object_detection_using_sift_and_android_as_cam_via_ipwebcam.py

import cv2
import urllib.request
import numpy as np


def sift_detector(new_image, image_template):
    # Function that compares input image to template
    # It then returns the number of SIFT matches between them
    
    image1 = cv2.cvtColor(new_image, cv2.COLOR_BGR2GRAY)
    image2 = image_template
    
    # Create SIFT detector object
    sift = cv2.xfeatures2d.SIFT_create()

    # Obtain the keypoints and descriptors using SIFT
    keypoints_1, descriptors_1 = sift.detectAndCompute(image1, None)
    keypoints_2, descriptors_2 = sift.detectAndCompute(image2, None)

    # Define parameters for our Flann Matcher
    FLANN_INDEX_KDTREE = 0
    index_params = dict(algorithm = FLANN_INDEX_KDTREE, trees = 3)
    search_params = dict(checks = 100)

    # Create the Flann Matcher object
    flann = cv2.FlannBasedMatcher(index_params, search_params)

    # Obtain matches using K-Nearest Neighbor Method
    # the result 'matchs' is the number of similar matches found in both images
    matches = flann.knnMatch(descriptors_1, descriptors_2, k=2)

    # Store good matches using Lowe's ratio test
    good_matches = []
    for m,n in matches:
        if m.distance < 0.7 * n.distance:
            good_matches.append(m) 

    return len(good_matches)





stream = urllib.request.urlopen('http://192.168.43.1:8080/video') #here use your server ip/video
bytes = bytes()
image_template = cv2.imread('mob.jpeg', 0) #template image 

while True:
    bytes += stream.read(1024)
    a = bytes.find(b'\xff\xd8')
    b = bytes.find(b'\xff\xd9')
    if a != -1 and b != -1:
        jpg = bytes[a:b+2]
        bytes = bytes[b+2:]
        frame = cv2.imdecode(np.fromstring(jpg, dtype=np.uint8), cv2.IMREAD_COLOR)
        # Get height and width of webcam frame
        height, width = frame.shape[:2]

        # Define ROI Box Dimensions
        top_left_x = width // 3
        top_left_y = (height // 2) + (height // 4)
        bottom_right_x = (width // 3) * 2
        bottom_right_y = (height // 2) - (height // 4)
        
        # Draw rectangular window for our region of interest   
        cv2.rectangle(frame, (top_left_x,top_left_y), (bottom_right_x,bottom_right_y), 255, 3)
        
        # Crop window of observation we defined above
        cropped = frame[bottom_right_y:top_left_y , top_left_x:bottom_right_x]
        
        # Flip frame orientation horizontally
        frame = cv2.flip(frame,1)
        
        # Get number of SIFT matches
        matches = sift_detector(cropped, image_template)

        # Display status string showing the current no. of matches 
        cv2.putText(frame,str(matches),(450,450), cv2.FONT_HERSHEY_COMPLEX, 2,(0,255,0),1)
        
        # Our threshold to indicate object deteciton
        # We use 10 since the SIFT detector returns little false positves
        threshold = 10
        
        # If matches exceed our threshold then object has been detected
        if matches > threshold:
            cv2.rectangle(frame, (top_left_x,top_left_y), (bottom_right_x,bottom_right_y), (0,255,0), 3)
            cv2.putText(frame,'Object Found',(50,50), cv2.FONT_HERSHEY_COMPLEX, 2 ,(0,255,0), 2)
        
        cv2.imshow('Object Detector using SIFT', frame)
        if cv2.waitKey(1) == 13: #13 is the Enter Key
            break


cap.release()
cv2.destroyAllWindows()