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Building AI projects is a surefire way to showcase your skills and stand out in the tech landscape. Here’s a curated list of innovative AI projects:
1. Real-Time Lane Detection
Enhance road safety by developing AI models to track lane markings in real time.
2. AirSketch: Hand-Drawing Calculator
Revolutionize education with a calculator that interprets air-drawn numbers.
3. Web Page Code Generation
Convert hand-drawn designs into HTML code using AI, bridging creativity with technology.
4. VegGPT: Food Detection & Recipe Generator
Use computer vision and AI to detect food and generate healthy recipes instantly.
5. How to Train PaddleOCR
Master training OCR models for multilingual and font-rich applications.
6. Cell Nuclei Segmentation
Automate cell nuclei detection to accelerate breakthroughs in medical research.
7. Image Captioning with Visual Attention
Build models that generate meaningful captions with a focus on visual details.
8. CS:GO Aimbot
Explore AI for gaming by creating an aimbot and enhancing cheat detection systems.
9. ChatGPT StreamLit SEO Generator
Combine ChatGPT and StreamLit to develop tools that boost website SEO.
10. Pose Estimation Bicep Curl Counter
Create fitness apps that count bicep curls with AI-powered pose estimation.
You Should Know:
1. Real-Time Lane Detection with OpenCV & Python
import cv2
import numpy as np
def detect_lanes(frame):
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
blur = cv2.GaussianBlur(gray, (5, 5), 0)
edges = cv2.Canny(blur, 50, 150)
lines = cv2.HoughLinesP(edges, 1, np.pi/180, 50, maxLineGap=50)
for line in lines:
x1, y1, x2, y2 = line[bash]
cv2.line(frame, (x1, y1), (x2, y2), (0, 255, 0), 5)
return frame
cap = cv2.VideoCapture('road.mp4')
while True:
ret, frame = cap.read()
if not ret:
break
lane_frame = detect_lanes(frame)
cv2.imshow('Lane Detection', lane_frame)
if cv2.waitKey(1) == ord('q'):
break
cap.release()
cv2.destroyAllWindows()
2. Training PaddleOCR for Multilingual Text Recognition
Install PaddleOCR
pip install paddleocr paddlepaddle
Run OCR on an image
from paddleocr import PaddleOCR
ocr = PaddleOCR(use_angle_cls=True, lang='en')
result = ocr.ocr('image.jpg', cls=True)
for line in result:
print(line)
3. Pose Estimation with MediaPipe
import cv2
import mediapipe as mp
mp_pose = mp.solutions.pose
pose = mp_pose.Pose()
cap = cv2.VideoCapture(0)
while cap.isOpened():
success, image = cap.read()
if not success:
continue
results = pose.process(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
if results.pose_landmarks:
mp.solutions.drawing_utils.draw_landmarks(image, results.pose_landmarks, mp_pose.POSE_CONNECTIONS)
cv2.imshow('Pose Estimation', image)
if cv2.waitKey(5) & 0xFF == 27:
break
cap.release()
4. StreamLit + ChatGPT SEO Generator
import streamlit as st
import openai
openai.api_key = 'your-api-key'
st.title('SEO Content Generator')
prompt = st.text_input('Enter SEO Topic:')
if prompt:
response = openai.Completion.create(
engine="text-davinci-003",
prompt=f"Generate SEO-friendly content about {prompt}",
max_tokens=500
)
st.write(response.choices[bash].text)
What Undercode Say:
AI-driven projects enhance technical portfolios by integrating computer vision, NLP, and automation. Mastering OpenCV, PaddleOCR, and MediaPipe empowers developers to build real-world solutions. Experiment with these tools, optimize models, and contribute to open-source AI repositories for career growth.
Expected Output:
- Lane detection video with highlighted paths.
- Extracted text from multilingual documents.
- Real-time human pose tracking.
- AI-generated SEO content in a web app.
References:
Reported By: Habib Shaikh – Hackers Feeds
Extra Hub: Undercode MoN
Basic Verification: Pass ✅
