more cleanup

ammico/cropposts.py

@@ -146,7 +146,9 @@ def crop_posts_image(
 ):
     """
     get file lists from dir and sub dirs
-    ref_view：ref_view for crop the posts images
+
+
+    ref_view： ref_view for crop the posts images
     view: posts image that need cropping
     rte： None - not cropped, or (crop_view, number of matches)
     """
@@ -185,7 +187,7 @@ def crop_posts_image(
 def get_file_list(dir, filelist, ext=None, convert_unix=True):
     """
     get file lists from dir and sub dirs
-    dir：root dir for file lists
+    dir： root dir for file lists
     ext: file extension
     rte： File list
     """
@@ -252,11 +254,9 @@ def crop_posts_from_files(
 
 def test_crop_from_file():
     # Load images
-    # view1 = np.array(Image.open("data/ref/ref-00.png")) / 255
-    # view2 = np.array(Image.open("data/napsa/100539_ben.png")) / 255
     view1 = np.array(Image.open("data/ref/ref-06.png"))
     view2 = np.array(Image.open("data/napsa/102956_eng.png"))
-    crop_view, match_num = crop_posts_image(view1, view2, plt_match=True, plt_crop=True)
+    crop_view, _ = crop_posts_image(view1, view2, plt_match=True, plt_crop=True)
     cv2.imwrite("data/crop_100489_ind.png", crop_view)
 
 
@@ -267,10 +267,3 @@ def test_crop_from_folder():
     crop_posts_from_files(
         ref_dir, crop_dir, save_crop_dir, plt_match=False, plt_crop=False
     )
-
-
-# do tests:
-
-# test_crop_from_file()
-
-# test_crop_from_folder()

ammico/data/map_test_set.json

@@ -1,127 +0,0 @@
-{
-    "v9_4": {
-        "order": 169,
-        "variable_label": "4=PICTURE_SPECIFIC_VisualONLY",
-        "variable_explanation": "Person visible",
-        "variable_coding": "Bool",
-        "variable_comment": "Yes if there's someone shown",
-        "variable_mydict": "face",
-        "value_mydict": "Yes"
-    },
-    "v9_5a": {
-        "order": 170,
-        "variable_label": "5a=PICTURE_SPECIFIC_VisualONLY",
-        "variable_explanation": "More than one person shown",
-        "variable_coding": "Bool",
-        "variable_comment": "Yes if there are several individuals who appear in the post (do not count profile pictures)",
-        "variable_mydict": "multiple_faces",
-        "value_mydict": "Yes"
-    },
-    "v9_5b": {
-        "order": 171,
-        "variable_label": "5b=PICTURE_SPECIFIC_VisualONLY",
-        "variable_explanation": "How many people shown?",
-        "variable_coding": "Int",
-        "variable_comment": "If more than 15, put 99",
-        "variable_mydict": "no_faces",
-        "value_mydict": "0"
-    },
-    "v9_6": {
-        "order": 172,
-        "variable_label": "6=PICTURE_SPECIFIC_VisualONLY",
-        "variable_explanation": "Face fully visible",
-        "variable_coding": "Bool",
-        "variable_comment": "Yes if you can see all their face (no mask on)",
-        "variable_mydict": "wears_mask",
-        "value_mydict": "No"
-    },
-    "v9_7": {
-        "order": 173,
-        "variable_label": "7=PICTURE_SPECIFIC_VisualONLY",
-        "variable_explanation": "Face ONLY partially visible",
-        "variable_coding": "Bool",
-        "variable_comment": "Yes if you can only see part of their face, including when they are wearing a mask",
-        "variable_mydict": "wears_mask",
-        "value_mydict": "Yes"
-    },
-    "v9_8": {
-        "order": 174,
-        "variable_label": "8=PICTURE_SPECIFIC_VisualONLY",
-        "variable_explanation": "Facial positive expression",
-        "variable_coding": "Bool",
-        "variable_comment": "Yes if they display some kind of positive facial expression (smiling, happy, relieved, hopeful etc.)",
-        "variable_mydict": "emotion (category)",
-        "value_mydict": "Positive"
-    },
-    "v9_8a": {
-        "order": 175,
-        "variable_label": "8a=PICTURE_SPECIFIC_VisualONLY",
-        "variable_explanation": "Positive expression: happiness",
-        "variable_coding": "Bool",
-        "variable_comment": "Yes if they display happiness",
-        "variable_mydict": "emotion",
-        "value_mydict": "happy"
-    },
-    "v9_9": {
-        "order": 176,
-        "variable_label": "9=PICTURE_SPECIFIC_VisualONLY",
-        "variable_explanation": "Facial negative expression",
-        "variable_coding": "Bool",
-        "variable_comment": "Yes if they display some kind of negative facial expression (crying, showing ager, fear, disgust etc.)",
-        "variable_mydict": "emotion (category)",
-        "value_mydict": "Negative"
-    },
-    "v9_10": {
-        "order": 177,
-        "variable_label": "10=PICTURE_SPECIFIC_VisualONLY",
-        "variable_explanation": "Negative expression: anxiety",
-        "variable_coding": "Bool",
-        "variable_comment": "Yes if they show fear or anxiety. If you can't tell, choose No=0",
-        "variable_mydict": "emotion",
-        "value_mydict": "fear"
-    },
-    "v9_11": {
-        "order": 178,
-        "variable_label": "11=PICTURE_SPECIFIC_VisualONLY",
-        "variable_explanation": "Negative expression: anger",
-        "variable_coding": "Bool",
-        "variable_comment": "Yes if they show anger or outrage. If you can't tell, choose No=0",
-        "variable_mydict": "emotion",
-        "value_mydict": "angry"
-    },
-    "v9_12": {
-        "order": 179,
-        "variable_label": "12=PICTURE_SPECIFIC_VisualONLY",
-        "variable_explanation": "Negative expression: disgust",
-        "variable_coding": "Bool",
-        "variable_comment": "Yes if they show disgust. If you can't tell, choose No=0",
-        "variable_mydict": "emotion",
-        "value_mydict": "disgust"
-    },
-    "v9_13": {
-        "order": 180,
-        "variable_label": "13=PICTURE_SPECIFIC_VisualONLY",
-        "variable_explanation": "Negative expression: other, specify",
-        "variable_coding": "Bool",
-        "variable_comment": "Yes if they show any other negative emotion, please specify. If you can't tell, choose No=0",
-        "variable_mydict": "emotion",
-        "value_mydict": "sad"
-    },
-    "v9_13_text": {
-        "order": 181,
-        "variable_label": "13=PICTURE_SPECIFIC_VisualONLY",
-        "variable_explanation": "Negative expression: other, specify",
-        "variable_coding": "Str",
-        "variable_mydict": "emotion",
-        "value_mydict": ""
-    },
-    "v11_3": {
-        "order": 189,
-        "variable_label": "111_3=PICTURE_SPECIFIC_VisualONLY",
-        "variable_explanation": "Respect of the rules",
-        "variable_coding": "Bool",
-        "variable_comment": "Yes if the post shows mask wearing, vaccine taking, social distancing, any proof of respecting the rules",
-        "variable_mydict": "wears_mask",
-        "value_mydict": "Yes"
-    }
-}

ammico/objects_cvlib.py

@@ -1,7 +1,5 @@
 import cv2
 import cvlib as cv
-import numpy as np
-from PIL import Image
 
 
 def objects_from_cvlib(objects_list: list) -> dict:

ammico/test/test_faces.py

@@ -1,6 +1,5 @@
 import ammico.faces as fc
 import json
-import pytest
 
 
 def test_analyse_faces(get_path):

ammico/text.py

@@ -113,7 +113,6 @@ class TextDetector(utils.AnalysisMethod):
         self.subdict["text_english_correct"] = str(self.textblob.correct())
 
     def sentiment_analysis(self):
-        # self.subdict["sentiment"] = self.doc._.blob.sentiment_assessments.assessments
         # polarity is between [-1.0, 1.0]
         self.subdict["polarity"] = self.doc._.blob.polarity
         # subjectivity is a float within the range [0.0, 1.0]