#YoloV3 – Rapid Object detection with 5 lines of code ( @code and #Python for the win! )

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Hi !

Sometime ago, I wrote a simple Python class a wrapper for YoloV3. This allows me to write 5 lines of code to analyze an image. Once I use this class, I only press F5 in Visual Studio Code and it’s magic:

detecting images with 3 lines of code

A sample usage will be similar to this one. The only required parameter for the YoloV3 analyzer is the confidence. 0.5 is good enough for this demo:

# Bruno Capuano 2020
# simple implementation for YoloV3 object detection
import cv2
from yoloanalyzer import yoloV3_analyzer
image_path = "02.jpg"
# init Analyzer with confidence 50%
ya = yoloV3_analyzer(0.5)
# analyze and show image
image = cv2.imread(image_path)
newImage = ya.ImageProcess(image)
cv2.imshow("Rapid YoloV3 demo", newImage)
# wrap up

And the output is rocking, not only cats, also dogs and humans !

I added a couple of parameters to define if we want labels and bounding boxes in the output image. The same image without bounding boxes will be:

And finally, the main class to perform this. It’s a very simple one, and feel free to use it and remember that you must check the official YoloV3 repository to get the files:

  • coco.names
  • yolov3.cfg
  • yolov3.weights
# Bruno Capuano 2020
# performs object detection using YoloV3 in an image and return the processed image
import imghdr
import os
import numpy as np
import cv2
import time
class yoloV3_analyzer:
def __init__(self, confidence):
self.confidence = confidence
def InitYoloV3(self):
#global net, ln, LABELS
self.weights = "yolov3.weights"
self.config = "yolov3.cfg"
self.labelsPath = "coco.names"
self.LABELS = open(self.labelsPath).read().strip().split("\n")
self.COLORS = np.random.uniform(0, 255, size=(len(self.LABELS), 3))
self.net = cv2.dnn.readNetFromDarknet(self.config, self.weights)
self.ln = self.net.getLayerNames()
self.ln = [self.ln[i[0] 1] for i in self.net.getUnconnectedOutLayers()]
def ImageProcess(self, image, showLabels = True, showBoundingBox = True):
# Init YOLO if needed
if(self.net is None):
(H, W) = image.shape[:2]
frame = image.copy()
blob = cv2.dnn.blobFromImage(frame, 1 / 255.0, (416, 416), swapRB=True, crop=False)
starttime = time.time()
layerOutputs = self.net.forward(self.ln)
stoptime = time.time()
print("FPS: {:.4f}".format((stoptimestarttime)))
confidences = []
outline = []
class_ids = []
for output in layerOutputs:
for detection in output:
scores = detection[5:]
maxi_class = np.argmax(scores)
confidence = scores[maxi_class]
if confidence > self.confidence:
box = detection[0:4] * np.array([W, H, W, H])
(centerX, centerY, width, height) = box.astype("int")
x = int(centerX (width / 2))
y = int(centerY (height / 2))
outline.append([x, y, int(width), int(height)])
box_line = cv2.dnn.NMSBoxes(outline, confidences, 0.5, 0.3)
if len(box_line) > 0:
flat_box = box_line.flatten()
pairs = []
for i in flat_box:
(x, y) = (outline[i][0], outline[i][1])
(w, h) = (outline[i][2], outline[i][3])
x_plus_w = round(x+w)
y_plus_h = round(y+h)
label = str(self.LABELS[class_ids[i]])
color = self.COLORS[class_ids[i]]
if (showBoundingBox == True):
cv2.rectangle(frame, (x,y), (x_plus_w,y_plus_h), color, 2)
if (showLabels == True):
cv2.putText(frame, label, (x10,y10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 2)
return frame
# Yolo
net = (None)
ln = (None)
LABELS = (None)
frameno = 0
view raw YoloV3Analyzer.py hosted with ❤ by GitHub

Happy coding!


El Bruno