# Yolov3 and darknet problem

I developed my custom object detector using tiny yolo and darknet. It work great, but I need of one specific features:
the network outputs bounding boxes are each represented by a vector of number of classes + 5 elements. The first 4 elements represent the center_x, center_y, width and height. The fifth element represents the confidence that the bounding box encloses an object. The rest of the elements are the confidence associated with each class (i.e. object type). For each boxes, I need the confidence associated for each classes, but I have in output only max confindece, others confidence output are 0.

Example run :

print(scores)


returned

[0.        0.        0.5874982]


0.5874982 is the max confidence. It's the 3th class. But I don't understand because the others confidence are 0. Thanks for replay and I'm sorry for my bad english. This is code

 import cv2 as cv
import argparse
import sys
import numpy as np
import os.path

confThreshold = 0.5
nmsThreshold = 0.6
inpWidth = 416          #Width of network's input image
inpHeight = 416         #Height of network's input image

parser = argparse.ArgumentParser(description='Object Detection using YOLO in OPENCV')
args = parser.parse_args()

classesFile = "obj.names"
classes = None
with open(classesFile, 'rt') as f:

# Give the configuration and weight files for the model and load the network using them.
modelConfiguration = "yolov3-tiny-obj.cfg"
modelWeights = "pesi/pesi_3_classi_new/yolov3-tiny-obj_7050.weights"

net.setPreferableBackend(cv.dnn.DNN_BACKEND_OPENCV)
net.setPreferableTarget(cv.dnn.DNN_TARGET_CPU)

# Get the names of the output layers
def getOutputsNames(net):
layersNames = net.getLayerNames()
return [layersNames[i[0] - 1] for i in net.getUnconnectedOutLayers()]

# Draw the predicted bounding box
def drawPred(classId, conf, left, top, right, bottom):
if classId==1:
cv.rectangle(frame, (left, top), (right, bottom), (3, 14, 186), 3)
elif classId==0:
cv.rectangle(frame, (left, top), (right, bottom), (40, 198, 31), 3)
elif classId==2:
cv.rectangle(frame, (left, top), (right, bottom), (40, 198, 31), 3)

label = '%.2f' % conf

# Get the label for the class name and its confidence
if classes:
assert(classId < len(classes))
label = '%s:%s' % (classes[classId], label)

#Display the label at the top of the bounding box
labelSize, baseLine = cv.getTextSize(label, cv.FONT_HERSHEY_SIMPLEX, 0.5, 1)
top = max(top, labelSize[1])
cv.rectangle(frame, (left, top - round(1*labelSize[1])), (left + round(1*labelSize[0]), top + baseLine), (255, 255, 255), cv.FILLED)
cv.putText(frame, label, (left, top), cv.FONT_HERSHEY_SIMPLEX, 0.45, (0,0,0), 1)

# Remove the bounding boxes with low confidence using non-maxima suppression
def postprocess(frame, outs):
frameHeight = frame.shape[0]
frameWidth = frame.shape[1]

# Scan through all the bounding boxes output from the network and keep only the
# ones with high confidence scores. Assign the box's class label as the class with the highest score.
classIds = []
confidences = []
boxes = []
for out in outs:
for ...
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I think thats because yolo does "multiclass classification" - this means they train a single classifier for each class. So this means not all probabilities are necessarily distributed to sum up to 1. But i am only 70% sure about this.

If you have doubt on your code - just check the opencv yolov3 example. Opencv and yolo works great for me(in the newer versions)

( 2019-10-30 04:57:29 -0500 )edit

Yes, it work great! But I need know confidence for each classes for each object and not only max confidence..

( 2019-10-30 05:05:46 -0500 )edit

Uhmm study the opencv example again - you get back a vector with probabilities for each class.

( 2019-10-30 05:28:49 -0500 )edit