63 lines
2.4 KiB
Python
63 lines
2.4 KiB
Python
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import numpy as np
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import tensorflow as tf
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import cv2
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import os
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import json
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from Application.Classifiers.ClassifierInterface import ClassifierInterface
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class Classifier(ClassifierInterface):
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def __init__(self):
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self.threshold = .5
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with open(os.path.join(os.path.dirname(__file__), "coco_map.json")) as file:
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mapping = json.load(file)
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self.classes = dict()
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for element in mapping:
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self.classes[element["id"]-1] = element["display_name"]
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self.net = cv2.dnn.readNet(os.path.join(os.path.dirname(
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__file__), "yolov4.weights"), os.path.join(os.path.dirname(__file__), "yolov4.cfg"))
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# self.net.setPreferableBackend(cv2.dnn.DNN_BACKEND_CUDA)
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# self.net.setPreferableTarget(cv2.dnn.DNN_TARGET_CUDA)
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self.layer_names = self.net.getLayerNames()
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self.outputlayers = [self.layer_names[i[0] - 1]
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for i in self.net.getUnconnectedOutLayers()]
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print("Classifier Initiated")
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def tagLayer(self, imgs):
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# get the results from the net
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results = []
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for i, contours in enumerate(imgs[19:20]):
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# print(i)
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for contour in contours:
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height, width, channels = contour.shape
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dim = max(height, width)
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if dim > 320:
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img2 = np.zeros(shape=[dim, dim, 3], dtype=np.uint8)
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else:
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img2 = np.zeros(shape=[320, 320, 3], dtype=np.uint8)
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img2[:height, :width] = contour
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blob = cv2.dnn.blobFromImage(
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img2, 1/256, (320, 320), (0, 0, 0), True, crop=False) # reduce 416 to 320
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self.net.setInput(blob)
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outs = self.net.forward(self.outputlayers)
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for out in outs:
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for detection in out:
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scores = detection
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class_id = np.argmax(scores)
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confidence = scores[class_id]
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if confidence > self.threshold:
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if self.classes[class_id] not in results:
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cv2.imshow("changes x", img2)
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cv2.waitKey(10) & 0XFF
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results.append(self.classes[class_id])
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#print(self.classes[x], score)
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return results
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