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* colors expression by KMean algorithm * object detection by imageai * object detection by cvlib * add encapsulation of object detection * remove encapsulation of objdetect v0 * objects expression to dict * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * added imageai to requirements * add objects to dictionary * update for AnalysisMethod baseline * add objects dection support explore_analysis display * extend python version of misinf to allow imageai * account for older python * use global functionality for dict to csv convert * update for docker build * docker will build now but ipywidgets still not working * test code * include test data folder in repo * add some sample images * load cvs labels to dict * add test data * retrigger checks * add map to human coding * get orders from dict, missing dep * add module to test accuracy * retrigger checks * retrigger checks * now removing imageai * removed imageai * move labelmanager to analyse * multiple faces in mydict * fix pre-commit issues * map mydict * hide imageai * objects default using cvlib, isolate and disable imageai * correct python version * refactor faces tests * refactor objects tests * sonarcloud issues * refactor utils tests * address code smells * update readme * update notebook without imageai Co-authored-by: Ma Xianghe <825074348@qq.com> Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: iulusoy <inga.ulusoy@uni-heidelberg.de>
115 строки
3.5 KiB
Python
115 строки
3.5 KiB
Python
from misinformation.utils import DownloadResource
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from misinformation.objects_cvlib import ObjectsMethod
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from misinformation.objects_cvlib import init_default_objects
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from imageai.Detection import ObjectDetection
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import cv2
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import os
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import pathlib
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def objects_from_imageai(detections: list) -> dict:
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objects = init_default_objects()
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for obj in detections:
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obj_name = obj["name"]
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objects[obj_name] = "yes"
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return objects
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def objects_symlink_processor(name):
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def _processor(fname, action, pooch):
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if not os.path.exists(os.path.dirname(name)):
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os.makedirs(os.path.dirname(name))
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if not os.path.exists(name):
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os.symlink(fname, name)
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return fname
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return _processor
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pre_model_path = pathlib.Path.home().joinpath(
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".misinformation", "objects", "resnet50_coco_best_v2.1.0.h5"
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)
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retina_objects_model = DownloadResource(
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url="https://github.com/OlafenwaMoses/ImageAI/releases/download/essentials-v5/resnet50_coco_best_v2.1.0.h5/",
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known_hash="sha256:6518ad56a0cca4d1bd8cbba268dd4e299c7633efe7d15902d5acbb0ba180027c",
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processor=objects_symlink_processor(pre_model_path),
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)
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class ObjectImageAI(ObjectsMethod):
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def __init__(self):
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# init imageai client
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retina_objects_model.get()
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if not os.path.exists(pre_model_path):
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print("Download retina objects model failed.")
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return
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self.imgai_client = ObjectDetection()
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self.imgai_client.setModelTypeAsRetinaNet()
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self.imgai_client.setModelPath(pre_model_path)
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self.imgai_client.loadModel()
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self.custom = self.imgai_client.CustomObjects(
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person=True,
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bicycle=True,
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car=True,
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motorcycle=True,
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airplane=True,
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bus=True,
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train=True,
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truck=True,
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boat=True,
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traffic_light=True,
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cell_phone=True,
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)
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def detect_objects_imageai(self, image_path, custom=True, min_prob=30):
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"""Localize objects in the local image.
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Args:
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image_path: The path to the local file.
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custom: If only detect user defined specific objects.
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min_prob: Minimum probability that we trust as objects.
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"""
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img = cv2.imread(image_path)
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if custom:
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box_img, detections = self.imgai_client.detectCustomObjectsFromImage(
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custom_objects=self.custom,
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input_type="array",
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input_image=img,
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output_type="array",
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minimum_percentage_probability=min_prob,
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)
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else:
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box_img, detections = self.imgai_client.detectObjectsFromImage(
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input_type="array",
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input_image=img,
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output_type="array",
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minimum_percentage_probability=min_prob,
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)
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objects = objects_from_imageai(detections)
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return objects
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def analyse_image_from_file(self, image_path):
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"""Localize objects in the local image.
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Args:
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image_path: The path to the local file.
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"""
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objects = self.detect_objects_imageai(image_path)
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return objects
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def analyse_image(self, subdict):
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"""Localize objects in the local image.
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Args:
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subdict: The dictionary for an image expression instance.
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"""
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objects = self.analyse_image_from_file(subdict["filename"])
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for key in objects:
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subdict[key] = objects[key]
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return subdict
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