我是Python的新手，但是我需要在我的项目中使用Python，我一直在学习。我不明白下面的代码：
result['data'] = pool.map(image_processor, im_info)
所以，我发现pool.map(somefunc,[list of params])类似于Matlab的parfor。我不明白的是与image_processor有关的部分。我假设它应该是一个函数，但它没有定义为使用def的函数。代码工作得很好，我试图用pdb跟踪它，它似乎按批次处理图像，为caffe输入。这可能是一个非常微不足道的问题，我只是Python的新手。那么image_processor的工作顺序是什么，以及如何在这个函数中进行更改（我将传递多个参数，我只需要知道我可以在哪里进行更改，因为函数未使用def定义）Thx
#Data layer for video. Change flow_frames and RGB_frames to be the path to the flow and RGB frames.
import sys
sys.path.append('../../python')
import caffe
import io
from PIL import Image
import matplotlib.pyplot as plt
import numpy as np
import scipy.misc
import time
import pdb
import glob
import pickle as pkl
import random
import h5py
from multiprocessing import Pool
from threading import Thread
import skimage.io
import copy
flow_frames = 'flow_images/'
RGB_frames = 'frames/'
test_frames = 16
train_frames = 16
test_buffer = 3
train_buffer = 24
def processImageCrop(im_info, transformer, flow):
im_path = im_info[0]
im_crop = im_info[1]
im_reshape = im_info[2]
im_flip = im_info[3]
data_in = caffe.io.load_image(im_path)
if (data_in.shape[0] < im_reshape[0]) | (data_in.shape[1] < im_reshape[1]):
data_in = caffe.io.resize_image(data_in, im_reshape)
if im_flip:
data_in = caffe.io.flip_image(data_in, 1, flow)
data_in = data_in[im_crop[0]:im_crop[2], im_crop[1]:im_crop[3], :]
processed_image = transformer.preprocess('data_in',data_in)
return processed_image
class ImageProcessorCrop(object):
def __init__(self, transformer, flow):
self.transformer = transformer
self.flow = flow
def __call__(self, im_info):
return processImageCrop(im_info, self.transformer, self.flow)
class sequenceGeneratorVideo(object):
def __init__(self, buffer_size, clip_length, num_videos, video_dict, video_order):
self.buffer_size = buffer_size
self.clip_length = clip_length
self.N = self.buffer_size*self.clip_length
self.num_videos = num_videos
self.video_dict = video_dict
self.video_order = video_order
self.idx = 0
def __call__(self):
label_r = []
im_paths = []
im_crop = []
im_reshape = []
im_flip = []
if self.idx + self.buffer_size >= self.num_videos:
idx_list = range(self.idx, self.num_videos)
idx_list.extend(range(0, self.buffer_size-(self.num_videos-self.idx)))
else:
idx_list = range(self.idx, self.idx+self.buffer_size)
for i in idx_list:
key = self.video_order[i]
label = self.video_dict[key]['label']
video_reshape = self.video_dict[key]['reshape']
video_crop = self.video_dict[key]['crop']
label_r.extend([label]*self.clip_length)
im_reshape.extend([(video_reshape)]*self.clip_length)
r0 = int(random.random()*(video_reshape[0] - video_crop[0]))
r1 = int(random.random()*(video_reshape[1] - video_crop[1]))
im_crop.extend([(r0, r1, r0+video_crop[0], r1+video_crop[1])]*self.clip_length)
f = random.randint(0,1)
im_flip.extend([f]*self.clip_length)
rand_frame = int(random.random()*(self.video_dict[key]['num_frames']-self.clip_length)+1+1)
frames = []
for i in range(rand_frame,rand_frame+self.clip_length):
frames.append(self.video_dict[key]['frames'] %i)
im_paths.extend(frames)
im_info = zip(im_paths,im_crop, im_reshape, im_flip)
self.idx += self.buffer_size
if self.idx >= self.num_videos:
self.idx = self.idx - self.num_videos
return label_r, im_info
def advance_batch(result, sequence_generator, image_processor, pool):
label_r, im_info = sequence_generator()
#tmp = image_processor(im_info[0])
result['data'] = pool.map(image_processor, im_info)
result['label'] = label_r
cm = np.ones(len(label_r))
cm[0::16] = 0
result['clip_markers'] = cm
class BatchAdvancer():
def __init__(self, result, sequence_generator, image_processor, pool):
self.result = result
self.sequence_generator = sequence_generator
self.image_processor = image_processor
self.pool = pool
def __call__(self):
return advance_batch(self.result, self.sequence_generator, self.image_processor, self.pool)
class videoRead(caffe.Layer):
def initialize(self):
self.train_or_test = 'test'
self.flow = False
self.buffer_size = test_buffer #num videos processed per batch
self.frames = test_frames #length of processed clip
self.N = self.buffer_size*self.frames
self.idx = 0
self.channels = 3
self.height = 227
self.width = 227
self.path_to_images = RGB_frames
self.video_list = 'ucf101_split1_testVideos.txt'
def setup(self, bottom, top):
random.seed(10)
self.initialize()
f = open(self.video_list, 'r')
f_lines = f.readlines()
f.close()
video_dict = {}
current_line = 0
self.video_order = []
for ix, line in enumerate(f_lines):
video = line.split(' ')[0].split('/')[1]
l = int(line.split(' ')[1])
frames = glob.glob('%s%s/*.jpg' %(self.path_to_images, video))
num_frames = len(frames)
video_dict[video] = {}
video_dict[video]['frames'] = frames[0].split('.')[0] + '.%04d.jpg'
video_dict[video]['reshape'] = (240,320)
video_dict[video]['crop'] = (227, 227)
video_dict[video]['num_frames'] = num_frames
video_dict[video]['label'] = l
self.video_order.append(video)
self.video_dict = video_dict
self.num_videos = len(video_dict.keys())
#set up data transformer
shape = (self.N, self.channels, self.height, self.width)
self.transformer = caffe.io.Transformer({'data_in': shape})
self.transformer.set_raw_scale('data_in', 255)
if self.flow:
image_mean = [128, 128, 128]
self.transformer.set_is_flow('data_in', True)
else:
image_mean = [103.939, 116.779, 128.68]
self.transformer.set_is_flow('data_in', False)
channel_mean = np.zeros((3,227,227))
for channel_index, mean_val in enumerate(image_mean):
channel_mean[channel_index, ...] = mean_val
self.transformer.set_mean('data_in', channel_mean)
self.transformer.set_channel_swap('data_in', (2, 1, 0))
self.transformer.set_transpose('data_in', (2, 0, 1))
self.thread_result = {}
self.thread = None
pool_size = 24
self.image_processor = ImageProcessorCrop(self.transformer, self.flow)
self.sequence_generator = sequenceGeneratorVideo(self.buffer_size, self.frames, self.num_videos, self.video_dict, self.video_order)
self.pool = Pool(processes=pool_size)
self.batch_advancer = BatchAdvancer(self.thread_result, self.sequence_generator, self.image_processor, self.pool)
self.dispatch_worker()
self.top_names = ['data', 'label','clip_markers']
print 'Outputs:', self.top_names
if len(top) != len(self.top_names):
raise Exception('Incorrect number of outputs (expected %d, got %d)' %
(len(self.top_names), len(top)))
self.join_worker()
for top_index, name in enumerate(self.top_names):
if name == 'data':
shape = (self.N, self.channels, self.height, self.width)
elif name == 'label':
shape = (self.N,)
elif name == 'clip_markers':
shape = (self.N,)
top[top_index].reshape(*shape)
def reshape(self, bottom, top):
pass
def forward(self, bottom, top):
if self.thread is not None:
self.join_worker()
#rearrange the data: The LSTM takes inputs as [video0_frame0, video1_frame0,...] but the data is currently arranged as [video0_frame0, video0_frame1, ...]
new_result_data = [None]*len(self.thread_result['data'])
new_result_label = [None]*len(self.thread_result['label'])
new_result_cm = [None]*len(self.thread_result['clip_markers'])
for i in range(self.frames):
for ii in range(self.buffer_size):
old_idx = ii*self.frames + i
new_idx = i*self.buffer_size + ii
new_result_data[new_idx] = self.thread_result['data'][old_idx]
new_result_label[new_idx] = self.thread_result['label'][old_idx]
new_result_cm[new_idx] = self.thread_result['clip_markers'][old_idx]
for top_index, name in zip(range(len(top)), self.top_names):
if name == 'data':
for i in range(self.N):
top[top_index].data[i, ...] = new_result_data[i]
elif name == 'label':
top[top_index].data[...] = new_result_label
elif name == 'clip_markers':
top[top_index].data[...] = new_result_cm
self.dispatch_worker()
def dispatch_worker(self):
assert self.thread is None
self.thread = Thread(target=self.batch_advancer)
self.thread.start()
def join_worker(self):
assert self.thread is not None
self.thread.join()
self.thread = None
def backward(self, top, propagate_down, bottom):
pass
class videoReadTrain_flow(videoRead):
def initialize(self):
self.train_or_test = 'train'
self.flow = True
self.buffer_size = train_buffer #num videos processed per batch
self.frames = train_frames #length of processed clip
self.N = self.buffer_size*self.frames
self.idx = 0
self.channels = 3
self.height = 227
self.width = 227
self.path_to_images = flow_frames
self.video_list = 'ucf101_split1_trainVideos.txt'
class videoReadTest_flow(videoRead):
def initialize(self):
self.train_or_test = 'test'
self.flow = True
self.buffer_size = test_buffer #num videos processed per batch
self.frames = test_frames #length of processed clip
self.N = self.buffer_size*self.frames
self.idx = 0
self.channels = 3
self.height = 227
self.width = 227
self.path_to_images = flow_frames
self.video_list = 'ucf101_split1_testVideos.txt'
class videoReadTrain_RGB(videoRead):
def initialize(self):
self.train_or_test = 'train'
self.flow = False
self.buffer_size = train_buffer #num videos processed per batch
self.frames = train_frames #length of processed clip
self.N = self.buffer_size*self.frames
self.idx = 0
self.channels = 3
self.height = 227
self.width = 227
self.path_to_images = RGB_frames
self.video_list = 'ucf101_split1_trainVideos.txt'
class videoReadTest_RGB(videoRead):
def initialize(self):
self.train_or_test = 'test'
self.flow = False
self.buffer_size = test_buffer #num videos processed per batch
self.frames = test_frames #length of processed clip
self.N = self.buffer_size*self.frames
self.idx = 0
self.channels = 3
self.height = 227
self.width = 227
self.path_to_images = RGB_frames
self.video_list = 'ucf101_split1_testVideos.txt'
答案 0 :(得分：1)
见这一行：
self.image_processor = ImageProcessorCrop(self.transformer, self.flow)
ImageProcessorCrop类有一个__call__方法，允许使用这些对象，就像它们的功能一样。当你打电话给他们时，它会调用那个方法。