2 # -*- coding: utf-8 -*-
4 Wrapper for region processing task; wrapped in classifier for pipieline terminus
10 from sklearn.base import BaseEstimator, ClassifierMixin
12 # NOTE: If this class were built in another model (e.g. another vendor, class, etc), we would need to
13 # *exactly match* the i/o for the upstream (detection) and downstream (this processing)
14 from face_privacy_filter.transform_detect import FaceDetectTransform
16 class RegionTransform(BaseEstimator, ClassifierMixin):
18 A sklearn classifier mixin that manpulates image content based on input
21 def __init__(self, transform_mode="pixelate"):
22 self.transform_mode = transform_mode # specific image processing mode to utilize
24 def get_params(self, deep=False):
25 return {'transform_mode': self.transform_mode}
28 def generate_out_df(media_type="", bin_stream=b""):
29 # munge stream and mimetype into input sample
30 return pd.DataFrame([[media_type, bin_stream]], columns=[FaceDetectTransform.COL_IMAGE_MIME, FaceDetectTransform.COL_IMAGE_DATA])
33 def generate_in_df(idx=FaceDetectTransform.VAL_REGION_IMAGE_ID, x=0, y=0, w=0, h=0, image=0, bin_stream=b"", media=""):
34 return pd.DataFrame([[idx,x,y,w,h,image,media,bin_stream]],
35 columns=[FaceDetectTransform.COL_REGION_IDX, FaceDetectTransform.COL_FACE_X, FaceDetectTransform.COL_FACE_Y,
36 FaceDetectTransform.COL_FACE_W, FaceDetectTransform.COL_FACE_H,
37 FaceDetectTransform.COL_IMAGE_IDX, FaceDetectTransform.COL_IMAGE_MIME,
38 FaceDetectTransform.COL_IMAGE_DATA])
41 def output_names_(self):
42 return [FaceDetectTransform.COL_IMAGE_MIME, FaceDetectTransform.COL_IMAGE_DATA]
45 def output_types_(self):
46 list_name = self.output_names_
47 list_type = self.classes_
48 return [{list_name[i]:list_type[i]} for i in range(len(list_name))]
58 def score(self, X, y=None):
61 def fit(self, X, y=None):
64 def predict(self, X, y=None):
66 Assumes a numpy array of [[mime_type, binary_string] ... ]
67 where mime_type is an image-specifying mime type and binary_string is the raw image bytes
70 # group by image index first
71 # decode image at region -1
72 # collect all remaining regions, operate with each on input image
73 # generate output image, send to output
76 image_region_list = RegionTransform.transform_raw_sample(X)
77 for image_data in image_region_list:
79 img = image_data['data']
80 for r in image_data['regions']: # loop through regions
81 x_max = min(r[0]+r[2], img.shape[1])
82 y_max = min(r[1]+r[3], img.shape[0])
83 if self.transform_mode=="pixelate":
84 img[r[1]:y_max, r[0]:x_max] = \
85 RegionTransform.pixelate_image(img[r[1]:y_max, r[0]:x_max])
87 # for now, we hard code to jpg output; TODO: add more encoding output (or try to match source?)
88 img_binary = cv2.imencode(".jpg", img)[1].tostring()
89 img_mime = 'image/jpeg' # image_data['mime']
91 df = RegionTransform.generate_out_df(media_type=img_mime, bin_stream=img_binary)
92 if dfReturn is None: # create an NP container for all images
93 dfReturn = df.reindex_axis(self.output_names_, axis=1)
95 dfReturn = dfReturn.append(df, ignore_index=True)
96 print("IMAGE {:} found {:} total rows".format(image_data['image'], len(df)))
100 def transform_raw_sample(raw_sample):
101 """Method to transform raw samples into dict of image and regions"""
102 raw_sample.sort_values([FaceDetectTransform.COL_IMAGE_IDX], ascending=True, inplace=True)
103 groupImage = raw_sample.groupby(FaceDetectTransform.COL_IMAGE_IDX)
106 for nameG, rowsG in groupImage:
107 local_image = {'image': -1, 'data': b"", 'regions': [], 'mime': ''}
108 image_row = rowsG[rowsG[FaceDetectTransform.COL_REGION_IDX]==FaceDetectTransform.VAL_REGION_IMAGE_ID]
109 if len(image_row) < 1: # must have at least one image set
110 print("Error: RegionTransform could not find a valid image reference for image set {:}".format(nameG))
112 if not len(image_row[FaceDetectTransform.COL_IMAGE_DATA]): # must have valid image data
113 print("Error: RegionTransform expected image data, but found empty binary string {:}".format(nameG))
115 file_bytes = np.asarray(image_row[FaceDetectTransform.COL_IMAGE_DATA][0], dtype=np.uint8)
116 local_image['data'] = cv2.imdecode(file_bytes, cv2.IMREAD_COLOR)
117 local_image['image'] = nameG
118 local_image['mime'] = image_row[FaceDetectTransform.COL_IMAGE_MIME]
120 # now proceed to loop around regions detected
121 for index, row in rowsG.iterrows():
122 if row[FaceDetectTransform.COL_REGION_IDX]!=FaceDetectTransform.VAL_REGION_IMAGE_ID: # skip bad regions
123 local_image['regions'].append([row[FaceDetectTransform.COL_FACE_X], row[FaceDetectTransform.COL_FACE_Y],
124 row[FaceDetectTransform.COL_FACE_W], row[FaceDetectTransform.COL_FACE_H]])
125 return_set.append(local_image)
128 ################################################################
129 # image processing routines (using opencv)
131 # http://www.jeffreythompson.org/blog/2012/02/18/pixelate-and-posterize-in-processing/
133 def pixelate_image(img, blockSize=None):
134 if not img.shape[0] or not img.shape[1]:
136 if blockSize is None:
137 blockSize = round(max(img.shape[0], img.shape[2]) / 8)
138 ratio = (img.shape[1] / img.shape[0]) if img.shape[0] < img.shape[1] else (img.shape[0] / img.shape[1])
139 blockHeight = round(blockSize * ratio) # so that we cover all image
140 for x in range(0, img.shape[0], blockSize):
141 for y in range(0, img.shape[1], blockHeight):
142 max_x = min(x+blockSize, img.shape[0])
143 max_y = min(y+blockSize, img.shape[1])
144 fill_color = img[x,y] # img[x:max_x, y:max_y].mean()
145 img[x:max_x, y:max_y] = fill_color