Computer Vision questions in Google interviews. This article demonstrates the application of CV algorithms in practical scenarios through an image label detection problem. oavoservice helps you master CV interview essentials.
📋 Problem Scenario
Detect label positions on book spines for automated library management.
Input: Shelf image Output: Label position for each book (Bounding Box)
🎯 Solution
Image Preprocessing
import cv2
import numpy as np
def preprocess_image(image):
# Convert to grayscale
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
# Gaussian blur to reduce noise
blurred = cv2.GaussianBlur(gray, (5, 5), 0)
# Edge detection
edges = cv2.Canny(blurred, 50, 150)
return edges
def detect_labels(image):
edges = preprocess_image(image)
# Find contours
contours, _ = cv2.findContours(
edges,
cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE
)
labels = []
for contour in contours:
# Get bounding rect
x, y, w, h = cv2.boundingRect(contour)
# Filter: Labels are usually rectangular and within certain size range
if w > 20 and h > 30 and 0.3 < w/h < 3:
labels.append((x, y, w, h))
return labels
💼 How oavoservice Helps
CV Fundamentals - Common OpenCV Operations Algorithm Selection - Edge Detection and Contour Finding Parameter Tuning - Thresholding and Filtering Conditions
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Tags: #Google #ComputerVision #OpenCV #ImageProcessing #VOHelp #InterviewPrep #1point3acres
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