551 lines
22 KiB
Python
Executable File
551 lines
22 KiB
Python
Executable File
import glob
|
|
import math
|
|
import os
|
|
import random
|
|
import shutil
|
|
from pathlib import Path
|
|
|
|
import cv2
|
|
import numpy as np
|
|
import torch
|
|
from PIL import Image, ExifTags
|
|
from torch.utils.data import Dataset
|
|
from tqdm import tqdm
|
|
|
|
from utils.utils import xyxy2xywh, xywh2xyxy
|
|
|
|
img_formats = ['.bmp', '.jpg', '.jpeg', '.png', '.tif']
|
|
vid_formats = ['.mov', '.avi', '.mp4']
|
|
|
|
# Get orientation exif tag
|
|
for orientation in ExifTags.TAGS.keys():
|
|
if ExifTags.TAGS[orientation] == 'Orientation':
|
|
break
|
|
|
|
|
|
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:
|
|
None
|
|
|
|
return s
|
|
|
|
|
|
class LoadImages: # for inference
|
|
def __init__(self, path, img_size=416, half=False):
|
|
path = str(Path(path)) # os-agnostic
|
|
files = []
|
|
if os.path.isdir(path):
|
|
files = sorted(glob.glob(os.path.join(path, '*.*')))
|
|
elif os.path.isfile(path):
|
|
files = [path]
|
|
|
|
images = [x for x in files if os.path.splitext(x)[-1].lower() in img_formats]
|
|
videos = [x for x in files if os.path.splitext(x)[-1].lower() in vid_formats]
|
|
nI, nV = len(images), len(videos)
|
|
|
|
self.img_size = img_size
|
|
self.files = images + videos
|
|
self.nF = nI + nV # number of files
|
|
self.video_flag = [False] * nI + [True] * nV
|
|
self.mode = 'images'
|
|
self.half = half # half precision fp16 images
|
|
if any(videos):
|
|
self.new_video(videos[0]) # new video
|
|
else:
|
|
self.cap = None
|
|
assert self.nF > 0, 'No images or videos found in ' + path
|
|
|
|
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('video %g/%g (%g/%g) %s: ' % (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('image %g/%g %s: ' % (self.count, self.nF, path), end='')
|
|
|
|
# Padded resize
|
|
img, *_ = letterbox(img0, new_shape=self.img_size)
|
|
|
|
# Normalize RGB
|
|
img = img[:, :, ::-1].transpose(2, 0, 1) # BGR to RGB
|
|
img = np.ascontiguousarray(img, dtype=np.float16 if self.half else np.float32) # uint8 to fp16/fp32
|
|
img /= 255.0 # 0 - 255 to 0.0 - 1.0
|
|
|
|
# cv2.imwrite(path + '.letterbox.jpg', 255 * img.transpose((1, 2, 0))[:, :, ::-1]) # save letterbox image
|
|
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, img_size=416, half=False):
|
|
self.img_size = img_size
|
|
self.half = half # half precision fp16 images
|
|
|
|
pipe = 0 # 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 = 'rtsp://170.93.143.139/rtplive/470011e600ef003a004ee33696235daa' # IP traffic camera
|
|
# pipe = 'http://wmccpinetop.axiscam.net/mjpg/video.mjpg' # IP golf camera
|
|
|
|
# https://answers.opencv.org/question/215996/changing-gstreamer-pipeline-to-opencv-in-pythonsolved/
|
|
# pipe = '"rtspsrc location="rtsp://username:password@192.168.1.64/1" latency=10 ! appsink' # GStreamer
|
|
|
|
# https://answers.opencv.org/question/200787/video-acceleration-gstremer-pipeline-in-videocapture/
|
|
# https://stackoverflow.com/questions/54095699/install-gstreamer-support-for-opencv-python-package # install help
|
|
# 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
|
|
|
|
self.pipe = pipe
|
|
self.cap = cv2.VideoCapture(pipe) # video capture object
|
|
|
|
def __iter__(self):
|
|
self.count = -1
|
|
return self
|
|
|
|
def __next__(self):
|
|
self.count += 1
|
|
if cv2.waitKey(1) == 27: # esc to quit
|
|
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, 'Webcam Error'
|
|
img_path = 'webcam_%g.jpg' % self.count
|
|
print('webcam %g: ' % self.count, end='')
|
|
|
|
# Padded resize
|
|
img, *_ = letterbox(img0, new_shape=self.img_size)
|
|
|
|
# Normalize RGB
|
|
img = img[:, :, ::-1].transpose(2, 0, 1) # BGR to RGB
|
|
img = np.ascontiguousarray(img, dtype=np.float16 if self.half else np.float32) # uint8 to fp16/fp32
|
|
img /= 255.0 # 0 - 255 to 0.0 - 1.0
|
|
|
|
return img_path, img, img0, None
|
|
|
|
def __len__(self):
|
|
return 0
|
|
|
|
|
|
class LoadImagesAndLabels(Dataset): # for training/testing
|
|
def __init__(self, path, img_size=416, batch_size=16, augment=False, hyp=None, rect=True, image_weights=False,
|
|
cache_images=False):
|
|
path = str(Path(path)) # os-agnostic
|
|
with open(path, 'r') as f:
|
|
self.img_files = [x.replace('/', os.sep) for x in f.read().splitlines() # os-agnostic
|
|
if os.path.splitext(x)[-1].lower() in img_formats]
|
|
|
|
n = len(self.img_files)
|
|
bi = np.floor(np.arange(n) / batch_size).astype(np.int) # batch index
|
|
nb = bi[-1] + 1 # number of batches
|
|
assert n > 0, 'No images found in %s' % path
|
|
|
|
self.n = n
|
|
self.batch = bi # batch index of image
|
|
self.img_size = img_size
|
|
self.augment = augment
|
|
self.hyp = hyp
|
|
self.image_weights = image_weights
|
|
self.rect = False if image_weights else rect
|
|
|
|
# Define labels
|
|
self.label_files = [x.replace('images', 'labels').replace(os.path.splitext(x)[-1], '.txt')
|
|
for x in self.img_files]
|
|
|
|
# Rectangular Training https://github.com/ultralytics/yolov3/issues/232
|
|
if self.rect:
|
|
# Read image shapes
|
|
sp = 'data' + os.sep + path.replace('.txt', '.shapes').split(os.sep)[-1] # shapefile path
|
|
try:
|
|
with open(sp, 'r') as f: # read existing shapefile
|
|
s = [x.split() for x in f.read().splitlines()]
|
|
assert len(s) == n, 'Shapefile out of sync'
|
|
except:
|
|
s = [exif_size(Image.open(f)) for f in tqdm(self.img_files, desc='Reading image shapes')]
|
|
np.savetxt(sp, s, fmt='%g') # overwrites existing (if any)
|
|
|
|
# Sort by aspect ratio
|
|
s = np.array(s, dtype=np.float64)
|
|
ar = s[:, 1] / s[:, 0] # aspect ratio
|
|
i = ar.argsort()
|
|
self.img_files = [self.img_files[i] for i in i]
|
|
self.label_files = [self.label_files[i] for i in i]
|
|
self.shapes = s[i]
|
|
ar = ar[i]
|
|
|
|
# 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 / 32.).astype(np.int) * 32
|
|
|
|
# Preload labels (required for weighted CE training)
|
|
self.imgs = [None] * n
|
|
self.labels = [None] * n
|
|
if augment or image_weights: # cache labels for faster training
|
|
self.labels = [np.zeros((0, 5))] * n
|
|
extract_bounding_boxes = False
|
|
pbar = tqdm(self.label_files, desc='Reading labels')
|
|
nm, nf, ne = 0, 0, 0 # number missing, number found, number empty
|
|
for i, file in enumerate(pbar):
|
|
try:
|
|
with open(file, 'r') as f:
|
|
l = np.array([x.split() for x in f.read().splitlines()], dtype=np.float32)
|
|
except:
|
|
nm += 1 # print('missing labels for image %s' % self.img_files[i]) # file missing
|
|
continue
|
|
|
|
if l.shape[0]:
|
|
assert l.shape[1] == 5, '> 5 label columns: %s' % file
|
|
assert (l >= 0).all(), 'negative labels: %s' % file
|
|
assert (l[:, 1:] <= 1).all(), 'non-normalized or out of bounds coordinate labels: %s' % file
|
|
self.labels[i] = l
|
|
nf += 1 # file found
|
|
|
|
# Extract object detection boxes for a second stage classifier
|
|
if extract_bounding_boxes:
|
|
p = Path(self.img_files[i])
|
|
img = cv2.imread(str(p))
|
|
h, w, _ = img.shape
|
|
for j, x in enumerate(l):
|
|
f = '%s%sclassifier%s%g_%g_%s' % (p.parent.parent, os.sep, os.sep, x[0], j, p.name)
|
|
if not os.path.exists(Path(f).parent):
|
|
os.makedirs(Path(f).parent) # make new output folder
|
|
box = xywh2xyxy(x[1:].reshape(-1, 4)).ravel()
|
|
b = np.clip(box, 0, 1) # clip boxes outside of image
|
|
ret_val = cv2.imwrite(f, img[int(b[1] * h):int(b[3] * h), int(b[0] * w):int(b[2] * w)])
|
|
assert ret_val, 'Failure extracting classifier boxes'
|
|
else:
|
|
ne += 1 # file empty
|
|
|
|
pbar.desc = 'Reading labels (%g found, %g missing, %g empty for %g images)' % (nf, nm, ne, n)
|
|
assert nf > 0, 'No labels found. Recommend correcting image and label paths.'
|
|
|
|
# Cache images into memory for faster training (~5GB)
|
|
if cache_images and augment: # if training
|
|
for i in tqdm(range(min(len(self.img_files), 10000)), desc='Reading images'): # max 10k images
|
|
img_path = self.img_files[i]
|
|
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 and r < 1: # if training (NOT testing), downsize to inference shape
|
|
h, w, _ = img.shape
|
|
img = cv2.resize(img, (int(w * r), int(h * r)), interpolation=cv2.INTER_LINEAR) # or INTER_AREA
|
|
self.imgs[i] = img
|
|
|
|
# Detect corrupted images https://medium.com/joelthchao/programmatically-detect-corrupted-image-8c1b2006c3d3
|
|
detect_corrupted_images = False
|
|
if detect_corrupted_images:
|
|
from skimage import io # conda install -c conda-forge scikit-image
|
|
for file in tqdm(self.img_files, desc='Detecting corrupted images'):
|
|
try:
|
|
_ = io.imread(file)
|
|
except:
|
|
print('Corrupted image detected: %s' % file)
|
|
|
|
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):
|
|
if self.image_weights:
|
|
index = self.indices[index]
|
|
|
|
img_path = self.img_files[index]
|
|
label_path = self.label_files[index]
|
|
hyp = self.hyp
|
|
|
|
# Load image
|
|
img = self.imgs[index]
|
|
if img is None:
|
|
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 and r < 1: # if training (NOT testing), downsize to inference shape
|
|
h, w, _ = img.shape
|
|
img = cv2.resize(img, (int(w * r), int(h * r)), interpolation=cv2.INTER_LINEAR) # INTER_LINEAR fastest
|
|
|
|
# Augment colorspace
|
|
augment_hsv = True
|
|
if self.augment and augment_hsv:
|
|
# 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) * hyp['hsv_s'] + 1
|
|
b = random.uniform(-1, 1) * hyp['hsv_v'] + 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)
|
|
|
|
# Letterbox
|
|
h, w, _ = img.shape
|
|
if self.rect:
|
|
shape = self.batch_shapes[self.batch[index]]
|
|
img, ratiow, ratioh, padw, padh = letterbox(img, new_shape=shape, mode='rect')
|
|
else:
|
|
shape = self.img_size
|
|
img, ratiow, ratioh, padw, padh = letterbox(img, new_shape=shape, mode='square')
|
|
|
|
# Load labels
|
|
labels = []
|
|
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] = ratiow * w * (x[:, 1] - x[:, 3] / 2) + padw
|
|
labels[:, 2] = ratioh * h * (x[:, 2] - x[:, 4] / 2) + padh
|
|
labels[:, 3] = ratiow * w * (x[:, 1] + x[:, 3] / 2) + padw
|
|
labels[:, 4] = ratioh * h * (x[:, 2] + x[:, 4] / 2) + padh
|
|
|
|
# Augment image and labels
|
|
if self.augment:
|
|
img, labels = random_affine(img, labels,
|
|
degrees=hyp['degrees'],
|
|
translate=hyp['translate'],
|
|
scale=hyp['scale'],
|
|
shear=hyp['shear'])
|
|
|
|
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 letterbox(img, new_shape=416, color=(128, 128, 128), mode='auto'):
|
|
# 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):
|
|
ratio = float(new_shape) / max(shape)
|
|
else:
|
|
ratio = max(new_shape) / max(shape) # ratio = new / old
|
|
ratiow, ratioh = ratio, ratio
|
|
new_unpad = (int(round(shape[1] * ratio)), int(round(shape[0] * ratio)))
|
|
|
|
# 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)
|
|
ratiow, ratioh = new_shape / shape[1], new_shape / shape[0]
|
|
|
|
if shape[::-1] != new_unpad: # resize
|
|
img = cv2.resize(img, new_unpad, interpolation=cv2.INTER_AREA) # 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, ratiow, ratioh, 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 = []
|
|
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)
|
|
|
|
M = S @ T @ R # Combined rotation matrix. ORDER IS IMPORTANT HERE!!
|
|
imw = cv2.warpAffine(img, M[:2], dsize=(width, height), flags=cv2.INTER_AREA,
|
|
borderValue=(128, 128, 128)) # BGR order borderValue
|
|
|
|
# Return warped points also
|
|
if len(targets) > 0:
|
|
n = targets.shape[0]
|
|
points = targets[:, 1:5].copy()
|
|
area0 = (points[:, 2] - points[:, 0]) * (points[:, 3] - points[:, 1])
|
|
|
|
# warp points
|
|
xy = np.ones((n * 4, 3))
|
|
xy[:, :2] = points[:, [0, 1, 2, 3, 0, 3, 2, 1]].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
|
|
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 imw, targets
|
|
|
|
|
|
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')
|
|
if os.path.exists(output):
|
|
shutil.rmtree(output) # delete output folder
|
|
os.makedirs(output) # make new output folder
|
|
|
|
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)
|