This commit is contained in:
parent
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commit
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@ -2,7 +2,7 @@
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@ -1,4 +1,4 @@
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||||||
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@ -0,0 +1,33 @@
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import cv2
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import numpy as np
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def roi(frame):
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gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
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blurred = cv2.GaussianBlur(gray, (3, 3), 0)
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height, width = blurred.shape
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face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
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faces = face_cascade.detectMultiScale(blurred, scaleFactor=1.3, minNeighbors=5, minSize=(30, 30))
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for (x, y, w, h) in faces:
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c_x = int(x + w / 2)
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c_y = int(y + h / 2)
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c_x2 = int((int(c_x - w / 2 * 0.8) + int(c_x + w / 2 * 0.8)) / 2)
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c_y2 = int((int(c_y - h / 2 * 1.4) + int(c_y + h / 2 * 1.1)) / 2)
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center_ellipse = (c_x2, c_y2)
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axes = (int(w / 2 * 1), int(h / 2 * 1.4)) # Major axis, Minor axis
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angle = 0 # Rotation angle
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color = (255, 255, 255) # Color in BGR format (white)
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# thickness = 2
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mask = np.zeros((height, width), dtype=np.uint8)
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cv2.ellipse(mask, center_ellipse, axes, angle, 0, 360, 255, -1) # 255 for a white oval
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# Apply the inverted mask to the face
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result = cv2.bitwise_and(frame, frame, mask=mask)
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#black_mask = (result[:, :, 0] == 0) & (result[:, :, 1] == 0) & (result[:, :, 2] == 0)
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#print(result[c_x2, c_y2,1])
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# Replace black pixels with white pixels
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#result[black_mask] = [result[10, 10, 0], result[10, 10, 1], result[10, 10, 2]]
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return result
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||||||
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64
get_head.py
64
get_head.py
|
|
@ -1,47 +1,37 @@
|
||||||
import cv2
|
import cv2
|
||||||
import numpy as np
|
import time
|
||||||
|
|
||||||
# Démarrez la capture vidéo depuis la webcam
|
|
||||||
|
|
||||||
|
cap = cv2.VideoCapture(0)
|
||||||
|
|
||||||
def get_image():
|
def get_image():
|
||||||
cap = cv2.VideoCapture(0)
|
# Set the window name
|
||||||
count = 0
|
window_name = 'Camera Capture'
|
||||||
condition = False
|
|
||||||
while condition == False:
|
# Get the default frames per second (fps) of the camera
|
||||||
|
fps = int(cap.get(cv2.CAP_PROP_FPS))
|
||||||
|
|
||||||
|
# Set the countdown duration in seconds
|
||||||
|
countdown_duration = 10
|
||||||
|
# Start the countdown
|
||||||
|
for countdown in range(countdown_duration, 10, -1):
|
||||||
ret, frame = cap.read()
|
ret, frame = cap.read()
|
||||||
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
|
|
||||||
blurred = cv2.GaussianBlur(gray, (3, 3), 0)
|
|
||||||
height, width = blurred.shape
|
|
||||||
face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
|
|
||||||
faces = face_cascade.detectMultiScale(blurred, scaleFactor=1.3, minNeighbors=5, minSize=(30, 30))
|
|
||||||
|
|
||||||
color_cicle = (0, 0, 255)
|
# Display the camera feed
|
||||||
for (x, y, w, h) in faces:
|
cv2.imshow(window_name, frame)
|
||||||
if (x + w // 2 ) - int(width / 2) <= 10 and (x + w // 2 ) - int(width / 2) >= -10:
|
# Display the countdown on the image
|
||||||
if (y + h // 2) - int(height / 2) <= 10 and (y + h // 2) - int(height / 2) >= -10:
|
font = cv2.FONT_HERSHEY_SIMPLEX
|
||||||
if (w + h)//4 >= 140 and (w + h)//4 <= 160:
|
cv2.putText(frame, str(countdown)[0], (10, 30), font, 1, (0, 255, 0), 2, cv2.LINE_AA)
|
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color_cicle = (0, 255, 0)
|
cv2.imshow(window_name, frame)
|
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count += 1
|
|
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else:
|
|
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count = 0
|
|
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else:
|
|
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count = 0
|
|
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else:
|
|
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count = 0
|
|
||||||
|
|
||||||
if count == 5:
|
# Wait for 1 second and check for key press
|
||||||
print("take picture here")
|
if cv2.waitKey(100) & 0xFF == 27: # 27 is the ASCII code for the 'ESC' key
|
||||||
image = gray
|
|
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condition = True
|
|
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return image
|
|
||||||
cv2.circle(frame, (x + w // 2, y + h // 2), (w + h) // 4, color_cicle, 5)
|
|
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cv2.circle(frame, (int(width / 2), int(height / 2)), 150, (255, 255, 255), 2)
|
|
||||||
|
|
||||||
cv2.imshow('Head Detection', frame)
|
|
||||||
if cv2.waitKey(1) & 0xFF == ord('q'):
|
|
||||||
break
|
break
|
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|
|
||||||
cap.release()
|
# Take a picture at the end of the countdown
|
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cv2.destroyAllWindows()
|
ret, frame = cap.read()
|
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|
#cv2.imwrite("image.png", frame)
|
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|
|
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|
# Release the camera and close the OpenCV window
|
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|
cap.release()
|
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|
cv2
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|
return frame
|
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|
|
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10
main.py
10
main.py
|
|
@ -1,12 +1,14 @@
|
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import cv2
|
import cv2
|
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|
import show_img_conditions
|
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|
import ROI_face
|
||||||
import get_head
|
import get_head
|
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|
|
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|
#show_img_conditions.show()
|
||||||
gray = get_head.get_image()
|
gray = get_head.get_image()
|
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|
|
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cv2.imshow('Grey Image', gray)
|
cv2.imshow('Grey Image', gray)
|
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|
face = ROI_face.roi(gray)
|
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edges = cv2.Canny(gray, 10, 100)
|
cv2.imshow('Face', face)
|
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|
edges = cv2.Canny(face, 10, 100)
|
||||||
cv2.imshow('Image avec Contours de Visage', edges)
|
cv2.imshow('Image avec Contours de Visage', edges)
|
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|
|
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cv2.waitKey(0)
|
cv2.waitKey(0)
|
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|
|
|
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|
|
@ -0,0 +1,11 @@
|
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|
import cv2
|
||||||
|
import time
|
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|
|
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|
# Load an image
|
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|
image_path = "conditions.png" # Replace with the path to your image
|
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|
img = cv2.imread(image_path)
|
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|
|
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|
def show():
|
||||||
|
cv2.imshow('Image', img)
|
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|
cv2.waitKey(5000)
|
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|
cv2.destroyAllWindows()
|
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145
test
145
test
|
|
@ -1,145 +0,0 @@
|
||||||
#include <ros/ros.h>
|
|
||||||
#include <std_msgs/String.h>
|
|
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|
|
||||||
void ChatterCallback()
|
|
||||||
{
|
|
||||||
ROS_INFO("I heard : [%s]", msg.data.c_str());
|
|
||||||
}
|
|
||||||
|
|
||||||
int main( int argc, char **argv)
|
|
||||||
{
|
|
||||||
ros::init(argc, argv, "subcriber");
|
|
||||||
ros::NodeHandle nh;
|
|
||||||
ros::Subsciber subscriber=nh.subscribe("chatter", 1, ChatterCallback);
|
|
||||||
ros::spin();
|
|
||||||
return 0;
|
|
||||||
}
|
|
||||||
|
|
||||||
|
|
||||||
#include <ros/ros.h>
|
|
||||||
#include <std_msgs/String.h>
|
|
||||||
|
|
||||||
int main( int argc, char **argv )
|
|
||||||
{
|
|
||||||
ros::init(argc, argv, "Publisher");
|
|
||||||
ros::NodeHandle nh;
|
|
||||||
ros::Publisher publisher = nh.advertise<std_msgs::String>("chatter",1);
|
|
||||||
ros::Rate loopRate(10);
|
|
||||||
while(ros::ok())
|
|
||||||
{
|
|
||||||
std_msgs::String message;
|
|
||||||
message.data = "hello world" + std::to_string(count);
|
|
||||||
ROS_INFO_STREAM(message.data);
|
|
||||||
publisher.publish(message);
|
|
||||||
ros::spinOnce();
|
|
||||||
loopRate.sleep();
|
|
||||||
count==;
|
|
||||||
}
|
|
||||||
return 0;
|
|
||||||
}
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
#include <ros/ros.h>
|
|
||||||
#include <std_msgs/String.h>
|
|
||||||
|
|
||||||
void ChatterCallback()
|
|
||||||
{
|
|
||||||
ROS_INFO("I heard [%s]"+msg.data.c_str());
|
|
||||||
}
|
|
||||||
|
|
||||||
int main(int argc, char **argv)
|
|
||||||
{
|
|
||||||
ros::init(argc, argv, "Subscriber");
|
|
||||||
ros::NodeHandle nh;
|
|
||||||
ros::Subsctiber subscriber = nh.subscribe("chatter", 10, ChatterCallback);
|
|
||||||
ros::spin();
|
|
||||||
return 0;
|
|
||||||
}
|
|
||||||
|
|
||||||
|
|
||||||
#include <ros/ros.h>
|
|
||||||
#include <std_msgs/String.h>
|
|
||||||
|
|
||||||
int main(int argc, char **argv)
|
|
||||||
{
|
|
||||||
ros::init(argc, argv, "publisher");
|
|
||||||
ros::NodeHandle nh;
|
|
||||||
ros::Punlisher publisher = nh.advretise<std_msgs::String>("chatter", 1);
|
|
||||||
ros::Rate loopRate(10);
|
|
||||||
while(ros::ok()){
|
|
||||||
std_msgs::String message;
|
|
||||||
message.data = "Hello World"+std::to_string(count);
|
|
||||||
ROS_INFO_STREAM(message.data);
|
|
||||||
chatter_Publisher.publish(message);
|
|
||||||
ros::spinOnce;
|
|
||||||
loopRate.sleep();
|
|
||||||
count++;
|
|
||||||
}
|
|
||||||
return 0;
|
|
||||||
}
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
#inclulde <ros/ros.h>
|
|
||||||
#include <std_msgs/String.h>
|
|
||||||
|
|
||||||
void ChatterCallback()
|
|
||||||
{
|
|
||||||
ROS_INFO("I hear [%s]"std_msg.data.c_str());
|
|
||||||
}
|
|
||||||
|
|
||||||
int main(int argc, char **argv)
|
|
||||||
{
|
|
||||||
ros::init(argc, argv, "listener/subscriber";
|
|
||||||
ros::NodeHandle nh;
|
|
||||||
ros::Subscriber subscriber = nh.subscribe("chatter", 10, ChatterCallback);
|
|
||||||
ros::spin;
|
|
||||||
return 0;
|
|
||||||
}
|
|
||||||
|
|
||||||
#include <ros/ros.h>
|
|
||||||
#include <std_msgs/String.h>
|
|
||||||
|
|
||||||
int main(int argc, char **argv)
|
|
||||||
{
|
|
||||||
ros::init(argc, argv, "talker/publisher";
|
|
||||||
ros::NodeHandle nh;
|
|
||||||
ros::Publisher publisher = nh.advertise<std_msgs::String>("chatter",1);
|
|
||||||
ros::Rate loopRate(10);
|
|
||||||
while(ros::ok())
|
|
||||||
{
|
|
||||||
std_msgs::String message;
|
|
||||||
message.data = "Hello World "+std::to_string(count);
|
|
||||||
ROS_INFO_STREAM(message.data);
|
|
||||||
Chatter_Publisher.publish(message);
|
|
||||||
ros::spinOnce();
|
|
||||||
loopRate.sleep();
|
|
||||||
count++;
|
|
||||||
}
|
|
||||||
return 0;
|
|
||||||
}
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
81
test.py
81
test.py
|
|
@ -1,46 +1,57 @@
|
||||||
import cv2
|
import cv2
|
||||||
import dlib
|
|
||||||
import numpy as np
|
import numpy as np
|
||||||
from imutils import face_utils
|
|
||||||
from deeplabv3 import DeepLabV3
|
|
||||||
|
|
||||||
# Load face detector model from Dlib
|
cap = cv2.VideoCapture(0)
|
||||||
detector = dlib.get_frontal_face_detector()
|
count = 0
|
||||||
|
condition = False
|
||||||
|
while condition == False:
|
||||||
|
ret, frame = cap.read()
|
||||||
|
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
|
||||||
|
blurred = cv2.GaussianBlur(gray, (3, 3), 0)
|
||||||
|
height, width = blurred.shape
|
||||||
|
face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
|
||||||
|
faces = face_cascade.detectMultiScale(blurred, scaleFactor=1.3, minNeighbors=5, minSize=(30, 30))
|
||||||
|
|
||||||
# Load DeepLabV3 model for hair segmentation
|
for (x, y, w, h) in faces:
|
||||||
deeplab_model = DeepLabV3(weights='pascal_voc', input_shape=(None, None, 3))
|
c_x = int(x + w/2)
|
||||||
|
c_y = int(y + h/2)
|
||||||
|
center = (c_x, c_y)
|
||||||
|
top = (c_x, int(c_y-h/2*1.4))
|
||||||
|
bottom = (c_x, int(c_y+h/2*1.1))
|
||||||
|
left = (int(c_x - w / 2 * 0.8), c_y)
|
||||||
|
right = (int(c_x + w / 2 * 0.8), c_y)
|
||||||
|
|
||||||
# Load the input image
|
c_x2 = int(( int(c_x - w / 2 * 0.8) + int(c_x + w / 2 * 0.8) )/2)
|
||||||
image_path = "img.jpg"
|
c_y2 = int(( int(c_y-h/2*1.4) + int(c_y+h/2*1.1) )/2)
|
||||||
image = cv2.imread(image_path)
|
'''
|
||||||
original_image = image.copy()
|
cv2.circle(frame, center, 10, (255, 255, 255), 5) #center
|
||||||
|
cv2.circle(frame, top, 10, (255, 255, 255), 5) # top
|
||||||
|
cv2.circle(frame, bottom, 10, (255, 255, 255), 5) # bottom
|
||||||
|
cv2.circle(frame, left, 10, (255, 255, 255), 5) # left
|
||||||
|
cv2.circle(frame, right, 10, (255, 255, 255), 5) # right
|
||||||
|
|
||||||
# Convert image to RGB (DeepLabV3 model requires RGB format)
|
cv2.circle(frame, (x, y), 10, (255, 255, 255), 5) # top left
|
||||||
image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
|
cv2.circle(frame, (x+w, y), 10, (255, 255, 255), 5) # top right
|
||||||
|
cv2.circle(frame, (x, y+h), 10, (255, 255, 255), 5) # bottom left
|
||||||
|
cv2.circle(frame, (x+h, y+h), 10, (255, 255, 255), 5) # bottom right
|
||||||
|
'''
|
||||||
|
center_ellipse = (c_x2, c_y2)
|
||||||
|
axes = (int(w/2* 0.9), int(h/2*1.4)) # Major axis, Minor axis
|
||||||
|
angle = 0 # Rotation angle
|
||||||
|
color = (0, 255, 0) # Color in BGR format
|
||||||
|
thickness = 2
|
||||||
|
|
||||||
# Detect face using Dlib
|
# Draw the ellipse on the image
|
||||||
faces = detector(image_rgb)
|
#cv2.ellipse(frame, center_ellipse, axes, angle, 0, 360, color, thickness)
|
||||||
for face in faces:
|
|
||||||
(x, y, w, h) = face_utils.rect_to_bb(face)
|
|
||||||
cv2.rectangle(image, (x, y), (x + w, y + h), (255, 0, 0), 2)
|
|
||||||
|
|
||||||
# Perform hair segmentation using DeepLabV3
|
mask = np.zeros((height, width), dtype=np.uint8)
|
||||||
face_roi = original_image[y:y + h, x:x + w]
|
cv2.ellipse(mask, center_ellipse, axes, angle, 0, 360, 255, -1)
|
||||||
resized_face_roi = cv2.resize(face_roi, (512, 512)) # Resize for DeepLabV3 model input size
|
result = cv2.bitwise_and(frame, frame, mask=mask)
|
||||||
hair_mask = deeplab_model.predict(resized_face_roi)
|
|
||||||
hair_mask = (hair_mask.squeeze() == 15).astype(np.uint8) * 255 # Hair class in Pascal VOC dataset is 15
|
|
||||||
|
|
||||||
# Apply hair mask to the original image
|
cv2.imshow('Head Detection', result)
|
||||||
hair_segmented = cv2.bitwise_and(face_roi, face_roi, mask=hair_mask)
|
if cv2.waitKey(1) & 0xFF == ord('q'):
|
||||||
|
break
|
||||||
|
|
||||||
# Replace hair in the original image with the segmented hair
|
cap.release()
|
||||||
original_image[y:y + h, x:x + w] = cv2.bitwise_and(original_image[y:y + h, x:x + w], original_image[y:y + h, x:x + w], mask=cv2.bitwise_not(hair_mask))
|
|
||||||
original_image[y:y + h, x:x + w] += hair_segmented
|
|
||||||
|
|
||||||
# Save the segmented image
|
|
||||||
cv2.imwrite("segmented_face_and_hair.jpg", original_image)
|
|
||||||
|
|
||||||
# Display the segmented image
|
|
||||||
cv2.imshow("Segmented Face and Hair", original_image)
|
|
||||||
cv2.waitKey(0)
|
|
||||||
cv2.destroyAllWindows()
|
cv2.destroyAllWindows()
|
||||||
|
|
||||||
|
|
|
||||||
|
|
@ -0,0 +1,35 @@
|
||||||
|
import cv2
|
||||||
|
import numpy as np
|
||||||
|
|
||||||
|
# Load an image
|
||||||
|
image = cv2.imread('image.jpg')
|
||||||
|
|
||||||
|
# Convert the image to grayscale
|
||||||
|
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
|
||||||
|
|
||||||
|
# Apply thresholding to create a binary image
|
||||||
|
_, thresh = cv2.threshold(gray, 127, 255, cv2.THRESH_BINARY)
|
||||||
|
|
||||||
|
# Find contours in the binary image
|
||||||
|
contours, _ = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
|
||||||
|
|
||||||
|
# Choose the contour you want to remove (in this example, the first contour is removed)
|
||||||
|
contour_to_remove = contours[0]
|
||||||
|
|
||||||
|
# Create a mask with the same shape as the image
|
||||||
|
mask = np.zeros_like(image)
|
||||||
|
|
||||||
|
# Fill the contour with a background color (white in this case)
|
||||||
|
cv2.fillPoly(mask, [contour_to_remove], (255, 255, 255))
|
||||||
|
|
||||||
|
# Invert the mask to keep the rest of the image
|
||||||
|
mask_inv = cv2.bitwise_not(mask)
|
||||||
|
|
||||||
|
# Bitwise AND the original image with the inverted mask
|
||||||
|
result = cv2.bitwise_and(image, mask_inv)
|
||||||
|
|
||||||
|
# Display the original and processed images
|
||||||
|
cv2.imshow('Original Image', image)
|
||||||
|
cv2.imshow('Result', result)
|
||||||
|
cv2.waitKey(0)
|
||||||
|
cv2.destroyAllWindows()
|
||||||
Loading…
Reference in New Issue