face_privacy_filter/transform_detect.py

   1 #! python
   2 # -*- coding: utf-8 -*-
   3 """
   4 Wrapper for face detection task; wrapped in classifier for pipieline terminus
   5 """
   6 import cv2
   7 import os
   8 import pandas as pd
   9 import numpy as np
  10 from sklearn.base import BaseEstimator, ClassifierMixin
  11
  12 class FaceDetectTransform(BaseEstimator, ClassifierMixin):
  13     '''
  14     A sklearn transformer mixin that detects faces and optionally outputa the original detected image
  15     '''
  16     CASCADE_DEFAULT_FILE = "data/haarcascade_frontalface_alt.xml.gz"
  17     COL_FACE_X = 'x'
  18     COL_FACE_Y = 'y'
  19     COL_FACE_W = 'w'
  20     COL_FACE_H = 'h'
  21     COL_REGION_IDX = 'region'
  22     COL_IMAGE_IDX = 'image'
  23     COL_IMAGE_MIME = 'mime_type'
  24     COL_IMAGE_DATA = 'binary_stream'
  25     VAL_REGION_IMAGE_ID = -1
  26
  27     def __init__(self, cascade_path=None, include_image=True):
  28         self.include_image = include_image    # should output transform include image?
  29         self.cascade_path = cascade_path    # abs path outside of module
  30         self.cascade_obj = None # late-load this component
  31
  32     def get_params(self, deep=False):
  33         return {'include_image': self.include_image}
  34
  35     @staticmethod
  36     def generate_in_df(path_image="", bin_stream=b""):
  37         # munge stream and mimetype into input sample
  38         if path_image and os.path.exists(path_image):
  39             bin_stream = open(path_image, 'rb').read()
  40         return pd.DataFrame([['image/jpeg', bin_stream]], columns=[FaceDetectTransform.COL_IMAGE_MIME, FaceDetectTransform.COL_IMAGE_DATA])
  41
  42     @staticmethod
  43     def generate_out_image(row, path_image):
  44         # take image row and output to disk
  45         with open(path_image, 'wb') as f:
  46             f.write(row[FaceDetectTransform.COL_IMAGE_DATA][0])
  47
  48     @staticmethod
  49     def generate_out_dict(idx=VAL_REGION_IMAGE_ID, x=0, y=0, w=0, h=0, image=0):
  50         return {FaceDetectTransform.COL_REGION_IDX: idx, FaceDetectTransform.COL_FACE_X: x,
  51                 FaceDetectTransform.COL_FACE_Y: y, FaceDetectTransform.COL_FACE_W: w, FaceDetectTransform.COL_FACE_H: h,
  52                 FaceDetectTransform.COL_IMAGE_IDX: image,
  53                 FaceDetectTransform.COL_IMAGE_MIME: '', FaceDetectTransform.COL_IMAGE_DATA: ''}
  54
  55     @staticmethod
  56     def suppress_image(df):
  57         keep_col = [FaceDetectTransform.COL_FACE_X, FaceDetectTransform.COL_FACE_Y,
  58                     FaceDetectTransform.COL_FACE_W, FaceDetectTransform.COL_FACE_H,
  59                     FaceDetectTransform.COL_FACE_W, FaceDetectTransform.COL_FACE_H,
  60                     FaceDetectTransform.COL_REGION_IDX, FaceDetectTransform.COL_IMAGE_IDX]
  61         blank_cols = [col for col in df.columns if col not in keep_col]
  62         # set columns that aren't in our known column list to empty strings; search where face index==-1 (no face)
  63         df.loc[df[FaceDetectTransform.COL_REGION_IDX]==FaceDetectTransform.VAL_REGION_IMAGE_ID,blank_cols] = ""
  64         return df
  65
  66     @property
  67     def output_names_(self):
  68         return [FaceDetectTransform.COL_REGION_IDX, FaceDetectTransform.COL_FACE_X, FaceDetectTransform.COL_FACE_Y,
  69                  FaceDetectTransform.COL_FACE_W, FaceDetectTransform.COL_FACE_H,
  70                  FaceDetectTransform.COL_IMAGE_IDX, FaceDetectTransform.COL_IMAGE_MIME, FaceDetectTransform.COL_IMAGE_DATA]
  71
  72     @property
  73     def output_types_(self):
  74         list_name = self.output_names_
  75         list_type = self.classes_
  76         return [{list_name[i]:list_type[i]} for i in range(len(list_name))]
  77
  78     @property
  79     def n_outputs_(self):
  80         return 8
  81
  82     @property
  83     def classes_(self):
  84         return [int, int, int, int, int, int, str, str]
  85
  86     def score(self, X, y=None):
  87         return 0
  88
  89     def fit(self, X, y=None):
  90         return self
  91
  92     def predict(self, X, y=None):
  93         """
  94         Assumes a numpy array of [[mime_type, binary_string] ... ]
  95            where mime_type is an image-specifying mime type and binary_string is the raw image bytes
  96         """
  97         # if no model exists yet, create it
  98         if self.cascade_obj is None:
  99             if self.cascade_path is not None:
 100                 self.cascade_obj = cv2.CascadeClassifier(self.cascade_path)
 101             else:   # none provided, load what came with the package
 102                 pathRoot = os.path.dirname(os.path.abspath(__file__))
 103                 pathFile = os.path.join(pathRoot, FaceDetectTransform.CASCADE_DEFAULT_FILE)
 104                 self.cascade_obj = cv2.CascadeClassifier(pathFile)
 105
 106         dfReturn = None
 107         for image_idx in range(len(X)):
 108             file_bytes = np.asarray(bytearray(X[FaceDetectTransform.COL_IMAGE_DATA][image_idx]), dtype=np.uint8)
 109             img = cv2.imdecode(file_bytes, cv2.IMREAD_COLOR)
 110             # img = cv2.imread(image_set[1])
 111             faces = self.detect_faces(img)
 112
 113             df = pd.DataFrame()  # start with empty DF for this image
 114             if self.include_image:  # create and append the image if that's requested
 115                 dict_image = FaceDetectTransform.generate_out_dict(w=img.shape[1], h=img.shape[0], image=image_idx)
 116                 dict_image[FaceDetectTransform.COL_IMAGE_MIME] = X[FaceDetectTransform.COL_IMAGE_MIME][image_idx]
 117                 dict_image[FaceDetectTransform.COL_IMAGE_DATA] = X[FaceDetectTransform.COL_IMAGE_DATA][image_idx]
 118                 df = pd.DataFrame([dict_image])
 119             for idxF in range(len(faces)):  # walk through detected faces
 120                 face_rect = faces[idxF]
 121                 df = df.append(pd.DataFrame([FaceDetectTransform.generate_out_dict(idxF, face_rect[0], face_rect[1],
 122                                                                     face_rect[2], face_rect[3], image=image_idx)]),
 123                                ignore_index=True)
 124             if dfReturn is None:  # create an NP container for all image samples + features
 125                 dfReturn = df.reindex_axis(self.output_names_, axis=1)
 126             else:
 127                 dfReturn = dfReturn.append(df, ignore_index=True)
 128             #print("IMAGE {:} found {:} total rows".format(image_idx, len(df)))
 129
 130         return dfReturn
 131
 132     def detect_faces(self, img):
 133         if self.cascade_obj is None: return []
 134         gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
 135
 136         faces = self.cascade_obj.detectMultiScale(
 137             gray,
 138             scaleFactor=1.1,
 139             minNeighbors=5,
 140             minSize=(30, 30),
 141             flags=cv2.CASCADE_SCALE_IMAGE
 142         )
 143
 144         # Draw a rectangle around the faces
 145         #for (x, y, w, h) in faces:
 146         #    cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2)
 147         return faces
 148
 149     #############################################
 150     ## helper for i/o
 151     @staticmethod
 152     def read_byte_arrays(bytearray_string):
 153         """Method to recover bytes from pandas read/cast function:
 154             inputDf = pd.read_csv(config['input'], converters:{FaceDetectTransform.COL_IMAGE_DATA:FaceDetectTransform.read_byte_arrays})
 155            https://stackoverflow.com/a/43024993
 156         """
 157         from ast import literal_eval
 158         if type(bytearray_string)==str and bytearray_string.startswith("b'"):
 159             return bytearray(literal_eval(bytearray_string))
 160         return bytearray_string