104 lines
4.3 KiB
Python
104 lines
4.3 KiB
Python
# Code adapted from Tensorflow Object Detection Framework
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# https://github.com/tensorflow/models/blob/master/research/object_detection/object_detection_tutorial.ipynb
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# Tensorflow Object Detection Detector
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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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from Application.Classifiers.ClassifierInterface import ClassifierInterface
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class Classifier(ClassifierInterface):
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def __init__(self):
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print("1")
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self.model_path = "./class1.pb"
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self.odapi = DetectorAPI(path_to_ckpt=self.model_path)
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self.threshold = 0.6
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def detect(self, stream):
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cap = cv2.VideoCapture(stream)
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img = None
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r, img = cap.read()
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if img is None:
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return img
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# scale the image down for faster processing
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scale_percent = 60 # percent of original size
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width = int(img.shape[1] * scale_percent / 100)
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height = int(img.shape[0] * scale_percent / 100)
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dim = (width, height)
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img = cv2.resize(img, dim)
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# get the results from the net
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boxes, scores, classes, num = self.odapi.process_frame(img)
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res = False
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for i in range(len(boxes)):
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# Class 1 represents human
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# draw recogniction boxes and return resulting image + true/false
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if classes[i] == 1:
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if scores[i] > self.threshold:
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box = boxes[i]
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cv2.rectangle(img, (box[1], box[0]), (box[3], box[2]), (255, 0, 0), 2)
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res = True
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return img, res
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else:
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res = False
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return img, res
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def tagLayers(self, layers):
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print("tagging")
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# Detector API can be changed out given the I/O remains the same
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# this way you can use a different N-Net if you like to
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class DetectorAPI:
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def __init__(self, path_to_ckpt):
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self.path_to_ckpt = path_to_ckpt
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self.detection_graph = tf.Graph()
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with self.detection_graph.as_default():
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od_graph_def = tf.GraphDef()
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with tf.gfile.GFile(self.path_to_ckpt, 'rb') as fid:
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serialized_graph = fid.read()
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od_graph_def.ParseFromString(serialized_graph)
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tf.import_graph_def(od_graph_def, name='')
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self.default_graph = self.detection_graph.as_default()
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self.sess = tf.Session(graph=self.detection_graph)
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# Definite input and output Tensors for detection_graph
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self.image_tensor = self.detection_graph.get_tensor_by_name('image_tensor:0')
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# Each box represents a part of the image where a particular object was detected.
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self.detection_boxes = self.detection_graph.get_tensor_by_name('detection_boxes:0')
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# Each score represent how level of confidence for each of the objects.
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# Score is shown on the result image, together with the class label.
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self.detection_scores = self.detection_graph.get_tensor_by_name('detection_scores:0')
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self.detection_classes = self.detection_graph.get_tensor_by_name('detection_classes:0')
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self.num_detections = self.detection_graph.get_tensor_by_name('num_detections:0')
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def process_frame(self, image):
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# Expand dimensions since the trained_model expects images to have shape: [1, None, None, 3]
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image_np_expanded = np.expand_dims(image, axis=0)
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# Actual detection.
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(boxes, scores, classes, num) = self.sess.run(
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[self.detection_boxes, self.detection_scores, self.detection_classes, self.num_detections],
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feed_dict={self.image_tensor: image_np_expanded})
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im_height, im_width,_ = image.shape
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boxes_list = [None for i in range(boxes.shape[1])]
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for i in range(boxes.shape[1]):
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boxes_list[i] = (
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int(boxes[0, i, 0] * im_height),
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int(boxes[0, i, 1] * im_width),
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int(boxes[0, i, 2] * im_height),
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int(boxes[0, i, 3] * im_width)
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)
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return boxes_list, scores[0].tolist(), [int(x) for x in classes[0].tolist()], int(num[0])
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def close(self):
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self.sess.close()
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self.default_graph.close()
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