a_image_path = os.path.join(im_dir, a_subdir, a_image_name)
print(a_image_path)
a_image = cv2.imread(a_image_path)
#-----count the number of images----
idx += 1
if idx % 100 == 0:
print(idx, "images done")
#----------------------------------
neg_num = 0
while neg_num < 100:
a_image_copy = a_image.copy()
bboxes_copy = bboxes.copy()
select_angle = np.random.choice(angle_vecs)
a_image_copy, bboxes_copy = rotate_images(a_image_copy, bboxes_copy,
select_angle)
height, width, channel = a_image_copy.shape
size = npr.randint(40, min(width, height) / 2)
nx = npr.randint(0, width - size)
ny = npr.randint(0, height - size)
crop_box = np.array([nx, ny, nx + size, ny + size])
Iou = IoU(crop_box, bboxes_copy)
cropped_im = a_image_copy[ny:ny + size, nx:nx + size, :].copy()
resized_im = cv2.resize(cropped_im, (IMAGE_SIZE, IMAGE_SIZE),
interpolation=cv2.INTER_LINEAR)
if np.max(Iou) < 0.3:
# Iou with all gts must below 0.3
print(idx, "images done")
#----------------------------------
#if DEBUG:
# for a_box in bboxes:
# x1, y1, w, h = a_box
# x2 = x1 + w - 1
# y2 = y1 + h - 1
# x1, y1, x2, y2 = int(x1), int(y1), int(x2), int(y2)
# cv2.rectangle(a_image, (x1, y1), (x2, y2), (0, 255, 0), 1)
#
# a_target_image_path = os.path.join(target_image_dir, a_image_name)
# cv2.imwrite(a_target_image_path, a_image)
select_angle = np.random.choice(angle_vecs)
a_image, bboxes = rotate_images(a_image, bboxes, select_angle)
select_angle = 0
height, width, channel = a_image.shape
if DEBUG:
a_image_copy = a_image.copy()
for a_box in bboxes:
x1, y1, x2, y2 = a_box
x1, y1, x2, y2 = int(x1), int(y1), int(x2), int(y2)
cv2.rectangle(a_image_copy, (x1, y1), (x2, y2), (0, 255, 0), 1)
a_target_image_path = os.path.join(target_image_dir, a_image_name)
cv2.imwrite(a_target_image_path, a_image_copy)
if select_angle in face_up: #----faceing up or down
face_up_down_label = 1
elif select_angle in face_down:
plt.subplot(222)
plt.title('label')
io.imshow(label)
plt.subplot(223)
plt.title('invert')
io.imshow(img_tf)
plt.subplot(224)
plt.title('invert')
io.imshow(label_tf)
if 0:
ix = 367
angle = 270
img = X_train[ix]
label = np.squeeze(Y_train[ix])
X_rot, Y_rot = rotate_images(X_train, Y_train, angle)
img_tf = X_rot[ix]
label_tf = np.squeeze(Y_rot[ix])
plt.figure(figsize=(8, 8))
plt.subplot(221)
plt.title('image')
io.imshow(img)
plt.subplot(222)
plt.title('label')
io.imshow(label)
plt.subplot(223)
plt.title('rotate')
io.imshow(img_tf)
plt.subplot(224)
plt.title('rotate')
io.imshow(label_tf)
