Chapter 7: Computer Vision in Real-Time Applications (Django AI)
This chapter introduces real-time computer vision systems.
Here, AI learns to see, understand, and interpret visual data in real time.
Computer Vision is one of the most powerful applications of AI in the real world.
Real-time vision systems are used in:
security, healthcare, traffic systems, retail, automation, robotics, surveillance, and smart cities.
This chapter focuses on building live AI vision pipelines using Django and Python.
⭐ What is Real-Time Computer Vision?
- Live image input
- Video stream processing
- Real-time detection
- Instant AI inference
- Visual decision systems
⭐ Computer Vision System Architecture
Camera/Image → Preprocessing → AI Vision Model → Detection → Decision → Django API → System Action
⭐ Real-Time Vision Pipeline
Input → Image Processing → Vision Model → Prediction → Logic → Output
⭐ Image Processing Basics
Computer vision systems require preprocessing before AI inference.
import cv2
img = cv2.imread("image.jpg")
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
resized = cv2.resize(gray, (224, 224))
⭐ Simple Vision Logic Example
def vision_logic(value):
if value > 0.7:
return "Object Detected"
else:
return "No Object"
⭐ Django Image Processing API
Real-time image AI API using Django:
# views.py
from django.http import JsonResponse
from django.views.decorators.csrf import csrf_exempt
import json
import cv2
import numpy as np
@csrf_exempt
def vision_api(request):
if request.method == "POST":
file = request.FILES["image"]
img = np.frombuffer(file.read(), np.uint8)
img = cv2.imdecode(img, cv2.IMREAD_COLOR)
img = cv2.resize(img, (224,224))
# Simple logic simulation
mean_val = img.mean()
result = "Bright Image" if mean_val > 120 else "Dark Image"
return JsonResponse({"result": result})
⭐ URL Configuration
# urls.py
from django.urls import path
from .views import vision_api
urlpatterns = [
path("vision/", vision_api),
]
⭐ AI Vision Model Integration (Django)
# views.py
import tensorflow as tf
model = tf.keras.models.load_model("vision_model.h5")
@csrf_exempt
def ai_vision(request):
if request.method == "POST":
file = request.FILES["image"]
img = tf.keras.preprocessing.image.load_img(file, target_size=(224,224))
x = tf.keras.preprocessing.image.img_to_array(img)/255.0
x = np.expand_dims(x, axis=0)
pred = model.predict(x)
label = int(pred.argmax())
return JsonResponse({"class": label})
⭐ Real-Time Vision Use Cases
- Face recognition systems
- Traffic monitoring
- Medical imaging
- Retail analytics
- Security surveillance
- Industrial automation
⭐ Vision System Design Principles
- Low-latency processing
- Efficient models
- Fast image pipelines
- Scalable APIs
- Hardware optimization
⭐ Mini Practical Task
Build a Django-based vision system that:
- Accepts image input
- Processes image
- Applies AI logic/model
- Returns visual decision
# views.py
@csrf_exempt
def simple_vision(request):
if request.method == "POST":
file = request.FILES["image"]
img = np.frombuffer(file.read(), np.uint8)
img = cv2.imdecode(img, cv2.IMREAD_COLOR)
if img.mean() > 120:
result = "Bright Scene"
else:
result = "Dark Scene"
return JsonResponse({"scene": result})
📌 Chapter Outcome
- Understand real-time computer vision systems
- Build AI vision pipelines
- Create Django vision APIs
- Deploy vision AI services
- Design scalable AI vision systems
📌 Core Principle
Vision is data.
AI turns images into intelligence.
Real-time vision builds smart systems.
