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yolo.py
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yolo.py
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import numpy as np
import argparse
import cv2 as cv
import subprocess
import time
import os
from yolo_utils import infer_image, show_image
BASE_URL = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
BASE_URL = os.path.join(BASE_URL,'NAVIGATION_BLIND')
def detectobjects(frame, weights=os.path.join(BASE_URL,'yolov3.weights'), config=os.path.join(BASE_URL,'yolov3.cfg'), labels=os.path.join(BASE_URL,'coco-labels')):
print("entered")
confidence = 0.5
threshold = 0.3
labels = open(labels).read().strip().split("\n")
net = cv.dnn.readNetFromDarknet(config, weights)
layer_names = net.getLayerNames()
layer_names = [layer_names[i[0] - 1] for i in net.getUnconnectedOutLayers()]
frame_count = 0
# while True:
# grabbed, frame = vid.read()
# print(frame_count)
frame_count = frame_count + 1
# if width is None or height is None:
height, width = frame.shape[:2]
objects=[]
confidences, classids, idxs = infer_image(net, layer_names, height, width, frame, labels, confidence, threshold)
if len(idxs) > 0:
for i in idxs.flatten():
if(labels[classids[i]] not in objects):
objects.append(labels[classids[i]])
print ("[INFO] Cleaning up...")
return objects