CBSE AI Project — Image Recognition with Python 2025-26 | Class 10 & 12 | OpenCV MobileNet | Code 417 & 843

🤖 AI Project #1 — AI Logic School

Image Recognition
with Python & AI

Build a real AI system that detects and identifies objects in images — step by step, with full working code. For CBSE Class 10 and Class 12.

Class 10 — Beginner Class 12 — Advanced Python · OpenCV · MobileNet CBSE Code 417 & 843

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CBSE AI Project | Class 10 (Code 417) & Class 12 (Code 843) | Session 2025-26
This AI project covers Image Recognition using Python, OpenCV and MobileNet neural network. Two versions — beginner (Class 10) and advanced (Class 12) — both with full working code and line-by-line explanation.

// concept

What is Image Recognition?

Image recognition is the ability of a computer to identify objects, people, places, or actions in images using Artificial Intelligence. It is one of the most powerful and widely used AI applications in the world today.

In CBSE AI curriculum (Code 417 and 843), image recognition is a key topic in the Computer Vision unit. Building this project demonstrates your understanding of AI Project Cycle, data preprocessing, model usage, and evaluation.

💡 Real-world examples you already use every day

📸 Google Photos — automatically groups photos by person's face using face recognition

🚗 Self-driving cars — detect pedestrians, traffic lights, road signs in real time

🏥 Medical AI — detect tumors and abnormalities in X-ray and MRI scans

📦 Amazon Go stores — detect which products you pick up without any checkout

📱 Face Unlock — your phone's camera recognizes your face in milliseconds

Class 10 Version — Beginner

• Detect colours in an image
• Find dominant colour
• Use OpenCV and NumPy
• No neural network needed
• ~35 lines of code
• Perfect for Code 417

Class 12 Version — Advanced

• Object detection with labels
• Confidence score shown
• MobileNet neural network
• Bounding boxes drawn
• ~65 lines of code
• Perfect for Code 843

---

// class 10 project

Project 1 — Colour & Shape Detector Beginner

In this project we use Python and OpenCV to detect colours in any image. This covers Computer Vision concepts from the CBSE Class 10 AI syllabus — no machine learning required, perfect for beginners!

// step 1 — install libraries

Open Command Prompt and run these one by one:

pip install opencv-python pip install numpy pip install matplotlib

// step 2 — full code

colour_detector.py

# AI Logic School — Class 10 Image Recognition Project
# Colour Detector using OpenCV | CBSE Code 417

import cv2
import numpy as np
import matplotlib.pyplot as plt

# Step 1: Load image — replace 'photo.jpg' with your file
image = cv2.imread('photo.jpg')
image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)

# Step 2: Convert to HSV colour space for easier detection
hsv = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)

# Step 3: Define HSV ranges for Red, Blue, Green
lower_red   = np.array([0,   120, 70]);  upper_red   = np.array([10,  255, 255])
lower_blue  = np.array([100, 150, 50]);  upper_blue  = np.array([130, 255, 255])
lower_green = np.array([40,  70,  70]);  upper_green = np.array([80,  255, 255])

# Step 4: Create masks for each colour
mask_red   = cv2.inRange(hsv, lower_red,   upper_red)
mask_blue  = cv2.inRange(hsv, lower_blue,  upper_blue)
mask_green = cv2.inRange(hsv, lower_green, upper_green)

# Step 5: Apply masks to show detected colour only
result_red   = cv2.bitwise_and(image_rgb, image_rgb, mask=mask_red)
result_blue  = cv2.bitwise_and(image_rgb, image_rgb, mask=mask_blue)
result_green = cv2.bitwise_and(image_rgb, image_rgb, mask=mask_green)

# Step 6: Count detected pixels for each colour
red_px   = cv2.countNonZero(mask_red)
blue_px  = cv2.countNonZero(mask_blue)
green_px = cv2.countNonZero(mask_green)

print(f"Red pixels   : {red_px}")
print(f"Blue pixels  : {blue_px}")
print(f"Green pixels : {green_px}")

# Step 7: Find the dominant colour
colours  = {'Red': red_px, 'Blue': blue_px, 'Green': green_px}
dominant = max(colours, key=colours.get)
print(f"\n✅ Dominant colour: {dominant}")

# Step 8: Display all results
fig, axes = plt.subplots(1, 4, figsize=(16, 4))
axes[0].imshow(image_rgb);    axes[0].set_title('Original')
axes[1].imshow(result_red);   axes[1].set_title('Red Detected')
axes[2].imshow(result_blue);  axes[2].set_title('Blue Detected')
axes[3].imshow(result_green); axes[3].set_title('Green Detected')
for ax in axes: ax.axis('off')
plt.suptitle(f'Dominant Colour: {dominant}', fontsize=14, fontweight='bold')
plt.tight_layout()
plt.show()

▶ EXPECTED OUTPUT

Red pixels   : 3420
Blue pixels  : 8901
Green pixels : 2150

✅ Dominant colour: Blue

// line-by-line explanation

Line / Function	What it does
cv2.imread()	Loads your image file into Python as a matrix (array) of pixel values
cvtColor(BGR2HSV)	Converts image to HSV colour space — easier to detect colours than RGB
np.array([...])	Defines the lower and upper HSV range for each colour to detect
cv2.inRange()	Creates a mask — white pixels where colour is found, black everywhere else
bitwise_and()	Applies the mask to show only the detected colour on original image
countNonZero()	Counts how many pixels of each colour were found in the image
max(colours)	Finds which colour appeared the most — that becomes the dominant colour
plt.subplots()	Creates a 4-panel figure to display original image + 3 colour results

🎯 Try It Yourself — Class 10 Challenges

1. Add detection for Yellow colour (HSV lower: [20,100,100], upper: [30,255,255])
2. Print what percentage of the total image each colour covers
3. Save the result image using cv2.imwrite('result.jpg', image)
4. Try on 3 different images — a sunset, a forest, a school photo

---

// class 12 project

Project 2 — Object Detection with AI Advanced

Now we go deeper. We use a pre-trained neural network called MobileNet SSD to detect and label real objects in any image — like person, car, dog, laptop — with confidence scores shown on screen.

🧠 How the neural network works

MobileNet SSD is trained on 80 different object types (COCO dataset). It was trained by Google on millions of images. We just load the already-trained model — no training needed from scratch!

It outputs three things for each detection: bounding box coordinates (where the object is) + class label (what the object is) + confidence score (how sure the AI is, 0–100%)

This is an example of Transfer Learning — a key CBSE Class 12 AI concept. We use Google's pre-trained model and apply it to our own images.

// step 1 — install libraries

Open Command Prompt and run:

pip install opencv-python pip install numpy matplotlib

// step 2 — full code

object_detector.py

# AI Logic School — Class 12 Image Recognition Project
# Object Detection using MobileNet SSD | CBSE Code 843

import cv2
import numpy as np
import matplotlib.pyplot as plt
import urllib.request
import os

# 80 object labels from COCO dataset
LABELS = [
    "background", "person", "bicycle", "car", "motorcycle",
    "airplane", "bus", "train", "truck", "boat",
    "traffic light", "fire hydrant", "stop sign",
    "bench", "bird", "cat", "dog", "horse", "sheep",
    "cow", "elephant", "bear", "zebra", "giraffe",
    "backpack", "umbrella", "handbag", "suitcase",
    "bottle", "cup", "fork", "knife", "spoon", "bowl",
    "banana", "apple", "sandwich",
    "chair", "couch", "laptop", "cell phone", "book"
]
COLOURS = np.random.uniform(50, 255, size=(len(LABELS), 3))

# Download model files (runs only once, ~25 MB)
MODEL_URL = "https://github.com/chuanqi305/MobileNet-SSD/raw/master/MobileNetSSD_deploy.caffemodel"
PROTO_URL = "https://raw.githubusercontent.com/chuanqi305/MobileNet-SSD/master/MobileNetSSD_deploy.prototxt"

if not os.path.exists("model.caffemodel"):
    print("📥 Downloading model... (first time only)")
    urllib.request.urlretrieve(MODEL_URL, "model.caffemodel")
    urllib.request.urlretrieve(PROTO_URL, "model.prototxt")
    print("✅ Model downloaded!")

# Load the pre-trained neural network
print("🧠 Loading neural network...")
net = cv2.dnn.readNetFromCaffe("model.prototxt", "model.caffemodel")
print("✅ Model loaded!")

# Load and prepare image
image = cv2.imread("photo.jpg")   # replace with your image
image = cv2.resize(image, (600, 400))
(H, W) = image.shape[:2]

# Convert image to blob format the network understands
blob = cv2.dnn.blobFromImage(
    cv2.resize(image, (300, 300)),
    0.007843, (300, 300), 127.5
)

# Run image through the neural network
net.setInput(blob)
detections = net.forward()   # AI "thinks" here

# Draw bounding boxes on detected objects
found = []
for i in range(detections.shape[2]):
    confidence = detections[0, 0, i, 2]
    if confidence > 0.50:   # only show >50% confident detections
        idx    = int(detections[0, 0, i, 1])
        label  = LABELS[idx]
        colour = [int(c) for c in COLOURS[idx]]
        box    = detections[0, 0, i, 3:7] * np.array([W, H, W, H])
        (sX, sY, eX, eY) = box.astype("int")
        cv2.rectangle(image, (sX, sY), (eX, eY), colour, 2)
        text = f"{label}: {confidence*100:.1f}%"
        cv2.putText(image, text, (sX, sY-8),
                    cv2.FONT_HERSHEY_SIMPLEX, 0.5, colour, 2)
        found.append(f"  • {label} ({confidence*100:.1f}% confidence)")

# Print and display results
print(f"\n🎯 Objects detected: {len(found)}")
for obj in found: print(obj)

result_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
plt.figure(figsize=(10, 6))
plt.imshow(result_rgb)
plt.title(f"Objects Detected: {len(found)}", fontsize=14)
plt.axis('off')
plt.tight_layout()
plt.show()

▶ EXPECTED OUTPUT

📥 Downloading model... (first time only)
✅ Model downloaded!
🧠 Loading neural network...
✅ Model loaded!

🎯 Objects detected: 3
  • person   (94.2% confidence)
  • laptop   (87.6% confidence)
  • chair    (71.3% confidence)

// line-by-line explanation

Line / Concept	What it does
LABELS list	80 object names the model was trained to recognise — from person to giraffe
readNetFromCaffe()	Loads pre-trained MobileNet neural network weights from the downloaded file
blobFromImage()	Converts image to exact format (300×300, normalised) the network was designed for
net.forward()	The AI "thinks" — image passes through all neural network layers, predictions come out
detections[0,0,i,2]	Confidence score for detection i — how sure the AI is (0.0 to 1.0)
confidence > 0.50	Only show results where AI is more than 50% confident — reduces false detections
detections[...,3:7]	Bounding box as fractions (0-1) of image size — multiply by W and H for pixel coordinates
cv2.rectangle()	Draws the coloured box around each detected object
cv2.putText()	Writes the label name and confidence % above each bounding box

🎯 Try It Yourself — Class 12 Challenges

1. Change confidence threshold from 0.50 to 0.70 — what happens to number of detections?
2. Count how many people specifically are in the image and print that count
3. Save the result image with bounding boxes using cv2.imwrite('result.jpg', image)
4. Try the code on a classroom photo — can it detect students and chairs?
5. Extension: Modify code to use webcam using cv2.VideoCapture(0)

---

// viva & exam questions

Important Viva & Board Exam Questions

Q1. What is Image Recognition in AI?

Image recognition is the ability of AI to identify objects, people, or actions in images. It uses Computer Vision techniques and neural networks trained on millions of labeled images.

Q2. What is OpenCV? Why is it used?

OpenCV (Open Source Computer Vision) is a Python library for image processing tasks — reading images, colour detection, drawing shapes, applying filters, and running neural networks on images.

Q3. What is HSV colour space? Why use it over RGB?

HSV (Hue, Saturation, Value) separates colour information from brightness. This makes colour detection more reliable under different lighting conditions compared to RGB.

Q4. What is a pre-trained model? Give one example.

A pre-trained model is a neural network already trained on a large dataset. We can directly use it without training from scratch. Example: MobileNet SSD — trained by Google on 80 object types.

Q5. What is a confidence score in object detection?

A confidence score (0 to 1) tells how certain the AI is about its detection. A score of 0.94 means 94% confident. We typically only show detections above 50% confidence to reduce errors.

Q6. How does this project demonstrate the AI Project Cycle?

Problem Scoping: detect objects in images. Data Acquisition: use photo.jpg. Data Exploration: view image pixels. Modeling: run MobileNet neural network. Evaluation: check confidence scores and bounding boxes.

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AI Logic School · AI Project: Image Recognition · CBSE Class 10 & 12 · Code 417 & 843 · 2025-26

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