67 lines
1.5 KiB
Python
67 lines
1.5 KiB
Python
import cv2
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import numpy as np
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import json
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from flask import Flask, url_for
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app = Flask("Kotely")
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@app.route('/')
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def api_root():
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return 'Welcome'
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@app.route('/image/<path:imagepath>')
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def info(imagepath):
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return recognition("/"+imagepath)
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def get_output_layers(net):
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layer_names = net.getLayerNames()
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output_layers = [layer_names[i[0] - 1] for i in net.getUnconnectedOutLayers()]
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return output_layers
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def recognition(photo):
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image = cv2.imread(photo)
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Width = image.shape[1]
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Height = image.shape[0]
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scale = 0.00392
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with open("yolov3.txt", 'r') as f:
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classes = [line.strip() for line in f.readlines()]
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COLORS = np.random.uniform(0, 255, size=(len(classes), 3))
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net = cv2.dnn.readNet("yolov3.weights", "yolov3.cfg")
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blob = cv2.dnn.blobFromImage(image, scale, (416, 416), (0, 0, 0), True, crop=False)
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net.setInput(blob)
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outs = net.forward(get_output_layers(net))
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class_ids = []
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confidences = []
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boxes = []
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conf_threshold = 0.5
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nms_threshold = 0.4
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for out in outs:
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for detection in out:
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scores = detection[5:]
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class_id = np.argmax(scores)
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confidence = scores[class_id]
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if confidence > 0.5:
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class_ids.append(class_id)
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data = '{"path":"'+photo+'"}'
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js = json.loads(data)
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if 15 in class_ids:
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js.update({"cat":1})
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return js
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else:
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js.update({"cat": 0})
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return js
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if __name__ == '__main__':
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app.run(port="8809")
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