import cv2
from video_loop import run_video_capture_pipeline
def color(image):
# kernel_size = 5
# image = cv2.GaussianBlur(image, (kernel_size, kernel_size), 0)
image = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)
return image
run_video_capture_pipeline(transform_fn=color)
import cv2
from video_loop import run_video_capture_pipeline
def canny(image):
edges = cv2.Canny(cv2.cvtColor(image, cv2.COLOR_BGR2GRAY), 50, 150)
# Transform again to BGR
image = cv2.cvtColor(edges, cv2.COLOR_GRAY2BGR)
return image
run_video_capture_pipeline(transform_fn=canny)
) # link to model: http://dlib.net/files/shape_predictor_68_face_landmarks.dat.bz2
def blur(image):
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
faces = detector(gray, 0)
mask = np.zeros(image.shape[:2], np.uint8)
blurred_image = image.copy()
for face in faces: # if there are faces
(x, y, w, h) = (face.left(), face.top(), face.width(), face.height())
blurred_image[y:y + h, x:x + w, :] = anonymize_face_pixelate(
blurred_image[y:y + h, x:x + w, :], blocks=10)
# *** Facial Landmarks detection
shape = predictor(gray, face)
shape = face_utils.shape_to_np(shape)
# Get mask with only face shape
shape = cv2.convexHull(shape)
cv2.drawContours(mask, [shape], -1, 255, -1)
# Replace blurred image only in mask
mask = mask / 255.0
mask = np.expand_dims(mask, axis=-1)
image = (1.0 - mask) * image + mask * blurred_image
image = image.astype(np.uint8)
return image
run_video_capture_pipeline(transform_fn=blur)
import cv2
from video_loop import run_video_capture_pipeline
kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (3, 3))
fgbg = cv2.createBackgroundSubtractorMOG2()
def bg_substraction(image):
image = fgbg.apply(image)
image = cv2.morphologyEx(image, cv2.MORPH_OPEN, kernel)
# Transform again to BGR
image = cv2.cvtColor(image, cv2.COLOR_GRAY2BGR)
return image
run_video_capture_pipeline(transform_fn=bg_substraction)
import cv2
import numpy as np
from video_loop import run_video_capture_pipeline, args
cap = cv2.VideoCapture(args.read_camera)
ret, frame1 = cap.read()
prvs = cv2.cvtColor(frame1, cv2.COLOR_BGR2GRAY)
hsv = np.zeros_like(frame1)
hsv[..., 1] = 255
cap.release()
def dense_flow(image):
global prvs
next = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
flow = cv2.calcOpticalFlowFarneback(prvs, next, None, 0.5, 3, 15, 3, 5,
1.2, 0)
mag, ang = cv2.cartToPolar(flow[..., 0], flow[..., 1])
hsv[..., 0] = ang * 180 / np.pi / 2
hsv[..., 2] = cv2.normalize(mag, None, 0, 255, cv2.NORM_MINMAX)
image = cv2.cvtColor(hsv, cv2.COLOR_HSV2BGR)
prvs = next
return image
run_video_capture_pipeline(transform_fn=dense_flow)
# Range for lower red
lower_red = np.array([0, 120, 70])
upper_red = np.array([10, 255, 255])
mask1 = cv2.inRange(hsv, lower_red, upper_red)
# Range for upper range
lower_red = np.array([170, 120, 70])
upper_red = np.array([180, 255, 255])
mask2 = cv2.inRange(hsv, lower_red, upper_red)
# Generating the final mask to detect red color
mask1 = mask1 + mask2
mask1 = cv2.morphologyEx(mask1, cv2.MORPH_OPEN, np.ones((3, 3), np.uint8))
mask1 = cv2.morphologyEx(mask1, cv2.MORPH_DILATE, np.ones((3, 3),
np.uint8))
# creating an inverted mask to segment out the cloth from the frame
mask2 = cv2.bitwise_not(mask1)
# Segmenting the cloth out of the frame using bitwise and with the inverted mask
res1 = cv2.bitwise_and(image, image, mask=mask2)
# creating image showing static background frame pixels only for the masked region
res2 = cv2.bitwise_and(background, background, mask=mask1)
# Generating the final output
final_output = cv2.addWeighted(res1, 1, res2, 1, 0)
return final_output
run_video_capture_pipeline(transform_fn=invisibility)