[PYTHON] Collect only facial images of a specific person by web scraping
I started studying machine learning in April, and sometimes I wish I had a face image of a specific person, so I decided to collect it by web scraping. Perhaps I can find such a dataset if I search for it, but I wanted to do it with a photo of my favorite person's face anyway. Since the face detection of openCV alone extracts the non-face part and collects the face images of unrelated people, the purpose is to add a face recognition system and collect only the face images of a specific person.
Before running the program
Notes
Web scraping is a lot of gray, so be careful not to overload the server when you run it.
Environment / version
Winsows10 Anaconda3 Python 3.5.6 cmake 3.17.1 dlib 19.19.0 face-recognition 1.3.0 opencv-python 4.2.0.34
About execution result
I will write the results in detail later, but this program collects face images using face recognition, but the feeling I have experimented with is the face of a specific person with almost 100% accuracy. I got an image, but if it's a Japanese face (or rather an Asian), the accuracy will drop. When collecting Japanese faces, it is necessary to manually sort them after execution.
How to use the program
The program code has been published on github, so please download it from there. If you want to do it because the explanation is good, please read the simple procedure on github and execute it.
This time I would like to collect facial images of ** Ai Shinozaki, the cutest in Japan **
Install library
This time we will use a library called face_recognize. This requires cmake and dlib as described in the Documentation (https://github.com/ageitgey/face_recognition). All you have to do is add the required libraries.
Description of main functions
getExternalLinks
def getExternalLinks(page):
externalLinks = []
for url in search(name, lang="jp", start=(page-1)*10, stop=10,pause = 2.0):
externalLinks.append(url)
return externalLinks
It is a function that acquires and returns 10 URLs of sites that appear in the search word using the google-search library. This time, the search word is "Ai Shinozaki image" and stored in the variable name.
DownloadImage You can actually access it from the link obtained by the previous function and get the image uploaded there.
def DownloadImage(externalLinks):
for url in externalLinks:
global num
header = {'User-Agent': 'Mozilla/5.0 (Windows NT 6.1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/41.0.2228.0 Safari/537.3'}
req = Request(url, headers=header)
try:
html = urlopen(req).read()
except HTTPError as err:
return None
bs = BeautifulSoup(html, 'html.parser')
#get path of image
downloadList = bs.find_all('img')
for download in downloadList:
try:
#convert relative path to absolute path
fileUrl = getAbsoluteURL(url, download['src'])
except:
continue
if fileUrl is not None:
#get all faces in the picture
face_list = face_detect(fileUrl)
print(fileUrl)
if face_list is None:
continue
for face in face_list:
#judge face
result = face_recog(face)
true_num = 0
#when the number of True is more than the threshold, write the image in local
for i in result:
true_num += i*1
if true_num >= threshold:
try:
if ".png " in fileUrl:
cv2.imwrite(downloadDirectory+str(num)+".png ", face)
else:
cv2.imwrite(downloadDirectory+str(num)+".jpg ", face)
num += 1
except:
print("Fail to save")
time.sleep(1)
return None
First, get all the images uploaded on the site, cut out the face image from each, and get it. Then, the face_recog function defines whether or not the person is the person for the face image.
face_recog
def face_recog(face):
#load some images that contain target's face
sample_image = face_recognition.load_image_file("sample_image/<image file>")
sample_image1 = face_recognition.load_image_file("sample_image/<image file>")
sample_image2 = face_recognition.load_image_file("sample_image/<image file>")
sample_image3 = face_recognition.load_image_file("sample_image/<image file>")
sample_image4 = face_recognition.load_image_file("sample_image/<image file>")
sample_image = face_recognition.face_encodings(sample_image)[0]
sample_image1 = face_recognition.face_encodings(sample_image1)[0]
sample_image2 = face_recognition.face_encodings(sample_image2)[0]
sample_image3 = face_recognition.face_encodings(sample_image3)[0]
sample_image4 = face_recognition.face_encodings(sample_image4)[0]
try:
unknown_image = face_recognition.face_encodings(face)[0]
except:
return [False]
known_faces = [
sample_image,
sample_image1,
sample_image2,
sample_image3,
sample_image4
]
results = face_recognition.compare_faces(known_faces, unknown_image)
return results
Acquire an image showing the face of the person you want to take in advance for face recognition. This time, in order to improve the accuracy, we will acquire 5 images, compare each with the input face image, and return the result of each.
And this time, if 2 or more out of 5 images are True, I will save the image. (If it happens to be one, different people who happen to match will be saved, and if it is 5 out of 5, the accuracy will increase, but the fraction will decrease) And on github, there is also a process to delete the exact same image at the end.
result
In the case of Ai Shinozaki
Here is the result of collecting images for one page of google search.
As you can see, the face images of quite different people are saved.
For Scarlett Johansson
Next, I will experiment with Scarlett Johansson, the sexiest and most beautiful overseas.
This is brilliantly filled with Scarlett Johansson, Specifically, 342 out of 343 were Scarlett Johansson. This is considerably more accurate than when Ai Shinozaki was. Maybe the face_recongnize library is for foreigners' faces and not suitable for identifying Asian faces.
I would like to experiment and try it with various people in the future.
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