It's possible to get face coordinates in image source file or frame? Some thing like:
face.Height = Affdex.Face[0].PositionHeight;
face.Left = Affdex.Face[0].PositionLeft;
face.Top = Affdex.Face[0].PositionTop;
face.Width = Affdex.Face[0].PositionWidth;
http://developer.affectiva.com/fpi/
The bounding box for each face is not provided directly via the Affdex SDKs, but they do provide coordinates for all the face points, so to determine a face's bounding box, all you need to do is iterate through its face points and track the max/min values in each dimension.
As an example, see the drawFacePoints method in the DrawingView class of the AffdexMe sample app: https://github.com/Affectiva/affdexme-android/blob/master/app/src/main/java/com/affectiva/affdexme/DrawingView.java
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I'm new to opencv and I'm trying to detect person through cv2.findContours with morphological transformation of the video. Here is the code snippet..
import numpy as np
import imutils
import cv2 as cv
import time
cap = cv.VideoCapture(0)
while(cap.isOpened()):
ret, frame = cap.read()
#frame = imutils.resize(frame, width=700,height=100)
gray = cv.cvtColor(frame, cv.COLOR_BGR2GRAY)
gray = cv.GaussianBlur(gray, (21, 21), 0)
cv.accumulateWeighted(gray, avg, 0.5)
mask2 = cv.absdiff(gray, cv.convertScaleAbs(avg))
mask = cv.absdiff(gray, cv.convertScaleAbs(avg))
contours0, hierarchy = cv.findContours(mask2,cv.RETR_EXTERNAL,cv.CHAIN_APPROX_SIMPLE)
for cnt in contours0:
.
.
.
The rest of the code has the logic of a contour passing a line and incrementing the count.
The problem I'm encountering is, cv.findContours detects every movement/change in the frame (including the person). What I want is cv.findContours to detect only person and not any other movement. I know that person detection can be achieved through harrcasacade but is there any way I can implement detection using cv2.findContours?
If not then is there a way I can still do morphological transformation and detect people because the project I'm working on requires filtering of noise and much of the background to detect the person and increment it's count on passing the line.
I will show you two options to do this.
The method I mentioned in the comments which you can use with Yolo to detect humans:
Use saliency to detect the standout parts of the video
Apply K-Means Clustering to cluster the objects into individual clusters.
Apply Background Subtraction and erosion or dilation (or both depends on the video but try them all and see which one does the best job).
Crop the objects
Send the cropped objects to Yolo
If the class name is a pedestrian or human then draw the bounding boxes on them.
Using OpenCV's builtin pedestrian detection which is much more easier:
Convert frames to black and white
Use pedestrian_cascade.detectMultiScale() on the grey frames.
Draw a bounding box over each pedestrian
Second method is much simpler but it depends what is expected of you for this project.
I would like to calculate the Horizontal and Vertical field of view from the camera intrinsic matrix for the cameras used in the KITTI dataset. The reason I need the Field of view is to convert a depth map into 3D point clouds.
Though this question has been asked quite a long time ago, I felt it needed an answer as I ran into the same issue and was unable to find any info on it.
I have however solved it using the information available in this document and some more general camera calibration documents
Firstly, we need to convert the supplied disparity into distance. This can be done through fist converting the disp map into floats through the method in the dev_kit where they state:
disp(u,v) = ((float)I(u,v))/256.0;
This disparity can then be converted into a distance through the default stereo vision equation:
Depth = Baseline * focal length/ Disparity
Now come some tricky parts. I searched high and low for the focal length and was unable to find it in documentation.
I realised just now when writing that the baseline is documented in the aforementioned source however from section IV.B we can see that it can be found in P(i)rect indirectly.
The P_rects can be found in the calibration files and will be used for both calculating the baseline and the translation from uv in the image to xyz in the real world.
The steps are as follows:
For pixel in depthmap:
xyz_normalised = P_rect \ [u,v,1]
where u and v are the x and y coordinates of the pixel respectively
which will give you a xyz_normalised of shape [x,y,z,0] with z = 1
You can then multiply it with the depth that is given at that pixel to result in a xyz coordinate.
For completeness, as P_rect is the depth map here, you need to use P_3 from the cam_cam calibration txt files to get the baseline (as it contains the baseline between the colour cameras) and the P_2 belongs to the left camera which is used as a reference for occ_0 files.
I want to apply a warp to an image specified by the source and destination locations of a (potentially small) number of control points in deep learning framework. And I thought the function 'tf.contrib.image.sparse_image_warp' can do exactly what I want. But after I tried, the warped image didn't look good.
More specifically, I want to warp the source image to destination image by face landmarks. So, I used the following code:
warped_image, dense_flows = sparse_image_warp(source_image, source_image_landmarks, dest_image_landmarks)
And the results are here:
source image with landmark:
dest image with landmark:
warped result:
desired result generated by other method:
Am I using the function in wrong way? Or the function can't realize my need?
Pay close attention to tf.contrib.image.sparse_image_warp, you need to supply the control points (lfacial landmarks in your example) in y-x coordinate rather than x-y.
I am working on a dog detection system using deep learning (Tensorflow object detection) and Real Sense D425 camera. I am using the Intel(R) RealSense(TM) ROS Wrapper in order to get images from the camera.
I am executing "roslaunch rs_rgbd.launch" and my Python code is subscribed to "/camera/color/image_raw" topic in order to get the RGB image. Using this image and object detection library, I am able to infer (20 fps) the location of a dog in a image as a box level (xmin,xmax,ymin,ymax)
I will like to crop the PointCloud information with the object detection information (xmin,xmax,ymin,ymax)
and determine if the dog is far away or near the camera. I will like to use the aligned information pixel by pixel between the RGB image and the pointcloud.
How can I do it? Is there any topic for that?
Thanks in advance
Intel publishes their python notebook about the same problem at: https://github.com/IntelRealSense/librealsense/blob/jupyter/notebooks/distance_to_object.ipynb
What they do is as follow :
get color frame and depth frame (point cloud in your case)
align the depth to color
use ssd to detect the dog inside color frame
Get the average depth for detected dog and convert to meter
depth = np.asanyarray(aligned_depth_frame.get_data())
# Crop depth data:
depth = depth[xmin_depth:xmax_depth,ymin_depth:ymax_depth].astype(float)
# Get data scale from the device and convert to meters
depth_scale = profile.get_device().first_depth_sensor().get_depth_scale()
depth = depth * depth_scale
dist,_,_,_ = cv2.mean(depth)
print("Detected a {0} {1:.3} meters away.".format(className, dist))
Hope this help
How can I do a basic face alignment on a 2-dimensional image with the assumption that I have the position/coordinates of the mouth and eyes.
Is there any algorithm that I could implement to correct the face alignment on images?
Face (or image) alignment refers to aligning one image (or face in your case) with respect to another (or a reference image/face). It is also referred to as image registration. You can do that using either appearance (intensity-based registration) or key-point locations (feature-based registration). The second category stems from image motion models where one image is considered a displaced version of the other.
In your case the landmark locations (3 points for eyes and nose?) provide a good reference set for straightforward feature-based registration. Assuming you have the location of a set of points in both of the 2D images, x_1 and x_2 you can estimate a similarity transform (rotation, translation, scaling), i.e. a planar 2D transform S that maps x_1 to x_2. You can additionally add reflection to that, though for faces this will most-likely be unnecessary.
Estimation can be done by forming the normal equations and solving a linear least-squares (LS) problem for the x_1 = Sx_2 system using linear regression. For the 5 unknown parameters (2 rotation, 2 translation, 1 scaling) you will need 3 points (2.5 to be precise) for solving 5 equations. Solution to the above LS can be obtained through Direct Linear Transform (e.g. by applying SVD or a matrix pseudo-inverse). For cases of a sufficiently large number of reference points (i.e. automatically detected) a RANSAC-type method for point filtering and uncertainty removal (though this is not your case here).
After estimating S, apply image warping on the second image to get the transformed grid (pixel) coordinates of the entire image 2. The transform will change pixel locations but not their appearance. Unavoidably some of the transformed regions of image 2 will lie outside the grid of image 1, and you can decide on the values for those null locations (e.g. 0, NaN etc.).
For more details: R. Szeliski, "Image Alignment and Stitching: A Tutorial" (Section 4.3 "Geometric Registration")
In OpenCV see: Geometric Image Transformations, e.g. cv::getRotationMatrix2D cv::getAffineTransform and cv::warpAffine. Note though that you should estimate and apply a similarity transform (special case of an affine) in order to preserve angles and shapes.
For the face there is lot of variability in feature points. So it won't be possible to do a perfect fit of all feature points by just affine transforms. The only way to align all the points perfectly is to warp the image given the points. Basically you can do a triangulation of image given the points and do a affine warp of each triangle to get the warped image where all the points are aligned.
Face detection could be handled based on the just eye positions.
Herein, OpenCV, Dlib and MTCNN offers to detect faces and eyes. Besides, it is a python based framework but deepface wraps those methods and offers an out-of-the box detection and alignment function.
detectFace function applies detection and alignment in the background respectively.
#!pip install deepface
from deepface import DeepFace
backends = ['opencv', 'ssd', 'dlib', 'mtcnn']
DeepFace.detectFace("img.jpg", detector_backend = backends[0])
Besides, you can apply detection and alignment manually.
from deepface.commons import functions
img = functions.load_image("img.jpg")
backends = ['opencv', 'ssd', 'dlib', 'mtcnn']
detected_face = functions.detect_face(img = img, detector_backend = backends[3])
plt.imshow(detected_face)
aligned_face = functions.align_face(img = img, detector_backend = backends[3])
plt.imshow(aligned_face)
processed_img = functions.detect_face(img = aligned_face, detector_backend = backends[3])
plt.imshow(processed_img)
There's a section Aligning Face Images in OpenCV's Face Recognition guide:
http://docs.opencv.org/trunk/modules/contrib/doc/facerec/facerec_tutorial.html#aligning-face-images
The script aligns given images at the eyes. It's written in Python, but should be easy to translate to other languages. I know of a C# implementation by Sorin Miron:
http://code.google.com/p/stereo-face-recognition/