def _get_image_blob(roidb, scale_inds):
"""Builds an input blob from the images in the roidb at the specified
scales.
"""
num_images = len(roidb)
processed_ims = []
im_scales = []
for i in range(num_images):
#im = cv2.imread(roidb[i]['image'])
im = imread(roidb[i]['image'])
if len(im.shape) == 2:
im = im[:, :, np.newaxis]
im = np.concatenate((im, im, im), axis=2)
# flip the channel, since the original one using cv2
# rgb -> bgr
im = im[:, :, ::-1]
if roidb[i]['flipped']:
im = im[:, ::-1, :]
target_size = cfg.TRAIN.SCALES[scale_inds[i]]
im, im_scale = prep_im_for_blob(im, cfg.PIXEL_MEANS, target_size,
cfg.TRAIN.MAX_SIZE)
im_scales.append(im_scale)
processed_ims.append(im)
# Create a blob to hold the input images
blob = im_list_to_blob(processed_ims)
return blob, im_scales
def roi_data(image, target_size=240):
""" Prepare the input of faster rcnn for detecting objects """
# flip the channel, since the original one using cv2
# rgb -> bgr
# image = image[:, :, ::-1]
# Pixel mean values (BGR order) as a (1, 1, 3) array
# We use the same pixel mean for all networks even though it's not exactly what
# they were trained with
pixel_means = np.array([[[102.9801, 115.9465, 122.7717]]])
image, im_scale = prep_im_for_blob(im=image,
pixel_means=pixel_means,
target_size=target_size,
max_size=0,
normalize=None)
im_info = np.array([image.shape[0], image.shape[1], im_scale],
dtype=np.float32)
# numpy to tensor
image = torch.Tensor(image.astype(np.float32))
image = image.permute(2, 0, 1)
im_info = torch.Tensor(im_info)
gt_boxes = torch.ones(5, dtype=torch.float32)
num_boxes = torch.LongTensor([0])
image.requires_grad = False
im_info.requires_grad = False
gt_boxes.requires_grad = False
num_boxes.requires_grad = False
return image, im_info, gt_boxes, num_boxes
def _get_image_blob(roidb, scale_inds):
"""Builds an input blob from the images in the roidb at the specified
scales.
"""
num_images = len(roidb) # num_images = 1
processed_ims = []
im_scales = []
for i in range(num_images):
im = imread(roidb[i]['image'])
if len(im.shape) == 2:
im = im[:, :, np.newaxis]
im = np.concatenate((im, im, im), axis=2)
# flip the channel, since the original one using cv2
# rgb -> bgr
im = im[:, :, ::-1]
if roidb[i]['flipped']:
im = im[:, ::-1, :] # 对图像进行水平翻转
target_size = cfg.TRAIN.SCALES[scale_inds[i]]
im, im_scale = prep_im_for_blob(im, cfg.PIXEL_MEANS, target_size,
cfg.TRAIN.MAX_SIZE)
# im_scale = (target_size) / float(im_size_min)，表示原始图像的短边到训练尺寸600的变换倍数
im_scales.append(im_scale)
processed_ims.append(im)
# Create a blob to hold the input images
blob = im_list_to_blob(processed_ims)
# 返回blob形式[1,w,h,c]，im_scales表示图像resize的倍数
return blob, im_scales
def get_evaluate_batch(self, im_path, index):
# Sample random scales to use for each image in this batch
# Get the input image blob, formatted for caffe
# im_blob, im_scales = _get_image_blob(roidb, random_scale_inds)
im = imread(im_path)
if len(im.shape) == 2:
im = im[:, :, np.newaxis]
im = np.concatenate((im, im, im), axis=2)
# flip the channel, since the original one using cv2
# rgb -> bgr
im = im[:, :, ::-1]
target_size = cfg.TRAIN.SCALES[0]
im, im_scale = prep_im_for_blob(im, cfg.PIXEL_MEANS, target_size,
cfg.TRAIN.MAX_SIZE)
im_blob = im_list_to_blob([im])
blobs = {'data': im_blob}
# gt boxes: (x1, y1, x2, y2, cls)
gt_boxes = np.empty((0, 5), dtype=np.float32)
blobs['gt_boxes'] = gt_boxes
blobs['im_info'] = np.array([[im.shape[0], im.shape[1], im_scale]], dtype=np.float32)
blobs['img_id'] = index
return blobs
def prepare_im_func(prefix, random_idx, frame_idx, flipped):
frame_path = os.path.join(prefix,
'image_' + str(frame_idx).zfill(5) + '.jpg')
frame = cv2.imread(frame_path)
# process the boundary frame
if frame is None:
frames = sorted(os.listdir(prefix))
frame_path = frame_path = os.path.join(prefix, frames[-1])
frame = cv2.imread(frame_path)
frame = prep_im_for_blob(frame, cfg.PIXEL_MEANS,
tuple(cfg.TRAIN.FRAME_SIZE[::-1]),
cfg.TRAIN.CROP_SIZE, random_idx)
if flipped:
frame = frame[:, ::-1, :]
if DEBUG:
cv2.imshow('frame', frame / 255.0)
cv2.waitKey(0)
cv2.destroyAllWindows()
return frame
def _get_video_blob(
roidb,
scale_inds,
phase='train'
): # ([{'gt_classes': array([18.]), 'bg_name':... }], [0], 'train')
"""Builds an input blob from the videos in the roidb at the specified
scales.
"""
processed_videos = []
for i, item in enumerate(
roidb
): # i= 0,item的shape： {'gt_classes': array([18.]), 'bg_name':... }
# just one scale implementated
video_length = cfg.TRAIN.LENGTH[scale_inds[0]] # video_length = 512
video = np.zeros((
video_length,
cfg.TRAIN.CROP_SIZE, # (512, 112, 112, 3)
cfg.TRAIN.CROP_SIZE,
3))
j = 0
if phase == 'train':
random_idx = [
np.random.randint(
cfg.TRAIN.FRAME_SIZE[1] - cfg.TRAIN.CROP_SIZE
), # [np.random.randint(59), np.random.randint(16)]
np.random.randint(cfg.TRAIN.FRAME_SIZE[0] -
cfg.TRAIN.CROP_SIZE)
]
# TODO: data argumentation
#image_w, image_h, crop_w, crop_h = cfg.TRAIN.FRAME_SIZE[1], cfg.TRAIN.FRAME_SIZE[0], cfg.TRAIN.CROP_SIZE, cfg.TRAIN.CROP_SIZE
#offsets = GroupMultiScaleCrop.fill_fix_offset(False, image_w, image_h, crop_w, crop_h)
#random_idx = offsets[ npr.choice(len(offsets)) ]
else:
random_idx = [
int((cfg.TRAIN.FRAME_SIZE[1] - cfg.TRAIN.CROP_SIZE) / 2),
int((cfg.TRAIN.FRAME_SIZE[0] - cfg.TRAIN.CROP_SIZE) / 2)
]
if DEBUG:
print("offsets: {}, random_idx: {}".format(offsets, random_idx))
video_info = item['frames'][
0] # item['frames'][0]的shape:[0,1317,2085,1]
step = video_info[3] if cfg.INPUT == 'video' else 1 # step = 1
prefix = item['fg_name'] if video_info[0] else item[
'bg_name'] # 视频帧文件夹的绝对路径
if cfg.TEMP_SPARSE_SAMPLING:
if phase == 'train':
segment_offsets = npr.randint(
step, size=len(range(video_info[1], video_info[2], step)))
else:
segment_offsets = np.zeros(
len(range(video_info[1], video_info[2], step))) + step // 2
else: # 走这条路
segment_offsets = np.zeros(
len(range(video_info[1], video_info[2], step))) # 时序片段的帧长度
for i, idx in enumerate(range(video_info[1], video_info[2],
step)): # idx为该时序片段内的所有帧的下标
frame_idx = int(segment_offsets[i] + idx +
1) # frame_idx为该时序片段内的所有帧的下标+1
frame_path = os.path.join(prefix,
'image_' + str(frame_idx).zfill(5) +
'.jpg') # frame_path为该时序片段内的所有帧的绝对路径
frame = cv2.imread(frame_path) # 读取该路径下帧的图片
# process the boundary frame
if frame is None: # 若该时序片段下的帧为空，则读取该视频帧文件夹的最后一帧作为frame
frames = sorted(os.listdir(
prefix)) # frames为帧文件夹下所有帧图片名字构成的列表,且列表内的图片名字的数字由小到大排列
frame_path = frame_path = os.path.join(prefix, frames[-1])
frame = cv2.imread(frame_path)
frame = prep_im_for_blob(frame, cfg.PIXEL_MEANS,
tuple(cfg.TRAIN.FRAME_SIZE[::-1]),
cfg.TRAIN.CROP_SIZE, random_idx)
if item['flipped']: # 不走这条路(flipped=False)
frame = frame[:, ::-1, :]
if DEBUG: # 不走这条路(DEBUG=False)
cv2.imshow('frame', frame / 255.0)
cv2.waitKey(0)
cv2.destroyAllWindows()
video[
j] = frame # 把每一个时序片段的所有帧重新装入video这个列表里,video形状:[video_info[2]-video_info[1], 112, 112, 3]
j = j + 1
video[j:video_length] = video[
j -
1] # 若video长度不足512,则把video的最后一帧重复填充直至第512帧,最终video形状:[512, 112, 112, 3]
processed_videos.append(
video
) # 把每个video合在一起(可能是形成一个batch)(但实际上这级的for循环只会执行一次?所以batch_size在这里=1?) processed_videos的shape：[batch_size, 512, 112, 112, 3]
# Create a blob to hold the input images, dimension trans CLHW
blob = video_list_to_blob(
processed_videos) # blob的shape：[batch_size, 3, 512, 112, 112]
return blob # blob的shape：[batch_size, 3, 512, 112, 112]
