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experiment.py
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import argparse
import numpy as np
import torch
import random
import cv2
import math
import time
import matplotlib.pyplot as plt
import cv2
import torchvision
from roi_align import RoIAlign
from network.seghead import SegHead
from network.GeneralizedRCNN import GeneralizedRCNN
from network.YoLoLayer import YOLOLayer
from network.Darknet import Darknet,parse_model_cfg
def track(opt):
print(opt.cfg)
def letterbox(img, height=608, width=1088, color=(127.5, 127.5, 127.5)): # resize a rectangular image to a padded rectangular
shape = img.shape[:2] # shape = [height, width]
ratio = min(float(height)/shape[0], float(width)/shape[1])
new_shape = (round(shape[1] * ratio), round(shape[0] * ratio)) # new_shape = [width, height]
dw = (width - new_shape[0]) / 2 # width padding
dh = (height - new_shape[1]) / 2 # height padding
top, bottom = round(dh - 0.1), round(dh + 0.1)
left, right = round(dw - 0.1), round(dw + 0.1)
img = cv2.resize(img, new_shape, interpolation=cv2.INTER_AREA) # resized, no border
img = cv2.copyMakeBorder(img, top, bottom, left, right, cv2.BORDER_CONSTANT, value=color) # padded rectangular
return img, ratio, dw, dh
def get_data(img_path,arguement):
height = 1024
width = 2048
img = cv2.imread(img_path) # BGR
plt.imshow(img)
plt.show()
if img is None:
raise ValueError('File corrupt {}'.format(img_path))
augment_hsv = True
if arguement and augment_hsv:
# SV augmentation by 50%
fraction = 0.50
img_hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
S = img_hsv[:, :, 1].astype(np.float32)
V = img_hsv[:, :, 2].astype(np.float32)
a = (random.random() * 2 - 1) * fraction + 1
S *= a
if a > 1:
np.clip(S, a_min=0, a_max=255, out=S)
a = (random.random() * 2 - 1) * fraction + 1
V *= a
if a > 1:
np.clip(V, a_min=0, a_max=255, out=V)
img_hsv[:, :, 1] = S.astype(np.uint8)
img_hsv[:, :, 2] = V.astype(np.uint8)
cv2.cvtColor(img_hsv, cv2.COLOR_HSV2BGR, dst=img)
h, w, _ = img.shape
img, ratio, padw, padh = letterbox(img, height=height, width=width)
# Augment image and labels
if arguement:
img = random_affine(img,degrees=(-5, 5), translate=(0.10, 0.10), scale=(0.50, 1.20))
img = np.ascontiguousarray(img[:, :, ::-1]) # BGR to RGB
# if self.transforms is not None:
# img = self.transforms(img)
return img, img_path, (h, w)
def random_affine(img, targets=None, degrees=(-10, 10), translate=(.1, .1), scale=(.9, 1.1), shear=(-2, 2),
borderValue=(127.5, 127.5, 127.5)):
# 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
border = 0 # width of added border (optional)
height = img.shape[0]
width = img.shape[1]
# Rotation and Scale
R = np.eye(3)
a = random.random() * (degrees[1] - degrees[0]) + degrees[0]
# a += random.choice([-180, -90, 0, 90]) # 90deg rotations added to small rotations
s = random.random() * (scale[1] - scale[0]) + scale[0]
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.random() * 2 - 1) * translate[0] * img.shape[0] + border # x translation (pixels)
T[1, 2] = (random.random() * 2 - 1) * translate[1] * img.shape[1] + border # y translation (pixels)
# Shear
S = np.eye(3)
S[0, 1] = math.tan((random.random() * (shear[1] - shear[0]) + shear[0]) * math.pi / 180) # x shear (deg)
S[1, 0] = math.tan((random.random() * (shear[1] - shear[0]) + shear[0]) * math.pi / 180) # y shear (deg)
M = S @ T @ R # Combined rotation matrix. ORDER IS IMPORTANT HERE!!
imw = cv2.warpPerspective(img, M, dsize=(width, height), flags=cv2.INTER_LINEAR,
borderValue=borderValue) # BGR order borderValue
# Return warped points also
if targets is not None:
if len(targets) > 0:
n = targets.shape[0]
points = targets[:, 2:6].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
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
np.clip(xy[:, 0], 0, width, out=xy[:, 0])
np.clip(xy[:, 2], 0, width, out=xy[:, 2])
np.clip(xy[:, 1], 0, height, out=xy[:, 1])
np.clip(xy[:, 3], 0, height, out=xy[:, 3])
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[:, 2:6] = xy[i]
return imw, targets, M
else:
return imw
def format_box(bbox,fw,fh):
return torch.Tensor([[bbox[0] / 1024 * fh,
bbox[1] / 2048 * fw,
(bbox[2] + bbox[0]) / 1024 * fh,
(bbox[3]+bbox[1]) / 2048 * fw]])
if __name__ == '__main__':
# img,a,b = get_data(r'E:\Challenge\MOTSChallenge\train\images\0002\000001.jpg',False)
# img = cv2.imread(r'E:\Challenge\MOTSChallenge\train\images\0002\000001.jpg')
# print(img.shape)
# img=cv2.resize(img,(2048,1024)) #修改图片的尺寸
# # img, _, _, _ = letterbox(img, height=1024, width=2048)
# img_hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
cfg ='cfg/yolov3_1088x608.cfg'
cfg_dict = parse_model_cfg(cfg)
img1=608
img2=1088
x = torch.randn(1,3,img1,img2).cuda()
net = Darknet(cfg_dict,548).cuda()
targets = torch.ones(1,9,6).cuda()
targetslen = torch.Tensor([[9,]]).cuda()
loss, components ,featuremap = net(x,targets,targetslen)
for f in featuremap:
print(f.shape)
# seg = SegHead([2048, 1024]).cuda()
# backbone.load_state_dict(
# torch.load(os.path.join(save_dir, BackBoneName + '_epoch-' + str(999) + '.pth'),
# map_location=lambda storage, loc: storage))
# seghead.load_state_dict(
# torch.load(os.path.join(save_dir, SegHeadName + '_epoch-' + str(999) + '.pth'),
# map_location=lambda storage, loc: storage))