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heatmap_video_generation.py
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heatmap_video_generation.py
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#%%Initialzization
import h5py
import numpy as np
import matplotlib.pyplot as plt
import cv2
import matplotlib.animation as animation
from os.path import join
import keras.backend as K
from keras.models import load_model
import os
from mpl_toolkits.mplot3d import Axes3D
# Load .h5 file containing
def load_file_(filepath):
if filepath.endswith('.h5'):
with h5py.File(filepath, 'r') as f_:
data = (f_[list(f_.keys())[0]])[()]
else:
raise ValueError('.h5 required format.')
return data
# For each joint and timestep, Gaussian blur is used to smooth each heatmap
def decay_heatmap(heatmap, sigma2=4):
heatmap = cv2.GaussianBlur(heatmap,(0,0),sigma2)
heatmap /= np.max(heatmap) # to keep the max to 1
return heatmap
# Calculates MSE error for 2D predictions
def mse2D(y_true, y_pred):
mean_over_ch = K.mean(K.square(y_pred - y_true), axis=-1)
mean_over_w = K.mean(mean_over_ch, axis=-1)
mean_over_h = K.mean(mean_over_w, axis=-1)
return mean_over_h
# constant parameters
H = 260; W = 344; num_joints = 13
# generates ground truth 2D heatmaps
def get_2Dcoords_and_heatmaps_label(vicon_xyz, ch_idx):
" From 3D label, get 2D label coordinates and heatmaps for selected camera "
if ch_idx == 1:
P_mat_cam = np.load(join(P_mat_dir, 'P1.npy'))
elif ch_idx == 3:
P_mat_cam = np.load(join(P_mat_dir, 'P2.npy'))
elif ch_idx == 2:
P_mat_cam = np.load(join(P_mat_dir, 'P3.npy'))
elif ch_idx == 0:
P_mat_cam = np.load(join(P_mat_dir, 'P4.npy'))
# use homogeneous coordinates representation to project 3d XYZ coordinates to 2d UV pixel coordinates.
vicon_xyz_homog = np.concatenate([vicon_xyz, np.ones([1, 13])], axis=0)
coord_pix_homog = np.matmul(P_mat_cam, vicon_xyz_homog)
coord_pix_homog_norm = coord_pix_homog / coord_pix_homog[-1]
u = coord_pix_homog_norm[0]
v = H - coord_pix_homog_norm[1] # flip v coordinate to match the image direction
# mask is used to make sure that pixel positions are in frame range.
mask = np.ones(u.shape).astype(np.float32)
mask[np.isnan(u)] = 0;
mask[np.isnan(v)] = 0
mask[u > W] = 0;
mask[u <= 0] = 0;
mask[v > H] = 0;
mask[v <= 0] = 0
# pixel coordinates
u = u.astype(np.int32)
v = v.astype(np.int32)
# initialize, fill and smooth the heatmaps
label_heatmaps = np.zeros((H, W, num_joints))
for fmidx, zipd in enumerate(zip(v, u, mask)):
if zipd[2] == 1: # write joint position only when projection within frame boundaries
label_heatmaps[zipd[0], zipd[1], fmidx] = 1
label_heatmaps[:, :, fmidx] = decay_heatmap(label_heatmaps[:, :, fmidx])
return np.stack((v, u), axis=-1), mask, label_heatmaps
def predict_CNN_extract_skeleton_2d(ch_idx, imgidx, verbose=False):
""" Predict with CNN, extract predicted 2D coordinates,
and return image, 2D label and 2D prediction for imgidx sample """
# Cropping rightmost pixels
x_selected = x_h5['DVS'][imgidx:imgidx + 1, :, :344, ch_idx]
pred = trained_model.predict(np.expand_dims(x_selected, axis=-1))
y_2d, gt_mask, y_heatmaps = get_2Dcoords_and_heatmaps_label(y_h5['XYZ'][imgidx], ch_idx)
p_coords_max = np.zeros(y_2d.shape)
confidence = np.zeros(y_2d.shape[0]) # confidence is not used in this example.
for j_idx in range(y_2d.shape[0]):
pred_j_map = pred[0, :, :, j_idx]
# Predicted max value for each heatmap. Keep only the first one if more are present.
p_coords_max_tmp = np.argwhere(pred_j_map == np.max(pred_j_map))
p_coords_max[j_idx] = p_coords_max_tmp[0]
# Confidence of the joint
confidence[j_idx] = np.max(pred_j_map)
if verbose:
print('j{} GT: {} -- PRED: {} (mask: {})'.format(j_idx, y_2d[j_idx], p_coords_max[j_idx], gt_mask[j_idx]))
y_2d_float = y_2d.astype(float)
# where mask is 0, set gt back to NaN
y_2d_float[gt_mask == 0] = np.nan
dist_2d = np.linalg.norm((y_2d_float - p_coords_max), axis=-1)
mpjpe = np.nanmean(dist_2d)
print('sample mpjpe: {}'.format(mpjpe))
return x_selected[0], y_2d_float, p_coords_max
def plot_2d(dvs_frame, sample_gt, sample_pred):
" To plot image and 2D ground truth and prediction "
plt.figure()
plt.imshow(dvs_frame, cmap='gray')
plt.plot(sample_gt[:,1], sample_gt[:,0], '.', c='red', label='gt')
plt.plot(sample_pred[:,1], sample_pred[:,0], '.', c='blue', label='pred')
plt.legend()
plt.show()
# For 3D triangulation and plot:
def project_uv_xyz_cam(uv, M):
# adapted from: https://www.cc.gatech.edu/~hays/compvision/proj3/
N = len(uv)
uv_homog = np.hstack((uv, np.ones((N, 1))))
M_inv= np.linalg.pinv(M)
xyz = np.dot(M_inv, uv_homog.T).T
x = xyz[:, 0] / xyz[:, 3]; y = xyz[:, 1] / xyz[:, 3]; z = xyz[:, 2] / xyz[:, 3]
return x,y,z
def find_intersection(P0,P1):
# from: https://stackoverflow.com/questions/52088966/nearest-intersection-point-to-many-lines-in-python/52089867
"""P0 and P1 are NxD arrays defining N lines.
D is the dimension of the space. This function
returns the least squares intersection of the N
lines from the system given by eq. 13 in
http://cal.cs.illinois.edu/~johannes/research/LS_line_intersect.pdf."""
# generate all line direction vectors
n = (P1-P0) / np.linalg.norm(P1-P0,axis=1)[:,np.newaxis] # normalized
# generate the array of all projectors
projs = np.eye(n.shape[1]) - n[:,:,np.newaxis]*n[:,np.newaxis] # I - n*n.T
# see fig. 1
# generate R matrix and q vector
R = projs.sum(axis=0)
q = (np.matmul(projs,P0[:,:,np.newaxis])).sum(axis=0)
# solve the least squares problem for the
# intersection point p: Rp = q
p = np.linalg.lstsq(R,q, rcond=None)[0]
return p.T
def skeleton(x,y,z):
" Definition of skeleton edges "
# rename joints to identify name and axis
x_head, x_shoulderR, x_shoulderL, x_elbowR, x_elbowL, x_hipR, x_hipL, \
x_handR, x_handL, x_kneeR, x_kneeL, x_footR, x_footL = x[0], x[1], x[2], x[3], \
x[4], x[5], x[6], x[7], x[8], x[9], x[10], x[11], x[12]
y_head, y_shoulderR, y_shoulderL, y_elbowR, y_elbowL, y_hipR, y_hipL, \
y_handR, y_handL, y_kneeR, y_kneeL, y_footR, y_footL = y[0], y[1], y[2], y[3], \
y[4], y[5], y[6], y[7], y[8], y[9], y[10], y[11], y[12]
z_head, z_shoulderR, z_shoulderL, z_elbowR, z_elbowL, z_hipR, z_hipL, \
z_handR, z_handL, z_kneeR, z_kneeL, z_footR, z_footL = z[0], z[1], z[2], z[3],\
z[4], z[5], z[6], z[7], z[8], z[9], z[10], z[11], z[12]
#definition of the lines of the skeleton graph
skeleton=np.zeros((14,3,2))
skeleton[0,:,:]=[[x_head,x_shoulderR],[y_head,y_shoulderR],[z_head,z_shoulderR]]
skeleton[1,:,:]=[[x_head,x_shoulderL],[y_head,y_shoulderL],[z_head,z_shoulderL]]
skeleton[2,:,:]=[[x_elbowR,x_shoulderR],[y_elbowR,y_shoulderR],[z_elbowR,z_shoulderR]]
skeleton[3,:,:]=[[x_elbowL,x_shoulderL],[y_elbowL,y_shoulderL],[z_elbowL,z_shoulderL]]
skeleton[4,:,:]=[[x_elbowR,x_handR],[y_elbowR,y_handR],[z_elbowR,z_handR]]
skeleton[5,:,:]=[[x_elbowL,x_handL],[y_elbowL,y_handL],[z_elbowL,z_handL]]
skeleton[6,:,:]=[[x_hipR,x_shoulderR],[y_hipR,y_shoulderR],[z_hipR,z_shoulderR]]
skeleton[7,:,:]=[[x_hipL,x_shoulderL],[y_hipL,y_shoulderL],[z_hipL,z_shoulderL]]
skeleton[8,:,:]=[[x_hipR,x_kneeR],[y_hipR,y_kneeR],[z_hipR,z_kneeR]]
skeleton[9,:,:]=[[x_hipL,x_kneeL],[y_hipL,y_kneeL],[z_hipL,z_kneeL]]
skeleton[10,:,:]=[[x_footR,x_kneeR],[y_footR,y_kneeR],[z_footR,z_kneeR]]
skeleton[11,:,:]=[[x_footL,x_kneeL],[y_footL,y_kneeL],[z_footL,z_kneeL]]
skeleton[12,:,:]=[[x_shoulderR,x_shoulderL],[y_shoulderR,y_shoulderL],[z_shoulderR,z_shoulderL]]
skeleton[13,:,:]=[[x_hipR,x_hipL],[y_hipR,y_hipL],[z_hipR,z_hipL]]
return skeleton
def plotSingle3Dframe(ax, y_true_pred, c='red', limits=[[-500, 500], [-500, 500], [0, 1500]], plot_lines=True):
" 3D plot of single frame. Can be both label or prediction "
x = y_true_pred[:, 0];
y = y_true_pred[:, 1];
z = y_true_pred[:, 2]
ax.scatter(x, y, z, zdir='z', s=20, c=c, marker='o', depthshade=True)
# plot skeleton
lines_skeleton = skeleton(x, y, z)
if plot_lines:
for l in range(len(lines_skeleton)):
ax.plot(lines_skeleton[l, 0, :], lines_skeleton[l, 1, :], lines_skeleton[l, 2, :], c)
ax.set_xlabel('X Label');
ax.set_ylabel('Y Label');
ax.set_zlabel('Z Label');
ax.set_aspect('auto')
# set same scale for all the axis
x_limits = limits[0];
y_limits = limits[1];
z_limits = limits[2]
x_range = np.abs(x_limits[1] - x_limits[0]);
x_middle = np.mean(x_limits)
y_range = np.abs(y_limits[1] - y_limits[0]);
y_middle = np.mean(y_limits)
z_range = np.abs(z_limits[1] - z_limits[0]);
z_middle = np.mean(z_limits)
# The plot bounding box is a sphere in the sense of the infinity
# norm, hence I call half the max range the plot radius.
plot_radius = 0.5 * np.max([x_range, y_range, z_range])
ax.set_xlim3d([x_middle - plot_radius, x_middle + plot_radius])
ax.set_ylim3d([y_middle - plot_radius, y_middle + plot_radius])
ax.set_zlim3d([z_middle - plot_radius, z_middle + plot_radius])
def generate_gt_heatmap(subj, sess, mov, t, P_mat_cam, images_all, vicon_xyz_all, decay_maps_flag):
# NB: the order of channels in the .aedat file (and in the saved .h5) is different from the camera index.
# The next cell takes care of this, loading the proper camera projection matrix.
vicon_xyz = vicon_xyz_all[t]
image = images_all[t, :, :, ch_idx]
# Algorithm to convert XYZ Vicon coordinates to UV pixel coordinates
# use homogeneous coordinates representation to project 3d XYZ coordinates to 2d UV pixel coordinates.
vicon_xyz_homog = np.concatenate([vicon_xyz, np.ones([1,13])], axis=0)
coord_pix_all_cam2_homog = np.matmul(P_mat_cam, vicon_xyz_homog)
coord_pix_all_cam2_homog_norm = coord_pix_all_cam2_homog/coord_pix_all_cam2_homog[-1]
u = coord_pix_all_cam2_homog_norm[0]
v = image_h - coord_pix_all_cam2_homog_norm[1] # flip v coordinate to match the image direction
# mask is used to make sure that pixel positions are in frame range.
mask = np.ones(u.shape).astype(np.float32)
mask[u>image_w] = 0
mask[u<=0] = 0
mask[v>image_h] = 0
mask[v<=0] = 0
# pixel coordinates
u = u.astype(np.int32)
v = v.astype(np.int32)
# Generate the heatmaps and plot them over the image
# initialize the heatmaps
label_heatmaps = np.zeros((image_h, image_w, num_joints))
k = 2 # constant used to better visualize the joints when not using decay
for fmidx,pair in enumerate(zip(v,u, mask)):
if decay_maps_flag:
if pair[2]==1: # write joint position only when projection within frame boundaries
label_heatmaps[pair[0],pair[1], fmidx] = 1
label_heatmaps[:,:,fmidx] = decay_heatmap(label_heatmaps[:,:,fmidx])
else:
if pair[2]==1: # write joint position only when projection within frame boundaries
label_heatmaps[(pair[0]-k):(pair[0]+k+1),(pair[1]-k):(pair[1]+k+1), fmidx] = 1
return label_heatmaps, image
# path of files generated using matlab
path_ = '/Users/asudeaydin/PycharmProjects/thesis/previous_work/DHP19/constant_count_frames/h5_dataset_7500_events/346x260'
# home directory of the repository
homedir = '/Users/asudeaydin//Resilio Sync/DHP19/'
# camera projection matrices path
P_mat_dir = join(homedir, 'P_matrices/')
# folder to save fenerated videos
video_folder = '/Users/asudeaydin/PycharmProjects/thesis/previous_work/DHP19/videos'
# Import model
modeldir='/Users/asudeaydin/PycharmProjects/thesis/previous_work/DHP19/'
trained_model=load_model(join(modeldir,'DHP_CNN.model'), custom_objects={'mse2D': mse2D})
t = 19 # timestep of image to plot
subj, sess, mov = 9, 2, 4
decay_maps_flag = True # True to blur heatmaps
ch_idx = 3 # 0 to 3. This is the order of channels in .aedat/.h5
# Which image of the selected recording to use
imgidx = 24
if ch_idx==1:
P_mat_cam = np.load(join(P_mat_dir,'P1.npy'))
elif ch_idx==3:
P_mat_cam = np.load(join(P_mat_dir,'P2.npy'))
elif ch_idx==2:
P_mat_cam = np.load(join(P_mat_dir,'P3.npy'))
elif ch_idx==0:
P_mat_cam = np.load(join(P_mat_dir,'P4.npy'))
P_mat_cam1 = np.load(join(P_mat_dir,'P1.npy'))
P_mat_cam2 = np.load(join(P_mat_dir,'P2.npy'))
P_mat_cam3 = np.load(join(P_mat_dir,'P3.npy'))
P_mat_cam4 = np.load(join(P_mat_dir,'P4.npy'))
P_mats = [P_mat_cam1, P_mat_cam2, P_mat_cam3, P_mat_cam4]
cameras_pos = np.load(join(P_mat_dir,'camera_positions.npy'))
# .h5 are the event frames
# Load input and 3D label .h5 files
datafile = 'S{}_session{}_mov{}_7500events'.format(subj, sess, mov)
path_x = join(path_,datafile+'.h5')
path_y = join(path_,datafile+'_label.h5')
x_h5 = h5py.File(path_x, 'r')
y_h5 = h5py.File(path_y, 'r')
x_h5_shape = np.shape(x_h5['DVS'])
imgidx_max = x_h5_shape[0]
# gt_2d_cam : used only to plot in 2D.
# pred_2d_cam: used for 2D and 3D.
video_name = 'pred_heatmaps.mp4'
path_video = join(video_folder, video_name)
ims = []
for img_id in range(imgidx_max):
img_cam2, gt_2d_cam2, pred_2d_cam2 = predict_CNN_extract_skeleton_2d(3, img_id, verbose=True)
plt.figure()
plt.axis('off')
plt.imshow(img_cam2, cmap='gray')
plt.plot(gt_2d_cam2[:,1], gt_2d_cam2[:,0], '.', c='red', label='gt')
plt.plot(pred_2d_cam2[:,1], pred_2d_cam2[:,0], '.', c='blue', label='pred')
plt.savefig(join(video_folder,'img_{}.png'.format(img_id)))
fig = plt.figure()
plt.axis('off')
for img_id in range(imgidx_max):
im_read = plt.imread(join(video_folder,'img_{}.png'.format(img_id)))
im = plt.imshow(im_read, animated=True)
ims.append([im])
ani = animation.ArtistAnimation(fig, ims, interval=50, blit=False, repeat = False) #20fps
ani.save(path_video)