Real-Time Object Detection OpenCV Python With Source Code
The Real-Time Object Detection OpenCV Python was developed using Python OpenCV, This opencv realtime object detection script is a simple experimental tool to detect common objects (COCO) easily with your built-in webcam. It uses opencv’s readNet method and uses the external yolov3-tiny model (which can be upgraded to the full sized model). Opencv’s readNet method only runs on CPU (and not GPU), is very intensive, and therefore, it will be not be optimal for big AI projects.
A Object Detection OpenCV Python implements an image and video object detection classifier using pretrained yolov3 models. The yolov3 models are taken from the official yolov3 paper which was released in 2018. The yolov3 implementation is from darknet. Also, this project implements an option to perform classification real-time using the webcam.
In this Python OpenCV Project also includes a downloadable Python Project With Source Code for free, just find the downloadable source code below and click to start downloading.
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To start executing Real-Time Object Detection OpenCV Python With Source Code, make sure that you have installed Python 3.9 and PyCharm in your computer.
Real-Time Object Detection OpenCV Python With Source Code : Steps on how to run the project
Time needed: 5 minutes.
These are the steps on how to run Real-Time Object Detection OpenCV Python With Source Code
- Step 1: Download the given source code below.
First, download the given source code below and unzip the source code.
- Step 2: Import the project to your PyCharm IDE.
Next, import the source code you’ve download to your PyCharm IDE.
- Step 3: Run the project.
last, run the project with the command “py main.py”
import numpy as np
Complete Source Code
import numpy as np
# Load the YOLO model
net = cv2.dnn.readNet("./weights/yolov3-tiny.weights", "./configuration/yolov3-tiny.cfg")
classes = 
with open("./configuration/coco.names", "r") as f:
classes = [line.strip() for line in f.readlines()]
layer_names = net.getLayerNames()
output_layers = [layer_names[i - 1] for i in net.getUnconnectedOutLayers()]
colors = np.random.uniform(0, 255, size=(len(classes), 3))
# Load webcam
cap = cv2.VideoCapture(0)
font = cv2.FONT_HERSHEY_SIMPLEX
starting_time = time.time()
frame_id = 0
# Read webcam
_, frame = cap.read()
frame_id += 1
height, width, channels = frame.shape
# Detecting objects
blob = cv2.dnn.blobFromImage(frame, 0.00392, (416, 416), (0, 0, 0), True, crop=False)
outs = net.forward(output_layers)
# Visualising data
class_ids = 
confidences = 
boxes = 
for out in outs:
for detection in out:
scores = detection[5:]
class_id = np.argmax(scores)
confidence = scores[class_id]
if confidence > 0.1:
# Object detected
center_x = int(detection * width)
center_y = int(detection * height)
w = int(detection * width)
h = int(detection * height)
# Rectangle coordinates
x = int(center_x - w / 2)
y = int(center_y - h / 2)
boxes.append([x, y, w, h])
indexes = cv2.dnn.NMSBoxes(boxes, confidences, 0.8, 0.3)
for i in range(len(boxes)):
if i in indexes:
x, y, w, h = boxes[i]
label = str(classes[class_ids[i]])
confidence = confidences[i]
color = colors[class_ids[i]]
cv2.rectangle(frame, (x, y), (x + w, y + h), color, 2)
cv2.putText(frame, label + " " + str(round(confidence, 2)), (x, y + 30), font, 3, color, 3)
elapsed_time = time.time() - starting_time
fps = frame_id / elapsed_time
cv2.putText(frame, "FPS: " + str(round(fps, 2)), (40, 670), font, .7, (0, 255, 255), 1)
cv2.putText(frame, "press [esc] to exit", (40, 690), font, .45, (0, 255, 255), 1)
key = cv2.waitKey(1)
if key == 27:
print("[button pressed] ///// [esc].")
print("[feedback] ///// Videocapturing succesfully stopped")
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Download Source Code below
This project implements an image and video object detection classifier using pretrained yolov3 models. The yolov3 models are taken from the official yolov3 paper which was released in 2018. The yolov3 implementation is from darknet. Also, this project implements an option to perform classification real-time using the webcam.
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