import cv2, numpy as np
image = cv2.imread('TT.jpeg')
#cv2.imshow('Image', image)
cv2.waitKey(0) # Wait indefinitely (0) for key press
cv2.destroyAllWindows()
resized = cv2.resize(image, (400, 400)) # Resize to 400x400 pixels
#cv2.imshow('Resized', resized)
cv2.waitKey(0)
cv2.destroyAllWindows()
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
#cv2.imshow('Gray Image', gray)
cv2.waitKey(0)
cv2.destroyAllWindows()
edges = cv2.Canny(gray, threshold1=50, threshold2=150)
#cv2.imshow('Edges', edges)
cv2.waitKey(0)
cv2.destroyAllWindows()
kernel = np.ones((5, 5), np.uint8)
eroded = cv2.erode(image, kernel, iterations=1)
dilated = cv2.dilate(image, kernel, iterations=1)
#cv2.imshow('Eroded', eroded); cv2.moveWindow("Eroded", 0, 0)
#cv2.imshow('Dilated', dilated); cv2.moveWindow("Dilated", 950, 0)
#cv2.waitKey(0)
#cv2.destroyAllWindows()
opened = cv2.dilate(eroded, kernel, iterations=1)
closed = cv2.erode(dilated, kernel, iterations=1)
#cv2.imshow('Opened', opened); cv2.moveWindow("Opened", 0, 500)
#cv2.imshow('Closed', closed); cv2.moveWindow("Closed", 950, 500)
cv2.waitKey(0)
cv2.destroyAllWindows()
equalized = cv2.equalizeHist(gray) # Increases contrast
#cv2.imshow('Equalized', equalized)
cv2.waitKey(0)
cv2.destroyAllWindows()
hsv = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)
#cv2.imshow('HSV', hsv)
cv2.waitKey(0)
cv2.destroyAllWindows()
image1 = cv2.imread('View1.png') # Uploaded to drive
image2 = cv2.imread('View2.png') # Uploaded to drive
#cv2.imshow('Image1', image1)
#cv2.imshow('Image2', image2)
stitcher = cv2.Stitcher.create()
(status, stitched_image) = stitcher.stitch([image1, image2])
if status == cv2.STITCHER_OK:
#cv2.imshow('Stitched Image', stitched_image)
cv2.waitKey(0)
cv2.destroyAllWindows()
else:
print("Image stitching failed. Status:", status)
face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')
image = cv2.imread('Crowd.jpg') # Uploaded to drive
gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
faces = face_cascade.detectMultiScale(gray_image, scaleFactor=1.1, minNeighbors=5)
for (x, y, w, h) in faces:
cv2.rectangle(image, (x, y), (x + w, y + h), (0, 255, 0), 2)
#cv2.imshow('Face Detection', image)
cv2.waitKey(0)
cv2.destroyAllWindows()
image = cv2.imread('Traffic signal.jpg')
image = cv2.imread('Traffic.png')
gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
edges = cv2.Canny(gray_image, threshold1=50, threshold2=150)
kernel = np.ones((5, 5), np.uint8)
dilated = cv2.dilate(edges, kernel, iterations=1)
contours, _ = cv2.findContours(dilated, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
for contour in contours:
if cv2.contourArea(contour) > 100:
x, y, w, h = cv2.boundingRect(contour)
cv2.rectangle(image, (x, y), (x + w, y + h), (0, 255, 0), 2)
cv2.imshow('Traffic Sign Detection', image)
cv2.waitKey(0)
cv2.destroyAllWindows()
from ultralytics import YOLO
model = YOLO("yolo11n.pt")
image = cv2.imread("Traffic.png") # Uploaded to drive
results = model(image)
for result in results:
for box in result.boxes:
class_id = int(box.cls[0])
class_name = model.names[class_id]
x1, y1, x2, y2 = map(int, box.xyxy[0])
cv2.rectangle(image, (x1, y1), (x2, y2), (0, 255, 0), 2)
cv2.putText(image, class_name, (x1, y1 - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 255, 0), 2)
cv2.imshow("Vehicle Detection", image)
cv2.waitKey(0)
cv2.destroyAllWindows()
