How to increase the font size of the bounding box in Tensorflow object detection module?

Find "visualization_utils.py"
This exist in "research/object_detection/utils"

And find below code, modify font size.

If it is not working, change path to absolute path.

Or copy font file to project folder.

Thank you.

try:

#font = ImageFont.truetype('arial.ttf', 24)

font = ImageFont.truetype('/Library/Fonts/Arial.ttf', 100) #leon modify

except IOError:

font = ImageFont.load_default()

Font size up

Korean car number plate 822 images data share

Korean car number plate 822 images,
refer to this link:
https://www.amazon.com/clouddrive/share/zT8bgXMGB4m7ex8VMI8Mgibhi6qJYZRcOtnOwYArMgi

Thank you.

pedestrian 29,286 images data

I share pedestrian 29,286 images data.
https://www.amazon.com/clouddrive/share/bp055ddwIp8DSCgTJL9AWeZvqin6gkpoTg8YrM6K4rt

Thank you.

frontal face 4,429 images (size 24 * 24)

Frontal face 4,429 images

Size is 24 x 24

refer to this link:

https://www.amazon.com/clouddrive/share/YEBEF5klqZHytohUkUoPXDSGdfpqcwrM6H9bMItzyRz

*If this data have copyright, please inform to me, I will delete ASAP.

Face 2166 image data

I share face image 2,166 data.

I got these data from an website at very long time ago.

So, I don't remember which site.

If this data have copyright, please inform to me. 

I will delete ASAP.

share link here :

https://www.amazon.com/clouddrive/share/7f6ge3SqL73qImKyGdHLTuUfcphFeF756J8B7Tazmgk

Thank you.

 

Image data : background images

This is background images those are 12,439.
I used this images for negative data when I train face detection model.

This is random images and there is no specific object.
So it will be use for background image or negative image.

Share link is here:
https://www.amazon.com/clouddrive/share/vYnNKcwkK987A7fAUuasX0aPmaMt5wKl6ekSk1v6QAR

I hope it help to anyone.
Thank you.

google object detection api error: ModuleNotFoundError: No module named 'pycocotools’

You might see this error : No module named 'pycocotools’, when you try to start train.
I refer to my solution.
Good luck!

Error:

ModuleNotFoundError: No module named 'pycocotools’

Solution :

git clone https://github.com/cocodataset/cocoapi.git

cd cocoapi/PythonAPI

make

cp -r pycocotools <path_to_object_detection_api>/models/research/

google object detection api error : object_detection/protos/model.proto:12:5: "Ssd" is not defined.

You might meet this error : "object_detection/protos/model.proto:12:5: "Ssd" is not defined.", when you try below command.

# From tensorflow/models/research/

protoc object_detection/protos/*.proto --python_out=.

I solved this error to use below work.

# Make sure you grab the latest version

curl -OL https://github.com/google/protobuf/releases/download/v3.2.0/protoc-3.2.0-linux-x86_64.zip 

# Unzip 

unzip protoc-3.2.0-linux-x86_64.zip -d protoc3 

# Move protoc to /usr/local/bin/ 

sudo mv protoc3/bin/* /usr/local/bin/ 

# Move protoc3/include to /usr/local/include/ 

sudo mv protoc3/include/* /usr/local/include/ 

# Optional: change owner 

sudo chwon [user] /usr/local/bin/protoc 

sudo chwon -R [user] /usr/local/include/google 

refer to this url:

https://gist.github.com/sofyanhadia/37787e5ed098c97919b8c593f0ec44d8

https://github.com/tensorflow/models/issues/4223

python make folder (create directory)

import os

makeFolder = 'folderName'

if not os.path.exists(makeFolder): #if not exist

os.makedirs(makeFolder) #make folder

vs code auto indentations

On Windows Shift + Alt + F
On Mac Shift + Option + F
On Ubuntu Ctrl + Shift +

python pandas, shuffle

refer to example code:

from sklearn.utils import shuffle

import pandas as pd

df = pd.read_csv('test.csv')

df = shuffle(df) #suffle

df.reset_index(drop=True) #index reset

df.to_csv('rfine_table_shuffle.csv', index=False)

Python String handling tip, remove specific character forward and backward in string

So, this is example code for this processing

input string
__A b c_D___

output string
A b c_D

'_' character is removed in forward and backward.

Refer to code. 🤘
Thank you.

testStr = [' Abc ', ' ', ' ', 'A bc', 'A bc ', ' ', ' a 12 43', ' da ', ' ', 'a2 34', ' 1 2 ']

testStr2 = ['___Abc__', '_', '__', 'A_bc', 'A_bc_', '_____', '_a_12_43', '_da____', '__', 'a2_34', '_1_2_']

def removeTFoward(str, T):

if len(str) == 0:

return str

if str[0] == T:

return removeTFoward(str[1:],T)

else:

return str

def removeTBackword(str, T):

if len(str) == 0:

return str

if str[-1] == T:

return removeTBackword(str[:-1],T)

else:

return str

for n, v in enumerate(testStr2):

str = removeTFoward(v, '_')

str = removeTBackword(str, '_')

if len(str) == 0:

print('nothing')

else:

print(str)

output :
Abc
nothing
nothing
A_bc
A_bc
nothing
a_12_43
da
nothing
a2_34
1_2

Dithering python opencv source code (Floyd–Steinberg dithering)

This is dithering example, it make image like a stippling effect.

I referenced to blew website.
wiki page:
https://en.wikipedia.org/wiki/Floyd–Steinberg_dithering 

youtube tutorial (thank you, good job!) :
https://www.youtube.com/watch?v=0L2n8Tg2FwI&t=0s&list=WL&index=151

In the source code, there are two functions those are :

dithering_gray, dithering_color

One is for Gary image (1 channel), other is for color (3 channel).

It's very easy to use, just call function~ ^^

import cv2

import numpy as np

def minmax(v):

if v > 255:

v = 255

if v < 0:

v = 0

return v

def dithering_gray(inMat, samplingF):

#https://en.wikipedia.org/wiki/Floyd–Steinberg_dithering

#https://www.youtube.com/watch?v=0L2n8Tg2FwI&t=0s&list=WL&index=151

#input is supposed as color

# grab the image dimensions

h = inMat.shape[0]

w = inMat.shape[1]

# loop over the image

for y in range(0, h-1):

for x in range(1, w-1):

# threshold the pixel

old_p = inMat[y, x]

new_p = np.round(samplingF * old_p/255.0) * (255/samplingF)

inMat[y, x] = new_p

quant_error_p = old_p - new_p

# inMat[y, x+1] = minmax(inMat[y, x+1] + quant_error_p * 7 / 16.0)

# inMat[y+1, x-1] = minmax(inMat[y+1, x-1] + quant_error_p * 3 / 16.0)

# inMat[y+1, x] = minmax(inMat[y+1, x] + quant_error_p * 5 / 16.0)

# inMat[y+1, x+1] = minmax(inMat[y+1, x+1] + quant_error_p * 1 / 16.0)

inMat[y, x+1] = minmax(inMat[y, x+1] + quant_error_p * 7 / 16.0)

inMat[y+1, x-1] = minmax(inMat[y+1, x-1] + quant_error_p * 3 / 16.0)

inMat[y+1, x] = minmax(inMat[y+1, x] + quant_error_p * 5 / 16.0)

inMat[y+1, x+1] = minmax(inMat[y+1, x+1] + quant_error_p * 1 / 16.0)

# quant_error := oldpixel - newpixel

# pixel[x + 1][y ] := pixel[x + 1][y ] + quant_error * 7 / 16

# pixel[x - 1][y + 1] := pixel[x - 1][y + 1] + quant_error * 3 / 16

# pixel[x ][y + 1] := pixel[x ][y + 1] + quant_error * 5 / 16

# pixel[x + 1][y + 1] := pixel[x + 1][y + 1] + quant_error * 1 / 16

# return the thresholded image

return inMat

def dithering_color(inMat, samplingF):

#https://en.wikipedia.org/wiki/Floyd–Steinberg_dithering

#https://www.youtube.com/watch?v=0L2n8Tg2FwI&t=0s&list=WL&index=151

#input is supposed as color

# grab the image dimensions

h = inMat.shape[0]

w = inMat.shape[1]

# loop over the image

for y in range(0, h-1):

for x in range(1, w-1):

# threshold the pixel

old_b = inMat[y, x, 0]

old_g = inMat[y, x, 1]

old_r = inMat[y, x, 2]

new_b = np.round(samplingF * old_b/255.0) * (255/samplingF)

new_g = np.round(samplingF * old_g/255.0) * (255/samplingF)

new_r = np.round(samplingF * old_r/255.0) * (255/samplingF)

inMat[y, x, 0] = new_b

inMat[y, x, 1] = new_g

inMat[y, x, 2] = new_r

quant_error_b = old_b - new_b

quant_error_g = old_g - new_g

quant_error_r = old_r - new_r

inMat[y, x+1, 0] = minmax(inMat[y, x+1, 0] + quant_error_b * 7 / 16.0)

inMat[y, x+1, 1] = minmax(inMat[y, x+1, 1] + quant_error_g * 7 / 16.0)

inMat[y, x+1, 2] = minmax(inMat[y, x+1, 2] + quant_error_r * 7 / 16.0)

inMat[y+1, x-1, 0] = minmax(inMat[y+1, x-1, 0] + quant_error_b * 3 / 16.0)

inMat[y+1, x-1, 1] = minmax(inMat[y+1, x-1, 1] + quant_error_g * 3 / 16.0)

inMat[y+1, x-1, 2] = minmax(inMat[y+1, x-1, 2] + quant_error_r * 3 / 16.0)

inMat[y+1, x, 0] = minmax(inMat[y+1, x, 0] + quant_error_b * 5 / 16.0)

inMat[y+1, x, 1] = minmax(inMat[y+1, x, 1] + quant_error_g * 5 / 16.0)

inMat[y+1, x, 2] = minmax(inMat[y+1, x, 2] + quant_error_r * 5 / 16.0)

inMat[y+1, x+1, 0] = minmax(inMat[y+1, x+1, 0] + quant_error_b * 1 / 16.0)

inMat[y+1, x+1, 1] = minmax(inMat[y+1, x+1, 1] + quant_error_g * 1 / 16.0)

inMat[y+1, x+1, 2] = minmax(inMat[y+1, x+1, 2] + quant_error_r * 1 / 16.0)

# quant_error := oldpixel - newpixel

# pixel[x + 1][y ] := pixel[x + 1][y ] + quant_error * 7 / 16

# pixel[x - 1][y + 1] := pixel[x - 1][y + 1] + quant_error * 3 / 16

# pixel[x ][y + 1] := pixel[x ][y + 1] + quant_error * 5 / 16

# pixel[x + 1][y + 1] := pixel[x + 1][y + 1] + quant_error * 1 / 16

# return the thresholded image

return inMat

#read image

inMat = cv2.imread('iu.jpg') #lena.png')

#color ditering

outMat_color = dithering_color(inMat.copy(), 1)

cv2.imwrite('out_color.jpg', outMat_color)

#gray ditering

grayMat = cv2.cvtColor(inMat, cv2.COLOR_BGR2GRAY)

outMat_gray = dithering_gray(grayMat.copy(), 1)

cv2.imwrite('out_gray.jpg', outMat_gray)

python opencv, example code for image pixel access

The code is example to access every pixel in opencv python.
More detail, just see below code.
Thank you.

import cv2

def OpenCV_Access_RGBValue(inMat):

#input is supposed as color

# grab the image dimensions

h = inMat.shape[0]

w = inMat.shape[1]

# loop over the image

for y in range(0, h):

for x in range(0, w):

# threshold the pixel

b = inMat[y, x, 0]

g = inMat[y, x, 1]

r = inMat[y, x, 2]

b = 255 - b

g = 255 - g

r = 255 - r

inMat[y, x, 0] = b

inMat[y, x, 1] = g

inMat[y, x, 2] = r

#image[y, x] = 255 if image[y, x] >= 128T else 0

# return the thresholded image

return inMat

#read image

inMat = cv2.imread('iu.jpg')

#test value access

OpenCV_Access_RGBValue(inMat)

#display

cv2.namedWindow('test',0)

cv2.imwrite('output.jpg', inMat)

cv2.imshow('test',inMat)

cv2.waitKey(0)

search column name and modify data, python pandas usages

Above all,
Let's make initial column head.

import pandas as pd

#init

col_names = ['product', 'count']

word_pd = pd.DataFrame(columns = col_names)

There is no data yet.
So, let's add initial data

#add produce list

word_pd.loc[len(word_pd)] = ['apple', 4]

word_pd.loc[len(word_pd)] = ['orange', 7]

word_pd.loc[len(word_pd)] = ['beer', 10]

word_pd.loc[len(word_pd)] = ['cola', 7]

word_pd.loc[len(word_pd)] = ['beer', 8]

#check

print('origin data', word_pd)

>
origin data   product count
0   apple     4
1  orange     7
2    beer    10
3    cola     7
4    beer     8


OK, then let's find specific product name and increase count.

#find product

list_pd = word_pd.loc[word_pd['product'] == 'apple']

list_f = list_pd.index.tolist()

#add count

if len(list_f)>0:

for index in list_f:

word_pd.iloc[index][1] = word_pd.iloc[index, word_pd.columns.get_loc('count')] +1

print('result')

print('data', word_pd)

>
result
data   product count
0   apple     5
1  orange     7
2    beer    10
3    cola     7
4    beer     8



OK, at this time, let's find beer product and add count.
Note, there are 2 rows of beer product, so all beer product's count are increased.

#one more test

#find product

list_pd = word_pd.loc[word_pd['product'] == 'beer']

list_f = list_pd.index.tolist()

#add count

if len(list_f)>0:

for index in list_f:

word_pd.iloc[index][1] = word_pd.iloc[index, word_pd.columns.get_loc('count')] +1

print('result')

print('data', word_pd)

result
data   product count
0   apple     5
1  orange     7
2    beer    11
3    cola     7
4    beer     9




This is whole source code.

import pandas as pd

#init

col_names = ['product', 'count']

word_pd = pd.DataFrame(columns = col_names)

#add produce list

word_pd.loc[len(word_pd)] = ['apple', 4]

word_pd.loc[len(word_pd)] = ['orange', 7]

word_pd.loc[len(word_pd)] = ['beer', 10]

word_pd.loc[len(word_pd)] = ['cola', 7]

word_pd.loc[len(word_pd)] = ['beer', 8]

#check

print('origin data', word_pd)

#find product

list_pd = word_pd.loc[word_pd['product'] == 'apple']

list_f = list_pd.index.tolist()

#add count

if len(list_f)>0:

for index in list_f:

word_pd.iloc[index][1] = word_pd.iloc[index, word_pd.columns.get_loc('count')] +1

print('result')

print('data', word_pd)

#one more test

#find product

list_pd = word_pd.loc[word_pd['product'] == 'beer']

list_f = list_pd.index.tolist()

#add count

if len(list_f)>0:

for index in list_f:

word_pd.iloc[index][1] = word_pd.iloc[index, word_pd.columns.get_loc('count')] +1

print('result')

print('data', word_pd)

Thank you.

ValueError: too many values to unpack (expected 2), when you use "cv2.findContours"

check this tip!

before (error)
contours, _ = cv2.findContours(***, ***, ***)


after (solved)
_, contours, _ = cv2.findContours(***, ***, ***)

Mac error : missing xcrun at: /Library/Developer/CommandLineTools/usr/bin/xcrun (tip)

It seems to occur after osx updating.
In my case, I solved using below method.

xcode-select --install

^^.

python float decimal expression (tip)

Tip!

print('{} - {:.4f}'.format(11, 0.12314213))

> 11 - 0.1231

Exclude files that you don't want to show in directory window in VS CODE (The tip for I don't want to see ._* files in vs code)

You can set exclude file pattern in exclude tab in settings.


go to preferences -> settings



find exclude menu and add pattern that you don't want to see.
press 'ok' button and it will work immediately.

Thank you.

python, get index and value in for loop

Use 'enumerate'

for index, item in enumerate(values):

print(index, item)

Python opencv, get ROI Mat (cropping)

Please refer to this function..

def getROImat(inMat, x1, y1, x2, y2):

# Crop image

imCrop = inMat[int(y1):int(y2), int(x1):int(x2)]

return imCrop

pbtxt file for coco v2 data (90 categories contents)

item {
  name: "/m/01g317"
  id: 1
  display_name: "person"
}
item {
  name: "/m/0199g"
  id: 2
  display_name: "bicycle"
}
item {
  name: "/m/0k4j"
  id: 3
  display_name: "car"
}
item {
  name: "/m/04_sv"
  id: 4
  display_name: "motorcycle"
}
item {
  name: "/m/05czz6l"
  id: 5
  display_name: "airplane"
}
item {
  name: "/m/01bjv"
  id: 6
  display_name: "bus"
}
item {
  name: "/m/07jdr"
  id: 7
  display_name: "train"
}
item {
  name: "/m/07r04"
  id: 8
  display_name: "truck"
}
item {
  name: "/m/019jd"
  id: 9
  display_name: "boat"
}
item {
  name: "/m/015qff"
  id: 10
  display_name: "traffic light"
}
item {
  name: "/m/01pns0"
  id: 11
  display_name: "fire hydrant"
}
item {
  name: "/m/02pv19"
  id: 13
  display_name: "stop sign"
}
item {
  name: "/m/015qbp"
  id: 14
  display_name: "parking meter"
}
item {
  name: "/m/0cvnqh"
  id: 15
  display_name: "bench"
}
item {
  name: "/m/015p6"
  id: 16
  display_name: "bird"
}
item {
  name: "/m/01yrx"
  id: 17
  display_name: "cat"
}
item {
  name: "/m/0bt9lr"
  id: 18
  display_name: "dog"
}
item {
  name: "/m/03k3r"
  id: 19
  display_name: "horse"
}
item {
  name: "/m/07bgp"
  id: 20
  display_name: "sheep"
}
item {
  name: "/m/01xq0k1"
  id: 21
  display_name: "cow"
}
item {
  name: "/m/0bwd_0j"
  id: 22
  display_name: "elephant"
}
item {
  name: "/m/01dws"
  id: 23
  display_name: "bear"
}
item {
  name: "/m/0898b"
  id: 24
  display_name: "zebra"
}
item {
  name: "/m/03bk1"
  id: 25
  display_name: "giraffe"
}
item {
  name: "/m/01940j"
  id: 27
  display_name: "backpack"
}
item {
  name: "/m/0hnnb"
  id: 28
  display_name: "umbrella"
}
item {
  name: "/m/080hkjn"
  id: 31
  display_name: "handbag"
}
item {
  name: "/m/01rkbr"
  id: 32
  display_name: "tie"
}
item {
  name: "/m/01s55n"
  id: 33
  display_name: "suitcase"
}
item {
  name: "/m/02wmf"
  id: 34
  display_name: "frisbee"
}
item {
  name: "/m/071p9"
  id: 35
  display_name: "skis"
}
item {
  name: "/m/06__v"
  id: 36
  display_name: "snowboard"
}
item {
  name: "/m/018xm"
  id: 37
  display_name: "sports ball"
}
item {
  name: "/m/02zt3"
  id: 38
  display_name: "kite"
}
item {
  name: "/m/03g8mr"
  id: 39
  display_name: "baseball bat"
}
item {
  name: "/m/03grzl"
  id: 40
  display_name: "baseball glove"
}
item {
  name: "/m/06_fw"
  id: 41
  display_name: "skateboard"
}
item {
  name: "/m/019w40"
  id: 42
  display_name: "surfboard"
}
item {
  name: "/m/0dv9c"
  id: 43
  display_name: "tennis racket"
}
item {
  name: "/m/04dr76w"
  id: 44
  display_name: "bottle"
}
item {
  name: "/m/09tvcd"
  id: 46
  display_name: "wine glass"
}
item {
  name: "/m/08gqpm"
  id: 47
  display_name: "cup"
}
item {
  name: "/m/0dt3t"
  id: 48
  display_name: "fork"
}
item {
  name: "/m/04ctx"
  id: 49
  display_name: "knife"
}
item {
  name: "/m/0cmx8"
  id: 50
  display_name: "spoon"
}
item {
  name: "/m/04kkgm"
  id: 51
  display_name: "bowl"
}
item {
  name: "/m/09qck"
  id: 52
  display_name: "banana"
}
item {
  name: "/m/014j1m"
  id: 53
  display_name: "apple"
}
item {
  name: "/m/0l515"
  id: 54
  display_name: "sandwich"
}
item {
  name: "/m/0cyhj_"
  id: 55
  display_name: "orange"
}
item {
  name: "/m/0hkxq"
  id: 56
  display_name: "broccoli"
}
item {
  name: "/m/0fj52s"
  id: 57
  display_name: "carrot"
}
item {
  name: "/m/01b9xk"
  id: 58
  display_name: "hot dog"
}
item {
  name: "/m/0663v"
  id: 59
  display_name: "pizza"
}
item {
  name: "/m/0jy4k"
  id: 60
  display_name: "donut"
}
item {
  name: "/m/0fszt"
  id: 61
  display_name: "cake"
}
item {
  name: "/m/01mzpv"
  id: 62
  display_name: "chair"
}
item {
  name: "/m/02crq1"
  id: 63
  display_name: "couch"
}
item {
  name: "/m/03fp41"
  id: 64
  display_name: "potted plant"
}
item {
  name: "/m/03ssj5"
  id: 65
  display_name: "bed"
}
item {
  name: "/m/04bcr3"
  id: 67
  display_name: "dining table"
}
item {
  name: "/m/09g1w"
  id: 70
  display_name: "toilet"
}
item {
  name: "/m/07c52"
  id: 72
  display_name: "tv"
}
item {
  name: "/m/01c648"
  id: 73
  display_name: "laptop"
}
item {
  name: "/m/020lf"
  id: 74
  display_name: "mouse"
}
item {
  name: "/m/0qjjc"
  id: 75
  display_name: "remote"
}
item {
  name: "/m/01m2v"
  id: 76
  display_name: "keyboard"
}
item {
  name: "/m/050k8"
  id: 77
  display_name: "cell phone"
}
item {
  name: "/m/0fx9l"
  id: 78
  display_name: "microwave"
}
item {
  name: "/m/029bxz"
  id: 79
  display_name: "oven"
}
item {
  name: "/m/01k6s3"
  id: 80
  display_name: "toaster"
}
item {
  name: "/m/0130jx"
  id: 81
  display_name: "sink"
}
item {
  name: "/m/040b_t"
  id: 82
  display_name: "refrigerator"
}
item {
  name: "/m/0bt_c3"
  id: 84
  display_name: "book"
}
item {
  name: "/m/01x3z"
  id: 85
  display_name: "clock"
}
item {
  name: "/m/02s195"
  id: 86
  display_name: "vase"
}
item {
  name: "/m/01lsmm"
  id: 87
  display_name: "scissors"
}
item {
  name: "/m/0kmg4"
  id: 88
  display_name: "teddy bear"
}
item {
  name: "/m/03wvsk"
  id: 89
  display_name: "hair drier"
}
item {
  name: "/m/012xff"
  id: 90
  display_name: "toothbrush"
}

google object detection api - error : Value Error: First Step Cannot Be Zero

This error is because of
schedule{
 step:0
 learning_rate: 0.00019...
}

So just change whole optimizer block to latest models optimizer block.
(This code is in the config file!)

before

optimizer {

momentum_optimizer {

learning_rate {

manual_step_learning_rate {

initial_learning_rate: 0.000199999994948

schedule {

step: 0

learning_rate: 0.000199999994948

}

schedule {

step: 900000

learning_rate: 1.99999994948e-05

}

schedule {

step: 1200000

learning_rate: 1.99999999495e-06

}

}

}

momentum_optimizer_value: 0.899999976158

}

use_moving_average: false

}

modified (example)

optimizer {

momentum_optimizer {

learning_rate {

cosine_decay_learning_rate {

learning_rate_base: 0.0399999991059

total_steps: 25000

warmup_learning_rate: 0.0133330002427

warmup_steps: 2000

}

}

momentum_optimizer_value: 0.899999976158

}

use_moving_average: false

}

or make step 0-> step 100 or something else, (not zero)


refer to this article
https://github.com/tensorflow/models/issues/3794

show all PYTHONPATH in command line (tip)

python -c "import sys; print(sys.path)"
or
echo $PYTHONPATH

has type str, but expected one of: bytes (tf.train.Example)

make str to bytes

for example

#String to bytes

my_str = "file name"

my_str_as_bytes = str.encode(my_str)

type(my_str_as_bytes) # ensure it is byte representation

#byte to string

my_decoded_str = my_str_as_bytes.decode()

type(my_decoded_str) # ensure it is string representation

Pandas simple tip

import pandas

read csv file without header

df = pandas.read_csv('./train.csv', header=None, index_col=False)
print(df)

                0     1     2     3     4      5
0    0101_003.png   770   946  2070  2973  table
1    0110_099.png   270  1653  2280  2580  table
2    0113_013.png   303   343  2273  2953  table
3    0140_007.png   664  1782  1814  2076  table
4    0146_281.png   704   432  1744  1552  table
5    0146_281.png   682  1740  1800  2440  table
6    0147_090.png   326   413  2106  1616  table
7    0147_090.png   760  1843  1643  2393  table
8    0147_125.png   310   338  2310   912  table
9    0147_125.png   754  1184  1798  1514  table
10   0147_256.png   590   366  1940  1520  table
..            ...   ...   ...   ...   ...    ...
410  9529_050.png   104  2234  2040  2512  table
411  9530_051.png    90   470  2394  1682  table
412  9531_070.png   166   368  2328  1088  table
413  9531_070.png   148  1100  2340  1788  table
414  9531_073.png    50   260  2336  2876  table
415  9532_146.png   563   490  2440  2853  table
416  9533_038.png  1278   454  2326  1358  table
417  9533_038.png  1270  1774  2328  2368  table

[418 rows x 6 columns]

several way to read first column

print(df[0].tolist())

print(df.values.tolist()[:][0])

print(df.iloc[:,0].values.tolist())

['0101_003.png', '0110_099.png', '0113_013.png', '0140_007.png', '0146_281.png', '0146_281.png', '0147_090.png', '0147_090.png', '0147_125.png', '0147_125.png', '0147_256.png', '0147_256.png', '0148_271.png', '0148_479.png', '0151_180.png', '0151_208.png', '0154_080.png', '0154_474.png', '0155_081.png', '0199_384.png', '0203_075.png', '0206_007.png', '0206_048.png',
...
'9522_041.png', '9522_055.png', '9522_055.png', '9525_037.png', '9525_043.png', '9525_043.png', '9525_043.png', '9526_017.png', '9526_028.png', '9526_028.png', '9526_028.png', '9527_018.png', '9527_024.png', '9527_024.png', '9528_043.png', '9528_043.png', '9528_061.png', '9528_061.png', '9528_061.png', '9529_050.png', '9529_050.png', '9529_050.png', '9530_051.png', '9531_070.png', '9531_070.png', '9531_073.png', '9532_146.png', '9533_038.png', '9533_038.png']

pandas for each row

for index, row in df.iterrows():

print(index, row[0], row[1])

0 0101_003.png 770
1 0110_099.png 270
2 0113_013.png 303
3 0140_007.png 664
4 0146_281.png 704
5 0146_281.png 682
6 0147_090.png 326
7 0147_090.png 760
8 0147_125.png 310
9 0147_125.png 754
10 0147_256.png 590
11 0147_256.png 368
...

Jupyter notebook ip and port setting (tip)

ipython notebook --ip=0.0.0.0 --port=80
or
jupyter notebook --ip=0.0.0.0 --port=80

object_detection/protos/*.proto: No such file or directory

move to this directory :
cd models/research

and try it again
protoc object_detection/protos/*.proto --python_out=.
good luck!