将视频按照一定时间截取图片并且保存，或者说隔几帧保存一下一幅图像。
import os
import subprocess
def convert_video_images(source_path, output_path):
fileTypes = ['.avi', '.mp4', '.flv', '.mov', '.mkv']
if not os.path.exists(source_path):
print("not exit sourcepath :%s" % (source_path))
return
if not os.path.exists(output_path):
os.mkdir(output_path)
for video_file in os.listdir(source_path):
print(video_file)
for video_file in os.listdir(source_path):
video_name = os.path.join(source_path, video_file)
if os.path.splitext(video_name)[1] not in fileTypes:
print("skip %s" % video_file)
continue
if video_name == ".DS_Store":
print(video_name)
continue
print("video name:", video_name)
strcmd = 'ffmpeg -i ' + video_name + ' -vf "scale=1920:1080,fps=1/5" ' + output_path + '/' + \
os.path.splitext(video_file)[0] + '%5d.png'
print(strcmd)
subprocess.call(strcmd, shell=True)
print('the video has generated image')
此方法需要安装FFmpeg，安装步骤网上有很多，不在介绍。代码中的fps=1/5，意味着5秒取一帧。
对视频依次取出每一帧，然后对该帧处理后在合成新的视频
i = 1
cap = cv2.VideoCapture('输入文件路径.mp4')
fps = cap.get(cv2.CAP_PROP_FPS)
print(fps)
size = (int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)),
int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)))
print(size)
fourcc = cv2.VideoWriter_fourcc('M', 'P', '4', '2')
outVideo = cv2.VideoWriter('输出文件路径', fourcc, fps, size)
if cap.isOpened():
rval, frame = cap.read()
print('ture')
else:
rval = False
print('False')
while rval:
rval, frame = cap.read()
#对每一帧进行处理后，产生新一帧：my_image
my_image = my_demo(sess, net, frame)
cv2.imshow('test', my_image)
outVideo.write(my_image)
cv2.waitKey(1)
cap.release()
outVideo.release()
cv2.destroyAllWindows()对xml结点内容进行修改：例子为将name结点内容guai修改为Guai
def _load_pascal_annotation(filename):
tree = ET.parse(filename)
objs = tree.findall('object')
for ix, obj in enumerate(objs):
if obj.find('name').text.lower().strip() == 'guai':
print('the Guai is the key but now it is guai ')
key_word = 'Guai'
obj.find('name').text = key_word
tree.write(filename)
print(filename)
if obj.find('name').text.lower().strip() == 'texiao':
print('the Texiao is the key but now it is texiao ')
key_word = 'Texiao'
obj.find('name').text = key_word
tree.write(filename)
print(filename)
将xml结点内容输入到txt文件内，针对文件夹内包括文件夹的：
def nodeToTxt():
source_path = '/media/ksyun/PROJECTDATA/810_xml/xml'
text_file = '/home/ksyun/11/11.txt'
writer_file = open(text_file,'a')
for names in os.listdir(source_path):
xml_flie = os.path.join(source_path, names)
print xml_flie
for name in os.listdir(xml_flie):
image_name_png = os.path.join(xml_flie, name)
name_image = os.path.splitext(name)[0]
isNone = _load_pascal_annotation(image_name_png)
if isNone is True:
print ('is None')
else:
writer_file.write(name_image + '\n')
移动或者拷贝文件从一个文件夹到另一个文件夹：
def mymovefile(srcfile, dstfile):
if not os.path.isfile(srcfile):
print "%s not exist!" % (srcfile)
else:
fpath, fname = os.path.split(dstfile) # 分离文件名和路径
if not os.path.exists(fpath):
os.makedirs(fpath) # 创建路径
shutil.move(srcfile, dstfile) # 移动文件
print "move %s -> %s" % (srcfile, dstfile)
def mycopyfile(srcfile, dstfile):
if not os.path.isfile(srcfile):
print "%s not exist!" % (srcfile)
else:
fpath, fname = os.path.split(dstfile) # 分离文件名和路径
if not os.path.exists(fpath):
os.makedirs(fpath) # 创建路径，
#dstfile可以是文件夹的名字，此时移动后的srcfile里的文件保持原来的名字
shutil.copyfile(srcfile, dstfile) # 复制文件
print "copy %s -> %s" % (srcfile, dstfile)
TensorFlow中pb文件与pbtxt文件的相互转换（其实就是参数as_text是否为真）：
import tensorflow as tf
from tensorflow.python.platform import gfile
from google.protobuf import text_format
def convert_pb_to_pbtxt(filename):
with gfile.FastGFile(filename,'rb') as f:
graph_def = tf.GraphDef()
graph_def.ParseFromString(f.read())
tf.import_graph_def(graph_def, name='')
tf.train.write_graph(graph_def, './', 'protobuf.pbtxt', as_text=True)
return
def convert_pbtxt_to_pb(filename):
"""Returns a `tf.GraphDef` proto representing the data in the given pbtxt file.
Args:
filename: The name of a file containing a GraphDef pbtxt (text-formatted
`tf.GraphDef` protocol buffer data).
"""
with tf.gfile.FastGFile(filename, 'r') as f:
graph_def = tf.GraphDef()
file_content = f.read()
# Merges the human-readable string in `file_content` into `graph_def`.
text_format.Merge(file_content, graph_def)
tf.train.write_graph( graph_def , './' , 'protobuf.pb' , as_text = False )
在image上进行画框，并保存画过框的图像
比如在进行图像检测时，如何将检测出来的图像上的目标进行画框，并将该图像保存。依次延伸到视频，对视频的每一祯进行处理：
def draw_bounding_box_on_image_array(image,
ymin,
xmin,
ymax,
xmax,
color='red',
thickness=4,
display_str_list=(),
use_normalized_coordinates=True):
image_pil = Image.fromarray(np.uint8(image)).convert('RGB')
draw_bounding_box_on_image(image_pil, ymin, xmin, ymax, xmax, color,
thickness, display_str_list,
use_normalized_coordinates)
np.copyto(image, np.array(image_pil))
def draw_bounding_box_on_image(image,
ymin,
xmin,
ymax,
xmax,
color='red',
thickness=5,
display_str_list=(),
use_normalized_coordinates=True):
draw = ImageDraw.Draw(image)
im_width, im_height = 1, 1
if use_normalized_coordinates:
(left, right, top, bottom) = (xmin * im_width, xmax * im_width,
ymin * im_height, ymax * im_height)
else:
(left, right, top, bottom) = (xmin, xmax, ymin, ymax)
draw.line([(left, top), (left, bottom), (right, bottom),
(right, top), (left, top)], width=thickness, fill=color)
try:
font = ImageFont.truetype('arial.ttf', 24)
except IOError:
font = ImageFont.load_default()
# If the total height of the display strings added to the top of the bounding
# box exceeds the top of the image, stack the strings below the bounding box
# instead of above.
display_str_heights = [font.getsize(ds)[1] for ds in display_str_list]
# Each display_str has a top and bottom margin of 0.05x.
total_display_str_height = (1 + 2 * 0.05) * sum(display_str_heights)
if top > total_display_str_height:
text_bottom = top
else:
text_bottom = bottom + total_display_str_height
# Reverse list and print from bottom to top.
for display_str in display_str_list[::-1]:
text_width, text_height = font.getsize(display_str)
margin = np.ceil(0.05 * text_height)
draw.rectangle(
[(left, text_bottom - text_height - 2 * margin), (left + text_width,
text_bottom)],
fill=color)
draw.text(
(left + margin, text_bottom - text_height - margin),
display_str,
fill='black',
font=font)
text_bottom -= text_height - 2 * margin
def my_vis_detections(im, class_name, dets, color, thresh=0.5):
"""Draw detected bounding boxes."""
inds = np.where(dets[:, -1] >= thresh)[0]
if len(inds) == 0:
return
for i in inds:
bbox = dets[i, :4]
score = dets[i, -1]
ymin, xmin, ymax, xmax = bbox[1], bbox[0], bbox[3], bbox[2]
draw_bounding_box_on_image_array(im,
ymin,
xmin,
ymax,
xmax,
color=color,
thickness=4,
display_str_list=['{:s} {:.3f}'.format(class_name, score)],
use_normalized_coordinates=True
)
def my_demo(sess, net, im):
scores, boxes = im_detect(sess, net, im)
im = im[:, :, (2, 1, 0)]
CONF_THRESH = 0.8
NMS_THRESH = 0.3
for cls_ind, cls in enumerate(CLASSES[1:]):
color = CLASSBBOXCOLOR[cls_ind]
cls_ind += 1 # because we skipped background
cls_boxes = boxes[:, 4 * cls_ind:4 * (cls_ind + 1)]
cls_scores = scores[:, cls_ind]
dets = np.hstack((cls_boxes,
cls_scores[:, np.newaxis])).astype(np.float32)
keep = nms(dets, NMS_THRESH)
dets = dets[keep, :]
my_vis_detections(im, cls, dets, color, thresh=CONF_THRESH)
return im该例子时我在已经通过faster-rcnn进行训练，通过im_detect(sess,net,im)函数后，出来的时对应的每一幅图像的目标检测结果，包括每一个目标的得分以及box。最终的cls和dets时经过赛选后的目标。通过my_vis_detextions()函数对每一类包含的目标进行画框（可以对框的颜色以及粗细进行设置）