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Paddle-Lite
未验证 提交 77041ac4 编写于 作者: S Shuai Yuan 提交者： GitHub
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Add readme for image preprocess and fix bug (#1597) 

* add comments for imagetools

* format code
上级 98ab80cd
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Showing with 144 addition and 79 deletion
README.md
浏览文件 @ 77041ac4
......@@ -93,8 +93,9 @@ At present，work in support of onnx is also under operation in Baidu. Related t
[https://github.com/PaddlePaddle/paddle-onnx](https://github.com/PaddlePaddle/paddle-onnx)
### 4. Download parts of testing models and testing pictures
[http://mms-graph.bj.bcebos.com/paddle-mobile%2FmodelsAndImages.zip](http://mms-graph.bj.bcebos.com/paddle-mobile%2FmodelsAndImages.zip)
[http://mms-graph.bj.bcebos.com/paddle-mobile%2FmodelsAndImages.zip](http://mms-graph.bj.bcebos.com/paddle-mobile%2FmodelsAndImages.zip)
- input data generated by tools from `tools/python/imagetools`.
## 模型获得
......
tools/python/imagetools/README.md 0 → 100644
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# imagetools
This directory contains scripts generating input data file for paddle-mobile. The image data `g_test_image_1x3x224x224_banana` (used by `test/net/test_mobilenet.cpp`) of [http://mms-graph.bj.bcebos.com/paddle-mobile%2FmodelsAndImages.zip](http://mms-graph.bj.bcebos.com/paddle-mobile%2FmodelsAndImages.zip) is generated by this script.
## Generate Input
Edit script `img2nchw.py` as below according to your need:
```python
if __name__ == "__main__":
# set paras
input_image_path = 'banana.jpg'
reshape_dict = {"n":1, "c":3, "h":48, "w":512}
output_path = input_image_path.replace(input_image_path[-4:],
"_" + "_".join([str(reshape_dict['n']),
str(reshape_dict['c']),
str(reshape_dict['h']),
str(reshape_dict['w']),
"nchw",
"float"],))
channel_type = ChannelType.BGR
mean_bgr = (103.94, 116.78, 123.68) # (0, 0, 0)
pixel_scale = 0.017
```
tools/python/imagetools/imagetools.py
浏览文件 @ 77041ac4
......@@ -3,11 +3,15 @@ import cv2
from array import array
def resize_take_rgbs(path, shape_h_w):
print '--------------resize_take_rgbs-----------------begin'
def resize_take_rgbs(path, shape_h_w, SHOW_IMG=False):
print("[INFO] ---- resize_take_rgbs ---- start")
image = cv2.imread(path)
# print image.shape
cv2.imshow("before", image)
print("[INFO] image.shape:{}".format(image.shape))
print("[INFO] shape_h_w:{}".format(shape_h_w))
if SHOW_IMG:
cv2.imshow("before", image)
print_rgb(image[0, 0])
# image len may be for .just check it
......@@ -15,8 +19,10 @@ def resize_take_rgbs(path, shape_h_w):
image = cv2.resize(image, (shape_h_w[0], shape_h_w[1]))
cv2.imshow("after", image)
print image.shape
if SHOW_IMG:
cv2.imshow("after", image)
print("[INFO] resized image.shape:{}".format(image.shape))
height = shape_h_w[0]
width = shape_h_w[1]
......@@ -25,15 +31,20 @@ def resize_take_rgbs(path, shape_h_w):
bs_ = []
for h in range(0, height):
for w in range(0, width):
'''
bs_.append(image[h, w, 0])
gs_.append(image[h, w, 1])
rs_.append(image[h, w, 2])
'''
bs_.append(image[w, h, 0])
gs_.append(image[w, h, 1])
rs_.append(image[w, h, 2])
# print image[2, 2, 0]/255.
print len(bs_)
print len(gs_)
print len(rs_)
print '--------------resize_take_rgbs-----------------end'
print("[INFO] ---- resize_take_rgbs ---- end")
return bs_, gs_, rs_
......@@ -56,6 +67,5 @@ def print_rgb((b, g, r)):
def save_to_file(to_file_name, array):
to_file = open(to_file_name, "wb")
array.tofile(to_file)
to_file.close()
with open(to_file_name, "wb") as file_handle:
array.tofile(file_handle)
tools/python/imagetools/img2nchw.py
浏览文件 @ 77041ac4
......@@ -9,22 +9,21 @@ class ChannelType(Enum):
RGB = 0,
BGR = 1
def combine_bgrs_nchw(bgrs, means_b_g_r=(103.94, 116.78, 123.68), scale=0.017, channel_type=ChannelType.BGR):
print("[INFO] ---- combine_bgrs_nchw ---- start")
print("[INFO] scale:{}".format(scale))
print("[INFO] mean_b_g_r:{}".format(means_b_g_r))
#print("[INFO] bgrs:{}".format(bgrs))
def combine_bgrs_nchw(bgrs, means_b_g_r, scale, channel_type=ChannelType.BGR):
print '--------------combine_bgrs_nchw-----------------begin'
print "scale: %f" % scale
print means_b_g_r
# print len(bgrs)
bs = bgrs[0]
gs = bgrs[1]
rs = bgrs[2]
assert len(bs) == len(gs) == len(rs)
print len(bs)
bgrs_float_array = array('f')
print("[INFO] element size of blue channel = len(bs) = {}".format(len(bs)))
bgrs_float_array = array('f')
if channel_type == ChannelType.BGR:
print 'bgr'
print('[INFO] bgr format')
for i in range(0, len(bs)):
bgrs_float_array.append((bs[i] - means_b_g_r[0]) * scale) # b
for i in range(0, len(gs)):
......@@ -32,8 +31,7 @@ def combine_bgrs_nchw(bgrs, means_b_g_r, scale, channel_type=ChannelType.BGR):
for i in range(0, len(rs)):
bgrs_float_array.append((rs[i] - means_b_g_r[2]) * scale) # r
elif channel_type == ChannelType.RGB:
print 'rgb'
print('[INFO] rgb format')
for i in range(0, len(rs)):
bgrs_float_array.append((rs[i] - means_b_g_r[2]) * scale) # r
for i in range(0, len(gs)):
......@@ -41,29 +39,50 @@ def combine_bgrs_nchw(bgrs, means_b_g_r, scale, channel_type=ChannelType.BGR):
for i in range(0, len(bs)):
bgrs_float_array.append((bs[i] - means_b_g_r[0]) * scale) # b
print len(bgrs_float_array)
'''
print("lenI(bgrs_float_array)={}".format(len(bgrs_float_array)))
print '------------------'
print bgrs_float_array[0]
print bgrs_float_array[224 * 224 * 2 + 224 * 2 + 2]
# for i in range(0, 9):
# print'bs %d' % i
# print bs[i] / 255.
print bs[224 * 2 + 2] / 255.
print '--------------combine_bgrs_nchw-----------------end'
'''
print("[INFO] ---- combine_bgrs_nchw ---- end")
return bgrs_float_array
# bgrs = tools.resize_take_rgbs('banana.jpeg', (224, 224, 3))
# array = combine_bgrs_nchw(bgrs, (103.94, 116.78, 123.68), 0.017, array,ChannelType.BGR)
# tools.save_to_file('banana_1_3_224_224_nchw_float')
# cv2.waitKey(0)
bgrs = tools.resize_take_rgbs('datas/jpgs/0000_0.9834-148196_82452-0ad4b83ec6bc0f9c5f28101539267054.jpg_p0_0.126571263346.jpg', (224, 224, 3))
array = combine_bgrs_nchw(bgrs, (0, 0, 0), 1. / 255, ChannelType.RGB)
tools.save_to_file('datas/desktop_1_3_224_224_nchw_float', array)
if __name__ == "__main__":
# set paras
#input_image_path = 'banana.jpg'
#input_image_path = "ocr_detect_512x512.png"
input_image_path = "ocr_recog_48x512.png"
reshape_dict = {"n":1, "c":3, "h":48, "w":512}
output_path = input_image_path.replace(input_image_path[-4:],
"_" + "_".join([str(reshape_dict['n']),
str(reshape_dict['c']),
str(reshape_dict['h']),
str(reshape_dict['w']),
"nchw",
"float"],))
channel_type = ChannelType.BGR
mean_bgr = (103.94, 116.78, 123.68)
pixel_scale = 0.017
#mean_bgr = (0, 0, 0)
#pixel_scale = 1. / 255
print("[INFO] input_image_path:{}".format(input_image_path))
print("[INFO] reshape_dict:{}".format(reshape_dict))
print("[INFO] output_path:{}".format(output_path))
print("[INFO] mean_bgr:{}".format(mean_bgr))
print("[INFO] pixel_scale:{}".format(pixel_scale))
bgrs = tools.resize_take_rgbs(input_image_path, (reshape_dict['h'],
reshape_dict['w'],
reshape_dict['c']))
array = combine_bgrs_nchw(bgrs, mean_bgr, pixel_scale, channel_type)
tools.save_to_file(output_path, array)
print("[INFO] save {} successfully".format(output_path))
#cv2.waitKey(0)
tools/python/imagetools/numpy2binary.py
浏览文件 @ 77041ac4
#!/usr/bin/env bash
# coding=utf-8
# 这个脚本是可以将numpy合并到二进制
# This script convert numpy format to binary's
import cv2
import numpy as np
import imagetools as tools
from array import array
#
# image = cv2.imread(path)
# print image.shape
#
# print_rgb(image[0, 0])
# # image len may be for .just check it
# image.resize(shape_h_w)
data = np.fromfile('/Users/xiebaiyuan/PaddleProject/paddle-mobile/tools/python/imagetools/datas/jpgs2/0000_0.9834-148196_82452-0ad4b83ec6bc0f9c5f28101539267054.jpg_p0_0.126571263346.jpg.input.npfile','f')
print data.size
print data
data.reshape(1, 3, 224, 224)
out_array = array('f')
print'--------------------'
print data.size
print data[0]
print '如果是nhwc --------'
# rgb rgb rgb rgb rgb
print data[224 * 3 * 2 + 3 * 2 + 2]
# print data[2]
print '如果是nchw --------'
# rgb rgb rgb rgb rgb
print data[224 * 224 * 2 + 224 * 2 + 2]
# print data[2]
# 明明是nchw
for i in range(0, data.size):
out_array.append(data[i])
print len(out_array)
print out_array[224 * 224 * 2 + 224 * 2 + 2]
# print out_array
tools.save_to_file('datas/in_put_1_3_224_224_nchw', out_array)
'''
image = cv2.imread(path)
print image.shape
print_rgb(image[0, 0])
# mage len may be for .just check it
image.resize(shape_h_w)
'''
if __name__ == "__main__":
# input params
reshape_dict = {"n": 1, "c": 3, "h": 224, "w": 224}
np_file_path = 'banana_1_3_224_224_nchw_float'
save_file_name = 'in_put_1_3_224_224_nchw'
# load input etc.
np = np.fromfile(np_file_path, 'f')
#np = cv2.imread(np_file_path)
print("np.size:{}".format(np.size))
print("np:{}".format(np))
np.reshape(reshape_dict['n'],
reshape_dict['c'],
reshape_dict['h'],
reshape_dict['w'])
out_array = array('f')
'''
print("--------------------")
print("np.size:{}".format(np.size))
print("np[0]:{}".format(np[0])
print("如果是nhw")
# rgb rgb rgb rgb rgb
print np[224 * 3 * 2 + 3 * 2 + 2]
# print np[2]
print '如果是nchw --------'
# rgb rgb rgb rgb rgb
print(np[224 * 224 * 2 + 224 * 2 + 2])
# print np[2]
# 明明是nchw
'''
for i in range(0, np.size):
out_array.append(np[i])
print("len(out_array):{}".format(len(out_array)))
print("out_array[224 * 224 * 2 + 224 * 2 + 2]:{}".format(out_array[224 * 224 * 2 + 224 * 2 + 2]))
# print out_array
tools.save_to_file(save_file_name, out_array)
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