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Accuracy test (#32)
* Create ci.yml * include pytest * Update pyproject.toml * include pytest-cov * use approx in pytest * Update test_faces.py * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * add coverage yaml * reduce passing grade * use copy instead of symlink on windows * crude attempt at calculating deviations Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Этот коммит содержится в:
Inga Ulusoy
GitHub
родитель
cf226ce535
Коммит
37d07da98a
@ -77,13 +77,24 @@ class LabelManager:
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# substitute the values that are not boolean
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if self.map[key]["variable_coding"] != "Bool":
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mapped_subdict[key] = mydict_current
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# take only first value in lists - this applies to faces,
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# reported are up to three in a list, we compare only the
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# largest one here
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if isinstance(mydict_current, list):
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mapped_subdict[key] = 1 if mydict_current[0] == mydict_value else 0
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# also cut out the likelihood for detected emotion
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if isinstance(mydict_current[0], tuple):
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mapped_subdict[key] = (
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1 if mydict_current[0][0] == mydict_value else 0
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)
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mapped_dict[id] = mapped_subdict
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return mapped_dict
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if __name__ == "__main__":
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files = utils.find_files(
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path="/home/inga/projects/misinformation-project/misinformation/misinformation/test/data/Europe APRMAY20 visual data/cropped images"
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path="/home/inga/projects/misinformation-project/misinformation/misinformation/test/data/Europe APRMAY20 visual data/cropped images",
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limit=500,
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)
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mydict = utils.initialize_dict(files)
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# analyze faces
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@ -102,8 +113,35 @@ if __name__ == "__main__":
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orders = lm.get_orders()
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# map mydict to the specified variable names and values
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mydict_map = lm.map_dict(mydict)
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print(mydict_map)
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lm.filter_from_order([1, 2, 3] + orders)
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labels = lm.gen_dict()
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print(labels)
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comp = {}
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for key in labels.keys():
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if str(key) not in mydict_map:
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print("Key {} not found.".format(key))
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continue
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print("ref: {}".format(labels[key]))
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print("com: {}".format(mydict_map[str(key)]))
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for subkey in labels[key]:
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if type(labels[key][subkey]) != int:
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continue
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if type(mydict_map[str(key)][subkey]) != int:
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continue
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comp[subkey] = comp.get(subkey, 0) + abs(
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labels[key][subkey] - mydict_map[str(key)][subkey]
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)
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print("summary: ")
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# why v9_5a not there - bec reads in as float from the csv
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print(comp)
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# summary:
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# {'v9_4': 42, 'v9_5b': 1579, 'v9_6': 229, 'v9_7': 45, 'v9_8': 39, 'v9_8a': 31, 'v9_9': 58, 'v9_10': 33, 'v9_11': 22, 'v9_12': 2, 'v9_13': 24, 'v11_3': 39}
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# Important here is:
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# Overall positive - 'v9_8': 39 deviations
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# Overall negative - 'v9_9': 58
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# happy - 'v9_8a': 31
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# fear - 'v9_10': 33
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# angry - 'v9_11': 22
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# disgust - 'v9_12': 2
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# sad - 'v9_13': 24
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# respect of rules = wears mask - 'v11_3': 39
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@ -1,5 +1,5 @@
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{
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"V9_4": {
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"v9_4": {
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"order": 169,
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"variable_label": "4=PICTURE_SPECIFIC_VisualONLY",
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"variable_explanation": "Person visible",
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@ -8,7 +8,7 @@
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"variable_mydict": "face",
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"value_mydict": "Yes"
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},
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"V9_5a": {
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"v9_5a": {
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"order": 170,
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"variable_label": "5a=PICTURE_SPECIFIC_VisualONLY",
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"variable_explanation": "More than one person shown",
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@ -17,7 +17,7 @@
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"variable_mydict": "multiple_faces",
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"value_mydict": "Yes"
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},
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"V9_5b": {
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"v9_5b": {
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"order": 171,
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"variable_label": "5b=PICTURE_SPECIFIC_VisualONLY",
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"variable_explanation": "How many people shown?",
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@ -26,7 +26,7 @@
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"variable_mydict": "no_faces",
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"value_mydict": "0"
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},
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"V9_6": {
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"v9_6": {
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"order": 172,
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"variable_label": "6=PICTURE_SPECIFIC_VisualONLY",
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"variable_explanation": "Face fully visible",
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@ -35,7 +35,7 @@
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"variable_mydict": "wears_mask",
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"value_mydict": "No"
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},
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"V9_7": {
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"v9_7": {
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"order": 173,
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"variable_label": "7=PICTURE_SPECIFIC_VisualONLY",
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"variable_explanation": "Face ONLY partially visible",
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@ -44,7 +44,7 @@
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"variable_mydict": "wears_mask",
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"value_mydict": "Yes"
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},
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"V9_8": {
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"v9_8": {
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"order": 174,
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"variable_label": "8=PICTURE_SPECIFIC_VisualONLY",
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"variable_explanation": "Facial positive expression",
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@ -53,7 +53,7 @@
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"variable_mydict": "emotion (category)",
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"value_mydict": "Positive"
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},
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"V9_8a": {
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"v9_8a": {
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"order": 175,
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"variable_label": "8a=PICTURE_SPECIFIC_VisualONLY",
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"variable_explanation": "Positive expression: happiness",
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@ -62,7 +62,7 @@
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"variable_mydict": "emotion",
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"value_mydict": "happy"
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},
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"V9_9": {
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"v9_9": {
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"order": 176,
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"variable_label": "9=PICTURE_SPECIFIC_VisualONLY",
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"variable_explanation": "Facial negative expression",
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@ -71,7 +71,7 @@
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"variable_mydict": "emotion (category)",
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"value_mydict": "Negative"
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},
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"V9_10": {
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"v9_10": {
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"order": 177,
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"variable_label": "10=PICTURE_SPECIFIC_VisualONLY",
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"variable_explanation": "Negative expression: anxiety",
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@ -80,7 +80,7 @@
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"variable_mydict": "emotion",
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"value_mydict": "fear"
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},
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"V9_11": {
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"v9_11": {
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"order": 178,
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"variable_label": "11=PICTURE_SPECIFIC_VisualONLY",
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"variable_explanation": "Negative expression: anger",
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@ -89,7 +89,7 @@
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"variable_mydict": "emotion",
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"value_mydict": "angry"
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},
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"V9_12": {
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"v9_12": {
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"order": 179,
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"variable_label": "12=PICTURE_SPECIFIC_VisualONLY",
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"variable_explanation": "Negative expression: disgust",
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@ -98,7 +98,7 @@
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"variable_mydict": "emotion",
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"value_mydict": "disgust"
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},
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"V9_13": {
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"v9_13": {
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"order": 180,
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"variable_label": "13=PICTURE_SPECIFIC_VisualONLY",
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"variable_explanation": "Negative expression: other, specify",
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@ -107,7 +107,7 @@
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"variable_mydict": "emotion",
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"value_mydict": "sad"
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},
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"V9_13_text": {
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"v9_13_text": {
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"order": 181,
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"variable_label": "13=PICTURE_SPECIFIC_VisualONLY",
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"variable_explanation": "Negative expression: other, specify",
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@ -115,7 +115,7 @@
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"variable_mydict": "emotion",
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"value_mydict": ""
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},
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"V11_3": {
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"v11_3": {
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"order": 189,
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"variable_label": "111_3=PICTURE_SPECIFIC_VisualONLY",
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"variable_explanation": "Respect of the rules",
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@ -1,6 +1,7 @@
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import cv2
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import numpy as np
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import os
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import shutil
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import pathlib
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import ipywidgets
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@ -17,7 +18,12 @@ import misinformation.utils as utils
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def deepface_symlink_processor(name):
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def _processor(fname, action, pooch):
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if not os.path.exists(name):
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os.symlink(fname, name)
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# symlink does not work on windows
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# use copy if running on windows
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if not os.name == "nt":
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os.symlink(fname, name)
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else:
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shutil.copy(fname, name)
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return fname
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return _processor
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