def test_Yolov1Loss():
v1_loss = Yolov1Loss(weight_coord=1.,
weight_noobject=1.,
num_boxes=2,
num_classes=20,
grid_size=(7, 7))
print(v1_loss)
img_size, grid_size = (448, 448), (7, 7)
rs_ = Resize(size=img_size)
tt = ToTensor()
gco = ToGridCellOffset(img_size=img_size, grid_size=grid_size)
img_trans = Compose([rs_, tt])
box_trans = Compose([rs_, gco])
voc = VOCDataset(config,
phase='train',
img_transform=img_trans,
box_transform=box_trans)
img, boxes = voc[4]
# print(img.size(), type(boxes), boxes[0].size(), boxes[1].size())
img = img.unsqueeze(0)
grid = boxes[0].unsqueeze(0)
box = boxes[1].unsqueeze(0)
# print(img.size(), type(boxes), grid.size(), box.size())
net = Yolo(num_boxes=3, num_classes=20, grid_size=grid_size)
# net = net.cuda()
# img = img.cuda()
out = net(img)
# print(out.size())
# print(out.view(out.size(0), grid_size[0], grid_size[1], -1).size())
loss = v1_loss(out, (grid, box))
print(loss)
def test_transform():
rhf = RandomHorizontalFlip(p=0.5)
rc = RandomCrop(ratio=0.8)
cj = ColorJitter(brightness=0.4, saturation=0.4, hue=0.4)
rb = RandomBlur(p=0.5, r=(2, 3))
rs = RandomShift(p=0.5, ratio=0.1)
rs_ = Resize(size=(448, 448))
tt = ToTensor()
img_trans = Compose([rhf, rc, cj, rb, rs, rs_, tt])
box_trans = Compose([rhf, rc, rs, rs_, tt])
voc = VOCDataset(config,
phase='train',
img_transform=img_trans,
box_transform=box_trans)
img, boxes = voc[4]
print(img.size(), type(boxes), boxes, boxes.size())
img = img.permute(1, 2, 0)
img = cv2.cvtColor(np.array(img), cv2.COLOR_RGB2BGR)
print(img.shape)
img_h, img_w = img.shape[:2]
for box in list(boxes):
class_idx, cx, cy, w, h = int(box[0]), float(box[1]), float(
box[2]), float(box[3]), float(box[4])
cx, cy, w, h = cx * img_w, cy * img_h, w * img_w, h * img_h
pt1, pt2 = (int(cx - w / 2), int(cy - h / 2)), (int(cx + w / 2),
int(cy + h / 2))
cv2.rectangle(img, pt1, pt2, (0, 255, 0), 2)
cv2.imshow('transform', img)
cv2.waitKey(0)
def test_ColorJitter():
cj = ColorJitter(brightness=0.4, saturation=0.2, hue=0.2)
img_trans = Compose([cj])
voc = VOCDataset(config, phase='train', img_transform=img_trans)
img, boxes = voc[2]
img = cv2.cvtColor(np.array(img), cv2.COLOR_RGB2BGR)
for box in boxes:
pt1, pt2 = box.points()
cv2.rectangle(img, pt1, pt2, (0, 255, 0), 2)
cv2.imshow('transform', img)
cv2.waitKey(0)
def test_RandomBlur():
rb = RandomBlur(p=0.5, r=(2, 3))
img_trans = Compose([rb])
voc = VOCDataset(config, phase='train', img_transform=img_trans)
img, boxes = voc[2]
img = cv2.cvtColor(np.array(img), cv2.COLOR_RGB2BGR)
for box in boxes:
pt1, pt2 = box.points()
cv2.rectangle(img, pt1, pt2, (0, 255, 0), 2)
cv2.imshow('transform', img)
cv2.waitKey(0)
def test_RandomCrop():
rc = RandomCrop(ratio=0.7)
img_trans = Compose([rc])
box_trans = Compose([rc])
voc = VOCDataset(config,
phase='train',
img_transform=img_trans,
box_transform=box_trans)
img, boxes = voc[1]
img = cv2.cvtColor(np.array(img), cv2.COLOR_RGB2BGR)
for box in boxes:
pt1, pt2 = box.points()
cv2.rectangle(img, pt1, pt2, (0, 255, 0), 2)
cv2.imshow('transform', img)
cv2.waitKey(0)
def test_Resize():
resize = Resize(size=(448, 448))
img_trans = Compose([resize])
box_trans = Compose([resize])
voc = VOCDataset(config,
phase='train',
img_transform=img_trans,
box_transform=box_trans)
img, boxes = voc[2]
img = cv2.cvtColor(np.array(img), cv2.COLOR_RGB2BGR)
print(img.shape)
for box in boxes:
pt1, pt2 = box.points()
cv2.rectangle(img, pt1, pt2, (0, 255, 0), 2)
cv2.imshow('transform', img)
cv2.waitKey(0)
def test_ToGridCellOffset():
rs_ = Resize(size=(448, 448))
tt = ToTensor()
gco = ToGridCellOffset(img_size=(448, 448), grid_size=(7, 7))
img_trans = Compose([rs_, tt])
box_trans = Compose([rs_, gco])
voc = VOCDataset(config,
phase='train',
img_transform=img_trans,
box_transform=box_trans)
img, boxes = voc[4]
print(img.size(), type(boxes), len(boxes))
print(boxes[0].size(), boxes[1].size())
print(boxes[0])
print(boxes[1])
img = img.permute(1, 2, 0)
img = cv2.cvtColor(np.array(img), cv2.COLOR_RGB2BGR)
for box in boxes[1]:
i, j, _, o_x, o_y, w, h = int(box[0]), int(box[1]), int(box[2]), float(
box[3]), float(box[4]), float(box[5]), float(box[6])
w, h = w * 448, h * 448
x, y = (i + o_x) * (448 / 7.) - w / 2, (j + o_y) * (448 / 7.) - h / 2
pt1 = (int(x), int(y))
pt2 = (int(x + w), int(y + h))
cv2.rectangle(img, pt1, pt2, (0, 255, 0), 2)
# Center point
cv2.line(img, (int(x + w / 2), int(y + h / 2)),
(int(x + w / 2), int(y + h / 2)), (255, 0, 0), 10)
# Draw grid
inter_ = 448 // 7
for i in range(7):
cv2.line(img, (0, inter_ * i), (448, inter_ * i), (0, 0, 255), 2)
for j in range(7):
cv2.line(img, (inter_ * j, 0), (inter_ * j, 448), (0, 0, 225), 2)
cv2.imshow('transform', img)
cv2.waitKey(0)
def test_ToTensor():
toTensor = ToTensor()
img_trans = Compose([toTensor])
box_trans = Compose([toTensor])
voc = VOCDataset(config,
phase='train',
img_transform=img_trans,
box_transform=box_trans)
img, boxes = voc[4]
print(img.size(), type(boxes), boxes, boxes.size())
img = img.permute(1, 2, 0)
img = cv2.cvtColor(np.array(img), cv2.COLOR_RGB2BGR)
print(img.shape)
img_h, img_w = img.shape[:2]
for box in list(boxes):
class_idx, cx, cy, w, h = int(box[0]), float(box[1]), float(
box[2]), float(box[3]), float(box[4])
cx, cy, w, h = cx * img_w, cy * img_h, w * img_w, h * img_h
pt1, pt2 = (int(cx - w / 2), int(cy - h / 2)), (int(cx + w / 2),
int(cy + h / 2))
cv2.rectangle(img, pt1, pt2, (0, 255, 0), 2)
cv2.imshow('transform', img)
cv2.waitKey(0)
def test_VOCDatest():
voc = VOCDataset(config, phase='train')
img, boxes = voc[0]
print(img.size, boxes)