
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import shutil
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import time
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from pathlib import Path
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from threading import Thread
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import cv2
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import numpy as np
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import torch
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from PIL import Image, ExifTags
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from torch.utils.data import Dataset
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from tqdm import tqdm
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from utils.utils import xyxy2xywh, xywh2xyxy
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img_formats = ['.bmp', '.jpg', '.jpeg', '.png', '.tif']
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vid_formats = ['.mov', '.avi', '.mp4']
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# Get orientation exif tag
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for orientation in ExifTags.TAGS.keys():
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if ExifTags.TAGS[orientation] == 'Orientation':
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break
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def exif_size(img):
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# Returns exif-corrected PIL size
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s = img.size # (width, height)
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try:
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rotation = dict(img._getexif().items())[orientation]
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if rotation == 6: # rotation 270
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s = (s[1], s[0])
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elif rotation == 8: # rotation 90
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s = (s[1], s[0])
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except:
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pass
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return s
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class LoadImages: # for inference
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def __init__(self, path, img_size=416, half=False):
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path = str(Path(path)) # os-agnostic
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files = []
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if os.path.isdir(path):
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files = sorted(glob.glob(os.path.join(path, '*.*')))
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elif os.path.isfile(path):
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files = [path]
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images = [x for x in files if os.path.splitext(x)[-1].lower() in img_formats]
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videos = [x for x in files if os.path.splitext(x)[-1].lower() in vid_formats]
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nI, nV = len(images), len(videos)
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self.img_size = img_size
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self.files = images + videos
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self.nF = nI + nV # number of files
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self.video_flag = [False] * nI + [True] * nV
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self.mode = 'images'
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self.half = half # half precision fp16 images
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if any(videos):
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self.new_video(videos[0]) # new video
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else:
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self.cap = None
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assert self.nF > 0, 'No images or videos found in ' + path
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def __iter__(self):
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self.count = 0
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return self
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def __next__(self):
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if self.count == self.nF:
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raise StopIteration
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path = self.files[self.count]
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if self.video_flag[self.count]:
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# Read video
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self.mode = 'video'
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ret_val, img0 = self.cap.read()
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if not ret_val:
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self.count += 1
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self.cap.release()
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if self.count == self.nF: # last video
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raise StopIteration
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else:
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path = self.files[self.count]
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self.new_video(path)
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ret_val, img0 = self.cap.read()
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self.frame += 1
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print('video %g/%g (%g/%g) %s: ' % (self.count + 1, self.nF, self.frame, self.nframes, path), end='')
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else:
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# Read image
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self.count += 1
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img0 = cv2.imread(path) # BGR
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assert img0 is not None, 'Image Not Found ' + path
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print('image %g/%g %s: ' % (self.count, self.nF, path), end='')
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# Padded resize
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img = letterbox(img0, new_shape=self.img_size)[0]
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# Normalize RGB
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img = img[:, :, ::-1].transpose(2, 0, 1) # BGR to RGB
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img = np.ascontiguousarray(img, dtype=np.float16 if self.half else np.float32) # uint8 to fp16/fp32
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img /= 255.0 # 0 - 255 to 0.0 - 1.0
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# cv2.imwrite(path + '.letterbox.jpg', 255 * img.transpose((1, 2, 0))[:, :, ::-1]) # save letterbox image
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return path, img, img0, self.cap
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def new_video(self, path):
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self.frame = 0
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self.cap = cv2.VideoCapture(path)
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self.nframes = int(self.cap.get(cv2.CAP_PROP_FRAME_COUNT))
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def __len__(self):
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return self.nF # number of files
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class LoadWebcam: # for inference
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def __init__(self, pipe=0, img_size=416, half=False):
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self.img_size = img_size
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self.half = half # half precision fp16 images
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if pipe == '0':
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pipe = 0 # local camera
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# pipe = 'rtsp://192.168.1.64/1' # IP camera
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# pipe = 'rtsp://username:password@192.168.1.64/1' # IP camera with login
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# pipe = 'rtsp://170.93.143.139/rtplive/470011e600ef003a004ee33696235daa' # IP traffic camera
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# pipe = 'http://wmccpinetop.axiscam.net/mjpg/video.mjpg' # IP golf camera
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# https://answers.opencv.org/question/215996/changing-gstreamer-pipeline-to-opencv-in-pythonsolved/
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# pipe = '"rtspsrc location="rtsp://username:password@192.168.1.64/1" latency=10 ! appsink' # GStreamer
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# https://answers.opencv.org/question/200787/video-acceleration-gstremer-pipeline-in-videocapture/
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# https://stackoverflow.com/questions/54095699/install-gstreamer-support-for-opencv-python-package # install help
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# pipe = "rtspsrc location=rtsp://root:root@192.168.0.91:554/axis-media/media.amp?videocodec=h264&resolution=3840x2160 protocols=GST_RTSP_LOWER_TRANS_TCP ! rtph264depay ! queue ! vaapih264dec ! videoconvert ! appsink" # GStreamer
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self.pipe = pipe
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self.cap = cv2.VideoCapture(pipe) # video capture object
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self.cap.set(cv2.CAP_PROP_BUFFERSIZE, 3) # set buffer size
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def __iter__(self):
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self.count = -1
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return self
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def __next__(self):
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self.count += 1
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if cv2.waitKey(1) == ord('q'): # q to quit
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self.cap.release()
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cv2.destroyAllWindows()
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raise StopIteration
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# Read frame
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if self.pipe == 0: # local camera
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ret_val, img0 = self.cap.read()
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img0 = cv2.flip(img0, 1) # flip left-right
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else: # IP camera
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n = 0
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while True:
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n += 1
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self.cap.grab()
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if n % 30 == 0: # skip frames
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ret_val, img0 = self.cap.retrieve()
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if ret_val:
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break
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# Print
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assert ret_val, 'Camera Error %s' % self.pipe
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img_path = 'webcam.jpg'
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print('webcam %g: ' % self.count, end='')
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# Padded resize
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img = letterbox(img0, new_shape=self.img_size)[0]
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# Normalize RGB
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img = img[:, :, ::-1].transpose(2, 0, 1) # BGR to RGB
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img = np.ascontiguousarray(img, dtype=np.float16 if self.half else np.float32) # uint8 to fp16/fp32
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img /= 255.0 # 0 - 255 to 0.0 - 1.0
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return img_path, img, img0, None
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def __len__(self):
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return 0
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class LoadStreams: # multiple IP or RTSP cameras
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def __init__(self, sources='streams.txt', img_size=416, half=False):
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self.mode = 'images'
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self.img_size = img_size
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self.half = half # half precision fp16 images
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if os.path.isfile(sources):
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with open(sources, 'r') as f:
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sources = [x.strip() for x in f.read().splitlines() if len(x.strip())]
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else:
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sources = [sources]
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n = len(sources)
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self.imgs = [None] * n
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self.sources = sources
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for i, s in enumerate(sources):
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# Start the thread to read frames from the video stream
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print('%g/%g: %s... ' % (i + 1, n, s), end='')
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cap = cv2.VideoCapture(0 if s == '0' else s)
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assert cap.isOpened(), 'Failed to open %s' % s
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w = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
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h = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
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fps = cap.get(cv2.CAP_PROP_FPS) % 100
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_, self.imgs[i] = cap.read() # guarantee first frame
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thread = Thread(target=self.update, args=([i, cap]), daemon=True)
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print(' success (%gx%g at %.2f FPS).' % (w, h, fps))
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thread.start()
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print('') # newline
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def update(self, index, cap):
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# Read next stream frame in a daemon thread
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n = 0
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while cap.isOpened():
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n += 1
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# _, self.imgs[index] = cap.read()
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cap.grab()
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if n == 4: # read every 4th frame
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_, self.imgs[index] = cap.retrieve()
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n = 0
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time.sleep(0.01) # wait time
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def __iter__(self):
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self.count = -1
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return self
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def __next__(self):
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self.count += 1
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img0 = self.imgs.copy()
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if cv2.waitKey(1) == ord('q'): # q to quit
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cv2.destroyAllWindows()
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raise StopIteration
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# Letterbox
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img = [letterbox(x, new_shape=self.img_size, interp=cv2.INTER_LINEAR)[0] for x in img0]
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# Stack
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img = np.stack(img, 0)
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# Normalize RGB
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img = img[:, :, :, ::-1].transpose(0, 3, 1, 2) # BGR to RGB
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img = np.ascontiguousarray(img, dtype=np.float16 if self.half else np.float32) # uint8 to fp16/fp32
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img /= 255.0 # 0 - 255 to 0.0 - 1.0
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return self.sources, img, img0, None
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def __len__(self):
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return 0 # 1E12 frames = 32 streams at 30 FPS for 30 years
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class LoadImagesAndLabels(Dataset): # for training/testing
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def __init__(self, path, img_size=416, batch_size=16, augment=False, hyp=None, rect=True, image_weights=False,
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cache_labels=False, cache_images=False):
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path = str(Path(path)) # os-agnostic
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with open(path, 'r') as f:
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self.img_files = [x.replace('/', os.sep) for x in f.read().splitlines() # os-agnostic
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if os.path.splitext(x)[-1].lower() in img_formats]
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n = len(self.img_files)
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bi = np.floor(np.arange(n) / batch_size).astype(np.int) # batch index
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nb = bi[-1] + 1 # number of batches
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assert n > 0, 'No images found in %s' % path
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self.n = n
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self.batch = bi # batch index of image
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self.img_size = img_size
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self.augment = augment
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self.hyp = hyp
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self.image_weights = image_weights
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self.rect = False if image_weights else rect
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# Define labels
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self.label_files = [x.replace('images', 'labels').replace(os.path.splitext(x)[-1], '.txt')
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for x in self.img_files]
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# Rectangular Training https://github.com/ultralytics/yolov3/issues/232
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if self.rect:
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# Read image shapes
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sp = 'data' + os.sep + path.replace('.txt', '.shapes').split(os.sep)[-1] # shapefile path
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try:
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with open(sp, 'r') as f: # read existing shapefile
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s = [x.split() for x in f.read().splitlines()]
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assert len(s) == n, 'Shapefile out of sync'
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except:
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s = [exif_size(Image.open(f)) for f in tqdm(self.img_files, desc='Reading image shapes')]
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np.savetxt(sp, s, fmt='%g') # overwrites existing (if any)
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# Sort by aspect ratio
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s = np.array(s, dtype=np.float64)
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ar = s[:, 1] / s[:, 0] # aspect ratio
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i = ar.argsort()
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self.img_files = [self.img_files[i] for i in i]
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self.label_files = [self.label_files[i] for i in i]
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self.shapes = s[i]
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ar = ar[i]
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# Set training image shapes
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shapes = [[1, 1]] * nb
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for i in range(nb):
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ari = ar[bi == i]
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mini, maxi = ari.min(), ari.max()
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if maxi < 1:
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shapes[i] = [maxi, 1]
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elif mini > 1:
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shapes[i] = [1, 1 / mini]
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self.batch_shapes = np.ceil(np.array(shapes) * img_size / 32.).astype(np.int) * 32
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# Preload labels (required for weighted CE training)
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self.imgs = [None] * n
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self.labels = [None] * n
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if cache_labels or image_weights: # cache labels for faster training
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self.labels = [np.zeros((0, 5))] * n
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extract_bounding_boxes = False
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create_datasubset = False
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pbar = tqdm(self.label_files, desc='Reading labels')
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nm, nf, ne, ns = 0, 0, 0, 0 # number missing, number found, number empty, number datasubset
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for i, file in enumerate(pbar):
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try:
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with open(file, 'r') as f:
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l = np.array([x.split() for x in f.read().splitlines()], dtype=np.float32)
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except:
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nm += 1 # print('missing labels for image %s' % self.img_files[i]) # file missing
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continue
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if l.shape[0]:
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assert l.shape[1] == 5, '> 5 label columns: %s' % file
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assert (l >= 0).all(), 'negative labels: %s' % file
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assert (l[:, 1:] <= 1).all(), 'non-normalized or out of bounds coordinate labels: %s' % file
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self.labels[i] = l
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nf += 1 # file found
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# Create subdataset (a smaller dataset)
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if create_datasubset and ns < 1E4:
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if ns == 0:
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create_folder(path='./datasubset')
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os.makedirs('./datasubset/images')
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exclude_classes = 43
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if exclude_classes not in l[:, 0]:
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ns += 1
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# shutil.copy(src=self.img_files[i], dst='./datasubset/images/') # copy image
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with open('./datasubset/images.txt', 'a') as f:
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f.write(self.img_files[i] + '\n')
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# Extract object detection boxes for a second stage classifier
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if extract_bounding_boxes:
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p = Path(self.img_files[i])
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img = cv2.imread(str(p))
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h, w = img.shape[:2]
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for j, x in enumerate(l):
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f = '%s%sclassifier%s%g_%g_%s' % (p.parent.parent, os.sep, os.sep, x[0], j, p.name)
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if not os.path.exists(Path(f).parent):
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os.makedirs(Path(f).parent) # make new output folder
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b = x[1:] * np.array([w, h, w, h]) # box
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b[2:] = b[2:].max() # rectangle to square
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b[2:] = b[2:] * 1.3 + 30 # pad
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b = xywh2xyxy(b.reshape(-1, 4)).ravel().astype(np.int)
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b[[0, 2]] = np.clip(b[[0, 2]], 0, w) # clip boxes outside of image
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b[[1, 3]] = np.clip(b[[1, 3]], 0, h)
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assert cv2.imwrite(f, img[b[1]:b[3], b[0]:b[2]]), 'Failure extracting classifier boxes'
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else:
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ne += 1 # file empty
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pbar.desc = 'Reading labels (%g found, %g missing, %g empty for %g images)' % (nf, nm, ne, n)
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assert nf > 0, 'No labels found. Recommend correcting image and label paths.'
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# Cache images into memory for faster training (~5GB)
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if cache_images and augment: # if training
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for i in tqdm(range(min(len(self.img_files), 10000)), desc='Reading images'): # max 10k images
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img_path = self.img_files[i]
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img = cv2.imread(img_path) # BGR
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assert img is not None, 'Image Not Found ' + img_path
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r = self.img_size / max(img.shape) # size ratio
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if self.augment and r < 1: # if training (NOT testing), downsize to inference shape
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h, w = img.shape[:2]
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img = cv2.resize(img, (int(w * r), int(h * r)), interpolation=cv2.INTER_LINEAR) # or INTER_AREA
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self.imgs[i] = img
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# Detect corrupted images https://medium.com/joelthchao/programmatically-detect-corrupted-image-8c1b2006c3d3
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detect_corrupted_images = False
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if detect_corrupted_images:
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from skimage import io # conda install -c conda-forge scikit-image
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for file in tqdm(self.img_files, desc='Detecting corrupted images'):
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try:
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_ = io.imread(file)
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except:
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print('Corrupted image detected: %s' % file)
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def __len__(self):
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return len(self.img_files)
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# def __iter__(self):
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# self.count = -1
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# print('ran dataset iter')
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# #self.shuffled_vector = np.random.permutation(self.nF) if self.augment else np.arange(self.nF)
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# return self
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def __getitem__(self, index):
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if self.image_weights:
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index = self.indices[index]
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img_path = self.img_files[index]
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label_path = self.label_files[index]
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mosaic = True and self.augment # load 4 images at a time into a mosaic (only during training)
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if mosaic:
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# Load mosaic
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img, labels = load_mosaic(self, index)
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h, w = img.shape[:2]
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else:
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# Load image
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img = load_image(self, index)
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# Letterbox
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h, w = img.shape[:2]
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if self.rect:
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img, ratio, padw, padh = letterbox(img, self.batch_shapes[self.batch[index]], mode='rect')
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else:
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img, ratio, padw, padh = letterbox(img, self.img_size, mode='square')
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# Load labels
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labels = []
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if os.path.isfile(label_path):
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x = self.labels[index]
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if x is None: # labels not preloaded
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with open(label_path, 'r') as f:
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x = np.array([x.split() for x in f.read().splitlines()], dtype=np.float32)
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if x.size > 0:
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# Normalized xywh to pixel xyxy format
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labels = x.copy()
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labels[:, 1] = ratio[0] * w * (x[:, 1] - x[:, 3] / 2) + padw
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labels[:, 2] = ratio[1] * h * (x[:, 2] - x[:, 4] / 2) + padh
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labels[:, 3] = ratio[0] * w * (x[:, 1] + x[:, 3] / 2) + padw
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labels[:, 4] = ratio[1] * h * (x[:, 2] + x[:, 4] / 2) + padh
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if self.augment:
|
|
# Augment colorspace
|
|
augment_hsv(img, hgain=self.hyp['hsv_h'], sgain=self.hyp['hsv_s'], vgain=self.hyp['hsv_v'])
|
|
|
|
# Augment imagespace
|
|
g = 0.0 if mosaic else 1.0 # do not augment mosaics
|
|
hyp = self.hyp
|
|
img, labels = random_affine(img, labels,
|
|
degrees=hyp['degrees'] * g,
|
|
translate=hyp['translate'] * g,
|
|
scale=hyp['scale'] * g,
|
|
shear=hyp['shear'] * g)
|
|
|
|
# Apply cutouts
|
|
# if random.random() < 0.9:
|
|
# labels = cutout(img, labels)
|
|
|
|
nL = len(labels) # number of labels
|
|
if nL:
|
|
# convert xyxy to xywh
|
|
labels[:, 1:5] = xyxy2xywh(labels[:, 1:5])
|
|
|
|
# Normalize coordinates 0 - 1
|
|
labels[:, [2, 4]] /= img.shape[0] # height
|
|
labels[:, [1, 3]] /= img.shape[1] # width
|
|
|
|
if self.augment:
|
|
# random left-right flip
|
|
lr_flip = True
|
|
if lr_flip and random.random() < 0.5:
|
|
img = np.fliplr(img)
|
|
if nL:
|
|
labels[:, 1] = 1 - labels[:, 1]
|
|
|
|
# random up-down flip
|
|
ud_flip = False
|
|
if ud_flip and random.random() < 0.5:
|
|
img = np.flipud(img)
|
|
if nL:
|
|
labels[:, 2] = 1 - labels[:, 2]
|
|
|
|
labels_out = torch.zeros((nL, 6))
|
|
if nL:
|
|
labels_out[:, 1:] = torch.from_numpy(labels)
|
|
|
|
# Normalize
|
|
img = img[:, :, ::-1].transpose(2, 0, 1) # BGR to RGB, to 3x416x416
|
|
img = np.ascontiguousarray(img, dtype=np.float32) # uint8 to float32
|
|
img /= 255.0 # 0 - 255 to 0.0 - 1.0
|
|
|
|
return torch.from_numpy(img), labels_out, img_path, (h, w)
|
|
|
|
@staticmethod
|
|
def collate_fn(batch):
|
|
img, label, path, hw = list(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, hw
|
|
|
|
|
|
def load_image(self, index):
|
|
# loads 1 image from dataset
|
|
img = self.imgs[index]
|
|
if img is None:
|
|
img_path = self.img_files[index]
|
|
img = cv2.imread(img_path) # BGR
|
|
assert img is not None, 'Image Not Found ' + img_path
|
|
r = self.img_size / max(img.shape) # size ratio
|
|
if self.augment: # if training (NOT testing), downsize to inference shape
|
|
h, w = img.shape[:2]
|
|
img = cv2.resize(img, (int(w * r), int(h * r)), interpolation=cv2.INTER_LINEAR) # _LINEAR fastest
|
|
return img
|
|
|
|
|
|
def augment_hsv(img, hgain=0.5, sgain=0.5, vgain=0.5):
|
|
x = (np.random.uniform(-1, 1, 3) * np.array([hgain, sgain, vgain]) + 1).astype(np.float32) # random gains
|
|
img_hsv = (cv2.cvtColor(img, cv2.COLOR_BGR2HSV) * x.reshape((1, 1, 3))).clip(None, 255).astype(np.uint8)
|
|
cv2.cvtColor(img_hsv, cv2.COLOR_HSV2BGR, dst=img) # no return needed
|
|
|
|
|
|
# def augment_hsv(img, hgain=0.5, sgain=0.5, vgain=0.5): # original version
|
|
# # SV augmentation by 50%
|
|
# img_hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV) # hue, sat, val
|
|
#
|
|
# S = img_hsv[:, :, 1].astype(np.float32) # saturation
|
|
# V = img_hsv[:, :, 2].astype(np.float32) # value
|
|
#
|
|
# a = random.uniform(-1, 1) * sgain + 1
|
|
# b = random.uniform(-1, 1) * vgain + 1
|
|
# S *= a
|
|
# V *= b
|
|
#
|
|
# img_hsv[:, :, 1] = S if a < 1 else S.clip(None, 255)
|
|
# img_hsv[:, :, 2] = V if b < 1 else V.clip(None, 255)
|
|
# cv2.cvtColor(img_hsv, cv2.COLOR_HSV2BGR, dst=img) # no return needed
|
|
|
|
|
|
def load_mosaic(self, index):
|
|
# loads images in a mosaic
|
|
|
|
labels4 = []
|
|
s = self.img_size
|
|
xc, yc = [int(random.uniform(s * 0.5, s * 1.5)) for _ in range(2)] # mosaic center x, y
|
|
img4 = np.zeros((s * 2, s * 2, 3), dtype=np.uint8) + 128 # base image with 4 tiles
|
|
indices = [index] + [random.randint(0, len(self.labels) - 1) for _ in range(3)] # 3 additional image indices
|
|
for i, index in enumerate(indices):
|
|
# Load image
|
|
img = load_image(self, index)
|
|
h, w, _ = img.shape
|
|
|
|
# place img in img4
|
|
if i == 0: # top left
|
|
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, max(xc, 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
|
|
|
|
# Load labels
|
|
label_path = self.label_files[index]
|
|
if os.path.isfile(label_path):
|
|
x = self.labels[index]
|
|
if x is None: # labels not preloaded
|
|
with open(label_path, 'r') as f:
|
|
x = np.array([x.split() for x in f.read().splitlines()], dtype=np.float32)
|
|
|
|
if x.size > 0:
|
|
# Normalized xywh to pixel xyxy format
|
|
labels = x.copy()
|
|
labels[:, 1] = w * (x[:, 1] - x[:, 3] / 2) + padw
|
|
labels[:, 2] = h * (x[:, 2] - x[:, 4] / 2) + padh
|
|
labels[:, 3] = w * (x[:, 1] + x[:, 3] / 2) + padw
|
|
labels[:, 4] = h * (x[:, 2] + x[:, 4] / 2) + padh
|
|
else:
|
|
labels = np.zeros((0, 5), dtype=np.float32)
|
|
labels4.append(labels)
|
|
|
|
if len(labels4):
|
|
labels4 = np.concatenate(labels4, 0)
|
|
|
|
# hyp = self.hyp
|
|
# img4, labels4 = random_affine(img4, labels4,
|
|
# degrees=hyp['degrees'],
|
|
# translate=hyp['translate'],
|
|
# scale=hyp['scale'],
|
|
# shear=hyp['shear'])
|
|
|
|
# Center crop
|
|
a = s // 2
|
|
img4 = img4[a:a + s, a:a + s]
|
|
if len(labels4):
|
|
labels4[:, 1:] -= a
|
|
|
|
return img4, labels4
|
|
|
|
|
|
def letterbox(img, new_shape=416, color=(128, 128, 128), mode='auto', interp=cv2.INTER_AREA):
|
|
# Resize a rectangular image to a 32 pixel multiple rectangle
|
|
# https://github.com/ultralytics/yolov3/issues/232
|
|
shape = img.shape[:2] # current shape [height, width]
|
|
|
|
if isinstance(new_shape, int):
|
|
r = float(new_shape) / max(shape) # ratio = new / old
|
|
else:
|
|
r = max(new_shape) / max(shape)
|
|
ratio = r, r # width, height ratios
|
|
new_unpad = (int(round(shape[1] * r)), int(round(shape[0] * r)))
|
|
|
|
# Compute padding https://github.com/ultralytics/yolov3/issues/232
|
|
if mode is 'auto': # minimum rectangle
|
|
dw = np.mod(new_shape - new_unpad[0], 32) / 2 # width padding
|
|
dh = np.mod(new_shape - new_unpad[1], 32) / 2 # height padding
|
|
elif mode is 'square': # square
|
|
dw = (new_shape - new_unpad[0]) / 2 # width padding
|
|
dh = (new_shape - new_unpad[1]) / 2 # height padding
|
|
elif mode is 'rect': # square
|
|
dw = (new_shape[1] - new_unpad[0]) / 2 # width padding
|
|
dh = (new_shape[0] - new_unpad[1]) / 2 # height padding
|
|
elif mode is 'scaleFill':
|
|
dw, dh = 0.0, 0.0
|
|
new_unpad = (new_shape, new_shape)
|
|
ratio = new_shape / shape[1], new_shape / shape[0] # width, height ratios
|
|
|
|
if shape[::-1] != new_unpad: # resize
|
|
img = cv2.resize(img, new_unpad, interpolation=interp) # INTER_AREA is better, INTER_LINEAR is faster
|
|
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_affine(img, targets=(), degrees=10, translate=.1, scale=.1, shear=10):
|
|
# torchvision.transforms.RandomAffine(degrees=(-10, 10), translate=(.1, .1), scale=(.9, 1.1), shear=(-10, 10))
|
|
# https://medium.com/uruvideo/dataset-augmentation-with-random-homographies-a8f4b44830d4
|
|
|
|
if targets is None: # targets = [cls, xyxy]
|
|
targets = []
|
|
border = 0 # width of added border (optional)
|
|
height = img.shape[0] + border * 2
|
|
width = img.shape[1] + border * 2
|
|
|
|
# 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 + scale)
|
|
R[:2] = cv2.getRotationMatrix2D(angle=a, center=(img.shape[1] / 2, img.shape[0] / 2), scale=s)
|
|
|
|
# Translation
|
|
T = np.eye(3)
|
|
T[0, 2] = random.uniform(-translate, translate) * img.shape[0] + border # x translation (pixels)
|
|
T[1, 2] = random.uniform(-translate, translate) * img.shape[1] + border # y translation (pixels)
|
|
|
|
# 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)
|
|
|
|
# Combined rotation matrix
|
|
M = S @ T @ R # ORDER IS IMPORTANT HERE!!
|
|
changed = (border != 0) or (M != np.eye(3)).any()
|
|
if changed:
|
|
img = cv2.warpAffine(img, M[:2], dsize=(width, height), flags=cv2.INTER_AREA, borderValue=(128, 128, 128))
|
|
|
|
# Transform label coordinates
|
|
n = len(targets)
|
|
if n:
|
|
# warp points
|
|
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)[:, :2].reshape(n, 8)
|
|
|
|
# create new boxes
|
|
x = xy[:, [0, 2, 4, 6]]
|
|
y = xy[:, [1, 3, 5, 7]]
|
|
xy = np.concatenate((x.min(1), y.min(1), x.max(1), y.max(1))).reshape(4, n).T
|
|
|
|
# # apply angle-based reduction of bounding boxes
|
|
# radians = a * math.pi / 180
|
|
# reduction = max(abs(math.sin(radians)), abs(math.cos(radians))) ** 0.5
|
|
# x = (xy[:, 2] + xy[:, 0]) / 2
|
|
# y = (xy[:, 3] + xy[:, 1]) / 2
|
|
# w = (xy[:, 2] - xy[:, 0]) * reduction
|
|
# h = (xy[:, 3] - xy[:, 1]) * reduction
|
|
# xy = np.concatenate((x - w / 2, y - h / 2, x + w / 2, y + h / 2)).reshape(4, n).T
|
|
|
|
# reject warped points outside of image
|
|
xy[:, [0, 2]] = xy[:, [0, 2]].clip(0, width)
|
|
xy[:, [1, 3]] = xy[:, [1, 3]].clip(0, height)
|
|
w = xy[:, 2] - xy[:, 0]
|
|
h = xy[:, 3] - xy[:, 1]
|
|
area = w * h
|
|
area0 = (targets[:, 3] - targets[:, 1]) * (targets[:, 4] - targets[:, 2])
|
|
ar = np.maximum(w / (h + 1e-16), h / (w + 1e-16))
|
|
i = (w > 4) & (h > 4) & (area / (area0 + 1e-16) > 0.1) & (ar < 10)
|
|
|
|
targets = targets[i]
|
|
targets[:, 1:5] = xy[i]
|
|
|
|
return img, targets
|
|
|
|
|
|
def cutout(image, labels):
|
|
# https://arxiv.org/abs/1708.04552
|
|
# https://github.com/hysts/pytorch_cutout/blob/master/dataloader.py
|
|
# https://towardsdatascience.com/when-conventional-wisdom-fails-revisiting-data-augmentation-for-self-driving-cars-4831998c5509
|
|
h, w = image.shape[:2]
|
|
|
|
def bbox_ioa(box1, box2, x1y1x2y2=True):
|
|
# 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
|
|
|
|
# create random masks
|
|
scales = [0.5] * 1 # + [0.25] * 4 + [0.125] * 16 + [0.0625] * 64 + [0.03125] * 256 # 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
|
|
mask_color = [random.randint(0, 255) for _ in range(3)]
|
|
image[ymin:ymax, xmin:xmax] = mask_color
|
|
|
|
# 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 < 0.90] # remove >90% obscured labels
|
|
|
|
return labels
|
|
|
|
|
|
def reduce_img_size(path='../data/sm3/images', img_size=1024): # from utils.datasets import *; reduce_img_size()
|
|
# creates a new ./images_reduced folder with reduced size images of maximum size img_size
|
|
path_new = path + '_reduced' # reduced images path
|
|
create_folder(path_new)
|
|
for f in tqdm(glob.glob('%s/*.*' % path)):
|
|
try:
|
|
img = cv2.imread(f)
|
|
h, w = img.shape[:2]
|
|
r = img_size / max(h, w) # size ratio
|
|
if r < 1.0:
|
|
img = cv2.resize(img, (int(w * r), int(h * r)), interpolation=cv2.INTER_AREA) # _LINEAR fastest
|
|
cv2.imwrite(f.replace(path, path_new), img)
|
|
except:
|
|
print('WARNING: image failure %s' % f)
|
|
|
|
|
|
def convert_images2bmp():
|
|
# cv2.imread() jpg at 230 img/s, *.bmp at 400 img/s
|
|
for path in ['../coco/images/val2014/', '../coco/images/train2014/']:
|
|
folder = os.sep + Path(path).name
|
|
output = path.replace(folder, folder + 'bmp')
|
|
create_folder(output)
|
|
|
|
for f in tqdm(glob.glob('%s*.jpg' % path)):
|
|
save_name = f.replace('.jpg', '.bmp').replace(folder, folder + 'bmp')
|
|
cv2.imwrite(save_name, cv2.imread(f))
|
|
|
|
for label_path in ['../coco/trainvalno5k.txt', '../coco/5k.txt']:
|
|
with open(label_path, 'r') as file:
|
|
lines = file.read()
|
|
lines = lines.replace('2014/', '2014bmp/').replace('.jpg', '.bmp').replace(
|
|
'/Users/glennjocher/PycharmProjects/', '../')
|
|
with open(label_path.replace('5k', '5k_bmp'), 'w') as file:
|
|
file.write(lines)
|
|
|
|
|
|
def imagelist2folder(path='../data/sm3/out_test.txt'): # from utils.datasets import *; imagelist2folder()
|
|
# Copies all the images in a text file (list of images) into a folder
|
|
create_folder(path[:-4])
|
|
with open(path, 'r') as f:
|
|
for line in f.read().splitlines():
|
|
os.system('cp "%s" %s' % (line, path[:-4]))
|
|
print(line)
|
|
|
|
|
|
def create_folder(path='./new_folder'):
|
|
# Create folder
|
|
if os.path.exists(path):
|
|
shutil.rmtree(path) # delete output folder
|
|
os.makedirs(path) # make new output folder
|