pip install instagram-scraper

insta.sh
#!/bin/sh
instagram_login_user='' # あなたのユーザーネーム
instagram_login_pass='' # あなたのパスワード

target_tag='cat' #スクレイピング対象のタグ

instagram-scraper \
 --login_user $instagram_login_user \
 --login_pass $instagram_login_pass \
 --tag $target_tag \ 
 --media-types image \　#取得するデータタイプの指定
 --maximum 100 \ #取得するデータの最大数
 --latest \ #最後にスクレイピングしたところから始める

# For running inference on the TF-Hub module.
import tensorflow as tf
import tensorflow_hub as hub
import os 
import glob
import time
import numpy as np
import matplotlib.patheffects as pe 
import matplotlib.pyplot as plt
import tempfile
from six.moves.urllib.request import urlopen
from six import BytesIO
import numpy as np
from PIL import Image
from PIL import ImageColor
from PIL import ImageDraw
from PIL import ImageFont
from PIL import ImageOps

#SSDかFaster R-CNNを選択
#module_handle = 'https://tfhub.dev/google/openimages_v4/ssd/mobilenet_v2/1' 
module_handle = 'https://tfhub.dev/google/faster_rcnn/openimages_v4/inception_resnet_v2/1'

detector = hub.load(module_handle).signatures['default']

def showImage(img, r, imgfile, min_score=0.1):
  fig = plt.figure(dpi=150,figsize=(8,8))
  ax = plt.gca()
  ax.tick_params(axis='both', which='both', left=False, 
                 labelleft=False, bottom=False, labelbottom=False)
  ax.imshow(img)

  decode = np.frompyfunc( lambda p : p.decode("ascii"), 1, 1)

  boxes =       r['detection_boxes']
  scores =      r['detection_scores']
  class_names = decode( r['detection_class_entities'] )

  n = np.count_nonzero(scores >= min_score)

  # class_names に対応した 色の準備
  class_set = np.unique(class_names[:n])
  colors = dict()
  cmap = plt.get_cmap('tab10')
  for i, v in enumerate(class_set):
    colors[v] =cmap(i)

  # 矩形を描画 スコアが低いものから描画
  img_w = img.shape[1]
  img_h = img.shape[0]
  for i in reversed(range(n)):
    text = f'{class_names[i]} {100*scores[i]:.0f}%'
    color = colors[class_names[i]]
    y1, x1, y2, x2 = tuple(boxes[i])
    y1, y2 = y1*img_h, y2*img_h
    x1, x2 = x1*img_w, x2*img_w

    # 枠
    r = plt.Rectangle(xy=(x1, y1), width=(x2-x1), height=(y2-y1),
                      fill=False, edgecolor=color, joinstyle='round', 
                      clip_on=False, zorder=8+(n-i) )
    ax.add_patch( r )

    # タグ：テキスト
    t = ax.text(x1+img_w/200, y1-img_h/300, text, va='bottom', fontsize=6, color=color,zorder=8+(n-i))
    t.set_path_effects([pe.Stroke(linewidth=1.5,foreground='white'), pe.Normal()])
    fig.canvas.draw()
    r = fig.canvas.get_renderer()
    coords = ax.transData.inverted().transform(t.get_window_extent(renderer=r))
    tag_w = abs(coords[0,0]-coords[1,0])+img_w/100
    tag_h = abs(coords[0,1]-coords[1,1])+img_h/120

    # タグ：背景
    r = plt.Rectangle(xy=(x1, y1-tag_h), width=tag_w, height=tag_h,
                      edgecolor=color, facecolor=color,
                      joinstyle='round', clip_on=False, zorder=8+(n-i))
    ax.add_patch( r )
  #保存
  plt.savefig('/content/save/'+imgfile)
  plt.close()

import time
import numpy as np
import PIL.Image as Image

def run_detector(detector, path,img_file):
  # 画像を読み込んで detector に入力できる形式に変換
  img = Image.open(path+img_file) # Pillow(PIL)
  if img.mode == 'RGBA' :
    img = img.convert('RGB')
  converted_img = img.copy()
  converted_img = converted_img.resize((227,227),Image.LANCZOS) # 入力サイズに縮小
  converted_img = np.array(converted_img, dtype=np.float32)     # np.arrayに変換
  converted_img = converted_img / 255. # 0.0 ～ 1.0 に正規化
  converted_img = converted_img.reshape([1,227,227,3])
  converted_img = tf.constant(converted_img)

  t1 = time.time()
  result = detector(converted_img) # 一般物体検出（本体）
  t2 = time.time()
  print(f'検出時間 : {t2-t1:.3f} 秒' )

  # 結果をテキスト出力するための準備
  r = {key:value.numpy() for key,value in result.items()}
  boxes =       r['detection_boxes']
  scores =      r['detection_scores']
  decode = np.frompyfunc( lambda p : p.decode('ascii'), 1, 1)
  class_names = decode( r['detection_class_entities'] )

  # スコアが 0.25 以上の結果（n件）についてテキスト出力 
  print(f'検出オブジェクト' )
  n = np.count_nonzero(scores >= 0.25 )
  for i in range(n):
    y1, x1, y2, x2 = tuple(boxes[i])
    x1, x2 = int(x1*img.width), int(x2*img.width)
    y1, y2 = int(y1*img.height),int(y2*img.height)
    t = f'{class_names[i]:10} {100*scores[i]:3.0f}%  '
    t += f'({x1:>4},{y1:>4}) - ({x2:>4},{y2:>4})'
    print(t)
  #猫を検出したとき出力
    if "Cat" in t:
      showImage(np.array(img), r, img_file,min_score=0.25) # 検出結果を画像にオーバーレイ
  return t2-t1