MounishT / project

project

Aim

To write a python program using OpenCV to do the following image manipulations. i) Extract ROI from an image. ii) Perform handwritting detection in an image. iii) Perform object detection with label in an image.

Software Required:

Anaconda - Python 3.7

Developed By :T MOUNISH

Register Number : 212223240098

Algorithm:

I)Perform ROI from an image

Step1:

Import necessary packages

Step2:

Read the image and convert the image into RGB

Step3:

Display the image

Step4:

Set the pixels to display the ROI

Step5:

Perform bit wise conjunction of the two arrays using bitwise_and

Step6:

Display the segmented ROI from an image.

PROGRAM :

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

image = 'orangeflower.jpg'
img = cv2.imread(image)
img_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)

plt.imshow(img_rgb)
plt.title('Original Image')
plt.axis('off')
plt.show()

hsv_img = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
lower_yellow = np.array([22, 93, 0])
upper_yellow = np.array([45, 255, 255])
mask = cv2.inRange(hsv_img, lower_yellow, upper_yellow)

segmented_image = cv2.bitwise_and(img, img, mask=mask)

segmented_image_rgb = cv2.cvtColor(segmented_image, cv2.COLOR_BGR2RGB)

plt.imshow(segmented_image_rgb)
plt.title('Segmented Image (Yellow)')
plt.axis('off')
plt.show()

OUTPUT :

II)Perform handwritting detection in an image

Step1:

Import necessary packages

Step2:

Define a function to read the image,Convert the image to grayscale,Apply Gaussian blur to reduce noise and improve edge detection,Use Canny edge detector to find edges in the image,Find contours in the edged image,Filter contours based on area to keep only potential text regions,Draw bounding boxes around potential text regions.

Step3:

Display the results.

PROGRAM :

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

def detect_handwriting(image_path):
    # Read the image
    img = cv2.imread(image_path)

    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

    blurred = cv2.GaussianBlur(gray, (5, 5), 0)

    edges = cv2.Canny(blurred, 50, 150)

    contours, _ = cv2.findContours(edges.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)

    min_area = 100
    text_contours = [cnt for cnt in contours if cv2.contourArea(cnt) > min_area]

    img_copy = img.copy()
    for contour in text_contours:
        x, y, w, h = cv2.boundingRect(contour)
        cv2.rectangle(img_copy, (x, y), (x + w, y + h), (0, 255, 0), 2)

    img_rgb = cv2.cvtColor(img_copy, cv2.COLOR_BGR2RGB)
    plt.imshow(img_rgb)
    plt.title('Handwriting Detection')
    plt.axis('off')
    plt.show()

image_path = 'coffecup.jpg'

detect_handwriting(image_path)

OUTPUT :

III)Perform object detection with label in an image

Step1:

Import necessary packages

Step2:

Set and add the config_file,weights to ur folder.

Step3:

Use a pretrained Dnn model (MobileNet-SSD v3)

Step4:

Create a classLabel and print the same

Step5:

Display the image using imshow()

Step6:

Set the model and Threshold to 0.5

Step7:

Flatten the index,confidence.

Step8:

Display the result.

PROGRAM :

config_file='ssd_mobilenet_v3_large_coco_2020_01_14.pbtxt'
frozen_model='frozen_inference_graph.pb'

model=cv2.dnn_DetectionModel(frozen_model,config_file)

classLabels = []
file_name='Labels.txt'
with open(file_name,'rt')as fpt:
    classLabels=fpt.read().rstrip('\n').split('\n')

print(classLabels)
print(len(classLabels))
img=cv2.imread('bi.jpg')
plt.imshow(img)
plt.imshow(cv2.cvtColor(img,cv2.COLOR_BGR2RGB))
model.setInputSize(320,320)
model.setInputScale(1.0/127.5)#255/2=127.5
model.setInputMean((127.5,127.5,127.5))
model.setInputSwapRB(True)
ClassIndex,confidence,bbox=model.detect(img,confThreshold=0.5)
print(ClassIndex)
font_scale=3
font=cv2.FONT_HERSHEY_PLAIN
for ClassInd,conf,boxes in zip(ClassIndex.flatten(),confidence.flatten(),bbox):
    cv2.rectangle(img,boxes,(0,0,255),2)
    cv2.putText(img,classLabels[ClassInd-1],(boxes[0]+10,boxes[1]+40),font,fontScale=font_scale,color=(255,0,0),thickness=1)
plt.imshow(cv2.cvtColor(img,cv2.COLOR_BGR2RGB))

OUTPUT:

Result :

Thus the extraction ROI from an image, performing handwritting detection in an image and performing object detection with label in an image. are executed Successfully.