파이썬/텐서플로/2020-06-/ 셀렙 얼굴 맞추기 : 채닝 테이텀 #41

# -*- coding: utf-8 -*-
"""Copy of Which Celebrity are You.ipynb
 
Automatically generated by Colaboratory.
 
Original file is located at
    https://colab.research.google.com/drive/1cZT1xR47C_72L5P-uKpSAByFxSp6O6j0
 
# Introduction
Please check this article for more background and explanation about the implementation: (https://towardsdatascience.com/which-celebrity-are-you-d8c6507f21c9).
 
# Step 0: Install & load the necessary packages
"""
 
!pip install mtcnn
!pip install keras_vggface
!pip install tensorflow
!pip install keras
!pip install opencv
!pip install PIL
 
import mtcnn
from mtcnn.mtcnn import MTCNN
from keras_vggface.vggface import VGGFace
from keras_vggface.utils import preprocess_input
from keras_vggface.utils import decode_predictions
import PIL
import os
from urllib import request
import numpy as np
import cv2
# Import this one if you are working in the google colab environment
from google.colab.patches import cv2_imshow
 
"""# Step 1 : Image loading"""
 
# Give the image link
url = "https://upload.wikimedia.org/wikipedia/commons/thumb/8/8d/Channing_Tatum_by_Gage_Skidmore_3.jpg/330px-Channing_Tatum_by_Gage_Skidmore_3.jpg"
 
# Open the link and save the image to res
res = request.urlopen(url)
# Read the res object and convert it to an array
img = np.asarray(bytearray(res.read()), dtype='uint8')
# Add the color variable
img = cv2.imdecode(img, cv2.IMREAD_COLOR)
# Show the image
cv2_imshow(img)
 
"""# Step 2: Face detection"""
 
# Initialize mtcnn detector
detector = MTCNN()
 
# set face extraction parameters
target_size = (224,224) # output image size
border_rel = 0 # increase or decrease zoom on image
 
# detect faces in the image
detections = detector.detect_faces(img)
print(detections)
 
x1, y1, width, height = detections[0]['box']
dw = round(width * border_rel)
dh = round(height * border_rel)
x2, y2 = x1 + width + dw, y1 + height + dh
face = img[y1:y2, x1:x2]
 
# resize pixels to the model size
face = PIL.Image.fromarray(face)
face = face.resize((224, 224))
face = np.asarray(face)
# show face
cv2_imshow(face)
 
"""# Step 3: Preprocessing"""
 
# convert to float32
face_pp = face.astype('float32')
face_pp = np.expand_dims(face_pp, axis = 0)
 
face_pp = preprocess_input(face_pp, version = 2)
 
"""# Step 4: Predict"""
 
# Create the resnet50 Model
model = VGGFace(model= 'resnet50')
# Check what the required input of the model is & output
print('Inputs: {input}'.format(input = model.inputs))
print('Output: {output}'.format(output = model.outputs))
 
# predict the face with the input
prediction = model.predict(face_pp)
 
"""# Step 5: Extract results"""
 
# convert predictions into names & probabilities
results = decode_predictions(prediction)
# Display results
cv2_imshow(img)
for result in results[0]:
  print ('%s: %.3f%%' % (result[0], result[1]*100))
 
"""# Lets put everything together"""
 
def get_img(url):
  # Open the link and save the image to res
  res = request.urlopen(url)
  # Read the res object and convert it to an array
  img = np.asarray(bytearray(res.read()), dtype='uint8')
  # Add the color variable
  img = cv2.imdecode(img, cv2.IMREAD_COLOR)
  return img
 
def find_face(img, detector = None):
  # Initialize mtcnn detector
  detector = MTCNN() if detector is None else detector 
  # set face extraction parameters
  target_size = (224,224) # output image size
  border_rel = 0 # increase or decrease zoom on image
  # detect faces in the image
  detections = detector.detect_faces(img)
  x1, y1, width, height = detections[0]['box']
  dw = round(width * border_rel)
  dh = round(height * border_rel)
  x2, y2 = x1 + width + dw, y1 + height + dh
  face = img[y1:y2, x1:x2]
  # resize pixels to the model size
  face = PIL.Image.fromarray(face)
  face = face.resize((224, 224))
  face = np.asarray(face)
  return face
 
def preprocess_face(face):
  # convert to float32
  face_pp = face.astype('float32')
  face_pp = np.expand_dims(face_pp, axis = 0)
  face_pp = preprocess_input(face_pp, version = 2)
  return face_pp
 
def predict(face_pp, model=None):
  model = VGGFace(model= 'resnet50') if model is None else model
  return model.predict(face_pp)
 
def extract_and_display_results(prediction, img):
  # convert predictions into names & probabilities
  results = decode_predictions(prediction)
  # Display results
  cv2_imshow(img)
  for result in results[0]:
    print ('%s: %.3f%%' % (result[0], result[1]*100))
  return results
 
def predict_from_image_url(url, face_detector=None, model=None):
  img = get_img(url)
  face = find_face(img, detector=face_detector)
  face_pp = preprocess_face(face)
  prediction = predict(face_pp, model=model)
  return extract_and_display_results(prediction, img)
 
URL = "https://upload.wikimedia.org/wikipedia/commons/thumb/8/8d/Channing_Tatum_by_Gage_Skidmore_3.jpg/330px-Channing_Tatum_by_Gage_Skidmore_3.jpg"
results = predict_from_image_url(url=URL, face_detector=detector, model=model)