def FFT_blur(img, thresh=27, vis=False, test=False):
'''
thresh: blur or not judge level
vis:
test: for testing purpose, we can progressively blur our input image and conduct FFT-based blur detection on each example. This flag indicates whether to test or not.
'''
# load the image, resize, convert to grayscale
orig = cv2.imread(img)
orig = imutils.resize(orig, width=500)
gray = cv2.cvtColor(orig, cv2.COLOR_BGR2GRAY)
# apply blur one by FFT
(mean, blurry) = detect_blur_fft(gray, size=60) # size, thresh, vis
# draw on the image, indicating where or not it is blurry
image = np.dstack([gray] * 3)
color = (0, 0, 255) if blurry else (0, 255, 0)
text = 'Blurry {:.4f}' if blurry else 'Not Blurry ({:.4f})'
text = text.format(mean)
cv2.putText(image, text, (10, 25), cv2.FONT_HERSHEY_SIMPLEX, .7, color, 2)
if not test:
print('[INFO] {}'.format(text))
# show the output image
cv2.imshow('Output', image)
mac_show_cv2()
# check to see if are going to test our FFT blurriness detector using various size of a Gaussian Kernel
if test:
# loop over various blur radii
for radius in range(1, 30, 2):
# clone the original grayscale image
image = gray.copy()
# check to see if the kernel radius is greater than zero
if radius > 0:
# blur the input image by the supplied radius using a Gaussian kernel
image = cv2.GaussianBlur(image, (radius, radius), 0)
# apply our blur detctor using the FFT
(mean, blurry) = detect_blur_fft(image, size=60, thresh=thresh)
# draw on the image, indicating whet2her or not it is blurry
image = np.dstack([image] * 3)
color = (0, 0, 255) if blurry else (0, 255, 0)
text = 'Blurry ({:.4f})' if blurry else 'Not Blurry ({:.4f})'
text = text.format(mean)
cv2.putText(image, text, (10, 25), cv2.FONT_HERSHEY_SIMPLEX,
.7, color, 2)
print('[INFO] Kernel: {}, Result: {}'.format(radius, text))
# show the image
cv2.imshow('Test Image', image)
mac_show_cv2()
default=-1,
help="whether or not we should progressively blur the image")
args = vars(ap.parse_args())
# load the input image from disk, resize it, and convert it to
# grayscale
# orig = cv2.imread(args["image"])
orig = urlToImage(
"https://www.sony.com.ec/image/bc6d25fa6371c2899ce704a2bed7614c?fmt=png-alpha&wid=960"
)
orig = imutils.resize(orig, width=500)
gray = cv2.cvtColor(orig, cv2.COLOR_BGR2GRAY)
# apply our blur detector using the FFT
(mean, blurry) = detect_blur_fft(gray,
size=60,
thresh=args["thresh"],
vis=args["vis"] > 0)
# draw on the image, indicating whether or not it is blurry
image = np.dstack([gray] * 3)
color = (0, 0, 255) if blurry else (0, 255, 0)
text = "Blurry ({:.4f})" if blurry else "Not Blurry ({:.4f})"
text = text.format(mean)
cv2.putText(image, text, (10, 25), cv2.FONT_HERSHEY_SIMPLEX, 0.7, color, 2)
print("[INFO] {}".format(text))
# show the output image
cv2.imshow("Output", image)
cv2.waitKey(0)
# check to see if are going to test our FFT blurriness detector using
# initialize the video stream and allow the camera sensor to warm up
print("[INFO] starting video stream...")
vs = VideoStream(src=0).start()
time.sleep(2.0)
# loop over the frames from the video stream
while True:
# grab the frame from the threaded video stream and resize it
# to have a maximum width of 400 pixels
frame = vs.read()
frame = imutils.resize(frame, width=800, height=500)
# convert the frame to grayscale and detect blur in it
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
(mean, blurry) = detect_blur_fft(gray,
size=60,
thresh=args["thresh"],
vis=False)
# draw on the frame, indicating whether or not it is blurry
color = (0, 0, 255) if blurry else (0, 255, 0)
text = "Blurry ({:.4f})" if blurry else "Not Blurry ({:.4f})"
text = text.format(mean)
cv2.putText(frame, text, (30, 70), cv2.FONT_HERSHEY_SIMPLEX, 0.7, color, 2)
# show the output frame
cv2.imshow("Frame", frame)
key = cv2.waitKey(1) & 0xFF
# if the `q` key was pressed, break from the loop
if key == ord("q"):
break