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fmo_detection.py
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fmo_detection.py
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import cv2
import math
from matplotlib import pyplot as plt
import numpy as np
import os
import time
import json
import pandas as pd
from skvideo import io
from config_fmo import cfg
def from_json(file):
"""
Yields and array of the joint trajectories of a json file
"""
coordinates = ["x", "y"]
joints_list = ["right_shoulder", "right_elbow", "right_wrist", "left_shoulder","left_elbow", "left_wrist",
"right_hip", "right_knee", "right_ankle", "left_hip", "left_knee", "left_ankle",
"right_eye", "right_ear","left_eye", "left_ear", "nose ", "neck"]
with open(file, 'r') as inf:
out = json.load(inf)
liste = []
for fr in out["frames"]:
l_joints = []
for j in joints_list[:12]:
l_coo = []
for xy in coordinates:
l_coo.append(fr[j][xy])
l_joints.append(l_coo)
liste.append(l_joints)
return np.array(liste)
def get_slope_arctan(center1, center2):
"""
old function for slope: slope defined as arctan
not recommended as vectors in the opposite direction have the same slopes
"""
y_diff = (center1[1]-center2[1])
slope = np.arctan(y_diff/(center1[0]-center2[0]))
if y_diff < 0:
slope+= np.pi
return slope
def get_slope(center1, center2):
"""
slopes represented as normalized complex vectors
"""
y_diff = (center1[1]-center2[1])
x_diff = (center1[0]-center2[0])
return np.complex(x_diff, y_diff)/np.sqrt(x_diff**2 + y_diff**2)
class Node():
"""
Design graph with motion candidates as nodes
"""
def __init__(self, x1, y1, x2, y2):
self.bbox = [x1, y1, x2, y2] # bounding box
self.l = abs(x1-x2) # length
self.w = abs(y1-y2) # width
self.angle = np.arctan(self.l/self.w) # angle of diagonals
self.area = self.l*self.w # (l+w)/float(l*w)
self.center = np.array([(x1+x2)/2, (y1+y2)/2])
self.children = []
self.area_diffs = []
self.angle_diffs = []
self.slopes = []
self.dist = []
def add_child(self, no):
dist = np.linalg.norm(no.center-self.center)
if dist>cfg.min_dist:
self.children.append(no)
self.area_diffs.append(abs(1-(no.area/self.area)))
self.slopes.append(get_slope(self.center, no.center))
self.dist.append(dist)
self.angle_diffs.append(abs(self.angle-no.angle))
def favourite_child(self, no):
self.fav_child=no
def get_difference(im_tm1, im_t, im_tp1):
"""
calculates difference image and applies threshold to detect significant motion
parameters: three consecutive frames
returns binary image of same size as input frames indicating significantly different pixels
"""
delta_plus = cv2.absdiff(im_t, im_tm1)
delta_0 = cv2.absdiff(im_tp1, im_tm1)
delta_minus = cv2.absdiff(im_t,im_tp1)
sp = cv2.meanStdDev(delta_plus)
sm = cv2.meanStdDev(delta_minus)
s0 = cv2.meanStdDev(delta_0)
# print("E(d+):", sp, "\nE(d-):", sm, "\nE(d0):", s0)
th = [
sp[0][0, 0] + 3 * math.sqrt(sp[1][0, 0]),
sm[0][0, 0] + 3 * math.sqrt(sm[1][0, 0]),
s0[0][0, 0] + 3 * math.sqrt(s0[1][0, 0]),
]
# OPENCV THRESHOLD
ret, dbp = cv2.threshold(delta_plus, th[0], 255, cv2.THRESH_BINARY)
ret, dbm = cv2.threshold(delta_minus, th[1], 255, cv2.THRESH_BINARY)
ret, db0 = cv2.threshold(delta_0, th[2], 255, cv2.THRESH_BINARY)
detect = cv2.bitwise_and(cv2.bitwise_and(dbp, dbm), cv2.bitwise_not(db0))
# nd = cv2.bitwise_not(detect)
return detect
def get_candidates(nd, min_area):
"""
find connected components in a binary difference image
nd is a binary image indicating significant pixel changes
min_area is the minimum area of pixels in nd that should be recognized as a connected region
return list of candidates, each is a tuple (left_top_corner, right_bottom_corner, area)
"""
# only stats is used, not num, labels, centroids
num, labels, stats, centroids = cv2.connectedComponentsWithStats(nd, ltype=cv2.CV_16U)
# We set an arbitrary threshold to screen out smaller "components"
# which may result simply from noise, or moving leaves, and other
# elements not of interest.
candidates = list()
for stat in stats[1:]:
area = stat[cv2.CC_STAT_AREA]
if area < min_area:
continue # Skip small objects (noise)
lt = (stat[cv2.CC_STAT_LEFT], stat[cv2.CC_STAT_TOP])
rb = (lt[0] + stat[cv2.CC_STAT_WIDTH], lt[1] + stat[cv2.CC_STAT_HEIGHT])
candidates.append((lt, rb, area))
return candidates
def first_movement(cand_list, joints):
"""
Checks whether there is a motion candidate that is sufficiently close to the ankles or knees
If yes, the frame fr is added to the list of frames (ankle_move)
:param cand_list: list of motion candidates detected in this frame
:param joints: Array of joints detected by pose estimation in this frame
"""
knees= joints[[7, 10],:]
ankles = joints[[8,11],:]
#print(knees, ankles, knees-ankles, np.mean(knees-ankles, axis=0))
dist_ankle = cfg.factor_knee_radius * np.linalg.norm(np.mean(knees-ankles, axis=0)) #//2
#print("radius", dist_ankle)
for k, cand in enumerate(cand_list):
x1, y1 = cand[0]
x2, y2 = cand[1]
center = [(x1+x2)/2, (y1+y2)/2]
norms = np.array([np.linalg.norm(center - knees[0]), np.linalg.norm(center - knees[1]), np.linalg.norm(center - ankles[0]), np.linalg.norm(center - ankles[1])])
#print("center", cand.center, "knees", knees[0],knees[1], "ankles", ankles, "norms", norms)
if np.any(norms<dist_ankle):
# print("smaller radius", center)
return True
return False
def plot(im_t, candidates, frame_nr):
"""
Plot motion candidates on image im_t
"""
#print("DETECTED", t-1, whiteness_values[-1], candidate_values[-1])
plt.figure(figsize=(10, 10), edgecolor='r')
# print(candidates[fom])
img = np.tile(np.expand_dims(im_t.copy(), axis = 2), (1,1,3))
#print(img.shape)
#for jo in ankles:
# cv2.circle(img, (int(jo[0]), int(jo[1])), 8, [255,0,0], thickness=-1)
#for kn in knees:
# cv2.circle(img, (int(kn[0]), int(kn[1])), 8, [255,0,0], thickness=-1)
# cv2.circle(img, (690, 290),8, [255,0,0], thickness=-1)
# cv2.circle(img, (50, 400), 8, [255,0,0], thickness=-1)
for can in candidates: # einfuegen falls alles plotten
cv2.rectangle(img, can[0], can[1],[255,0,0], 4)
#cv2.rectangle(img,tuple(balls[-1][:2]), tuple(balls[-1][2:]), [255,0,0], 4)
plt.imshow(img, 'gray')
# plt.axis("off")
plt.title("Detected FMO frame"+ str(frame_nr)) #+str(candidates))
# plt.savefig("first_move_sequence/"+bsp[:-4]+"_"+str(frame_nr)) # [:400,200:700]
# plt.savefig("/Users/ninawiedemann/Desktop/BA/fmo detection/connected_components", pad_inches=0)
plt.show()
# print(candidates)
def add_candidate(candidate, candidates_per_frame):
"""
For each candidate, make a new Node object to put it in the Graph
Decide for each candidate of the previous frame if it gets the new candidates as children
"""
# The first two elements of each `candidate` tuple are
# the opposing corners of the bounding box.
x1, y1 = candidate[0]
x2, y2 = candidate[1]
no = Node(x1, y1, x2, y2)
#print("area_cand[3]:", candidate[2], "area node", no.area)
candidates_per_frame[-1].append(no)
if candidates_per_frame[-2]!=[]:
for nodes_in in candidates_per_frame[-2]:
nodes_in.add_child(no)
# print("previous detection", nodes.bbox, "gets child", no.bbox)
return candidates_per_frame
def confidence(s1, s2, d1, d2):
"""
Confidence value C that indicated how likely a triple of motion candidates is a ball detection
compares slopes s1 and s2 between the motion candidates, and distances d1 and d2
"""
slope_similarity = 1 - 0.5*abs(s1-s2)
distance_similarity = min(d1/float(d2), d2/float(d1))
return slope_similarity + distance_similarity
# abs(slope-cands2.slopes[k]) + abs(1- dist/cands2.dist[k]) #+ area+cands2.area_diffs[k] + angle + cands2.angle_diffs[k]
def ball_detection(candidates_per_frame, balls):
"""
Determines for each connected (in the graph) triple of candidates whether the confidence value is
sufficiently high
:param candidates_per_frame: List of Node objects, each representing a motion candidate in a frame
:balls: list of previous ball detections
If balls is empty, each triple of candidates of the last three frames is evaluated,
If balls is not empty, just the candidates of the current frame are checked (and added to the list if C is high enough)
"""
metric_thresh = cfg.metric_thresh
if len(balls)==0:
for cands3 in candidates_per_frame[-3]:
for j, cands2 in enumerate(cands3.children): # counts through children (ebene 2)
slope = cands3.slopes[j] # slope of 3 to 2
dist = cands3.dist[j]
area = cands3.area_diffs[j]
angle = cands3.angle_diffs[j]
# print("j", j, slope, dist, area, angle)
for k, cands1 in enumerate(cands2.children):
#print("k", k, cands2.slopes[k], cands2.dist[k], cands2.area_diffs[k], cands2.angle_diffs[k])
#print("metric: ", abs(slope-cands2.slopes[k]), abs(1- dist/cands2.dist[k]), area, cands2.area_diffs[k])
confidence_value = confidence(slope, cands2.slopes[k], dist, cands2.dist[k])
if confidence_value>metric_thresh:
balls = [cands3, cands2, cands1]
# because we do not want the last one that overcomes the threshold, but the one with highest C
metric_thresh = confidence_value
else: # if ball is already detected, just compare the new candidates to the last detected ball
j = balls[-2].children.index(balls[-1])
slope = balls[-2].slopes[j] # slope of 3 to 2
dist = balls[-2].dist[j]
area = balls[-2].area_diffs[j]
angle = balls[-2].angle_diffs[j]
new_ball = None
for k, cands1 in enumerate(balls[-1].children):
#print("k", k, balls[-1].slopes[k], balls[-1].dist[k], balls[-1].area_diffs[k], balls[-1].angle_diffs[k])
#print("metric: ", abs(slope-balls[-1].slopes[k]), abs(1- dist/balls[-1].dist[k]), area, balls[-1].area_diffs[k])
confidence_value = confidence(slope, balls[-1].slopes[k], dist, balls[-1].dist[k])
# abs(slope-balls[-1].slopes[k]) + abs(1- dist/balls[-1].dist[k]) # + area+balls[-1].area_diffs[k] + angle + balls[-1].angle_diffs[k]
if confidence_value>metric_thresh:
new_ball = cands1
# because we do not want the last one that overcomes the threshold, but the one with highest C
metric_thresh = confidence_value
if new_ball is None:
balls = []
else:
balls.append(new_ball)
# if abs(slope-cands2.slopes[k]) < 0.1 and abs(dist-cands2.dist[k])<10:
return balls
def _get_max_array(array_list):
"""
returns union of the binary images in array_list
"""
resultant_array = np.zeros(array_list[0].shape)
for array in array_list:
resultant_array = np.maximum(resultant_array, array)
return resultant_array
def plot_trajectory(trajectory):
"""
Scatter plot of the ball trajectory
"""
# max_y = np.amax(trajectory[:,1])+100
# max_x = np.amax(trajectory[:,0])+10
plt.figure(figsize=(10, 5), edgecolor='r')
plt.scatter(trajectory[:, 0], trajectory[:,1])
plt.title("Ball trajectory", fontsize = 15)
# plt.ylim(max_y,0)
# plt.xlim(0,max_x)
plt.gca().invert_yaxis()
plt.show()
def plot_trajectory_on_video(vid_path, out_path, ball_trajectory, radius=10):
"""
Mark each detected ball with a red circle in a video
:param vid_path: path to the input video
:param out_path: path where to save the video
:param ball_trajectory: array of ball detections (as outputted by detect_ball function)
:param radius: radius of the circle marking the ball
"""
cap = cv2.VideoCapture(vid_path)
arr = []
i=0
count=0
while True:
ret, img = cap.read()
if ret==False:
break
ori_img = img.copy()
out_img = img.copy()
if count<len(ball_trajectory) and ball_trajectory[count,2] ==i:
scale = int(255/(count+1))
for j in range(count+1):
weight = count-j
# print(j, weight*scale)
center = tuple(ball_trajectory[j,:2].astype(int))
img = cv2.circle(ori_img, center, int(radius),[255,weight*scale,weight*scale], thickness = 2)
# print(j, weight, anti_weight)
count+=1
#plt.figure(figsize = (10,10))
#plt.imshow(img[30:, 20:])
#plt.show()
i+=1
# append multiple times to have slow motion like video
for _ in range(5):
arr.append(img[30:, 20:])
io.vwrite(out_path, arr)
def detect_ball(folder, joints_array=None, min_area = 400, plotting=True, every_x_frame=1, roi=None, refine = False):
"""
:param folder: path to the input video
:param joints_array: for first movement, this must be an array of shape nr_frames x nr_joints x nr_coordinates
(if only ball should be detected, set None)
:param min_area: minium number of pixels to find connected components in the difference image
:param plotting: if all detected candidates should be plotted on the frame
:param roi: region of interest if not the whole frame is relevant, format: list [top, bottom, left, right] with top<bottom
"""
if not os.path.exists(folder):
print("Error: video path does not exist!")
return 0
cap = cv2.VideoCapture(folder)
location = []
ankle_move=[]
balls = []
# Read first frame and put it in list every_x*2 +1 times so the difference images can be calculated
length_lists = every_x_frame*2 +1
ret, frame = cap.read()
if roi is not None:
frame = frame[roi[0]:roi[1], roi[2]:roi[3]]
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
images = [frame for _ in range(length_lists)]
motion_images = [np.zeros((len(frame), len(frame[0]))) for _ in range(length_lists)]
candidates_per_frame = [[] for _ in range(length_lists)]
t=1
first_move_found = False
# function returns
first_move_frame = 0 # if not found
ball_trajectory = []
# Timing tests
start = time.time()
tocs = []
tocs2 = []
tocs3 = []
# balls_per_frame = []
while True:
ret, frame = cap.read() # read frame
if frame is None:
break
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
if roi is not None:
frame = frame[roi[0]:roi[1], roi[2]:roi[3]]
tic2 = time.time()
candidates_per_frame.append([])
tic = time.time()
# add frame to image list
images = np.roll(np.array(images), -1, axis=0)
images[-1] = frame
# three images for getting the difference image
im_tm1 = images[-2*every_x_frame-1]
im_t = images[-every_x_frame-1]
im_tp1 = images[-1]
diff_image = get_difference(im_tm1, im_t, im_tp1)
if every_x_frame==1: # shakiness removal (only possible if every frame is taken into account)
cumulative_motion = _get_max_array(motion_images)
final_diff_image = diff_image.astype(int) - cumulative_motion.astype(int)
final_diff_image[final_diff_image < 0] = 0
else:
final_diff_image = diff_image
# get connected components from difference images
candidates = get_candidates(final_diff_image.astype(np.uint8), min_area)
# balls_per_frame.append(len(candidates)) # for timing tests
# Add candidates to the graph for GBCV
for i, candidate in enumerate(candidates):
candidates_per_frame = add_candidate(candidate, candidates_per_frame)
### BALL DETECTION:
# check if there motion was detected in the last three frames
triple = np.array([len(candidates_per_frame[-i-1]) for i in range(3)])
# 1. case: No ball detected so far, and possible triple available
if np.all(triple>0) and len(balls)==0:
balls = ball_detection(candidates_per_frame, balls) # check confidence value
# 2. case: already balls detected
elif len(balls)>0:
# 2.1 possible new ball --> check metric
if len(candidates_per_frame)>0:
new_balls = ball_detection(candidates_per_frame, balls)
# 2.2 no new candidates
else:
new_balls = []
# --> if the confidence value was too low for all candidates: add the current ball list to the final ball_trajectory list
if len(new_balls)==0:
# Evaluate confidence value if there were balls detected earlier on
mean_slope = np.mean(np.array([get_slope(balls[i].center, balls[i+1].center) for i in range(len(balls)-1)]))
if len(ball_trajectory)!=0:
mean_slope_previous = np.mean(np.array([get_slope(ball_trajectory[i], ball_trajectory[i+1]) for i in range(len(ball_trajectory)-1)]))
else:
mean_slope_previous = mean_slope
# print("slopes", mean_slope, mean_slope_previous)
if abs(mean_slope-mean_slope_previous)<0.4:
for i, b in enumerate(balls):
# -1 because one behind because in the current frame no ball is detected anymore,
# and every_x_frame because difference image always lags one behind
frame_count = t-1-every_x_frame-len(balls)+i
# it might happen that the previous ball detection go until frame x, and then the ball is lost,
# but the next ball detection starts from frame x again. Thus, check if x already in the ball_trajectory
if len(ball_trajectory)==0 or frame_count not in np.asarray(ball_trajectory)[:,2]:
ball_trajectory.append([b.center[0], b.center[1], frame_count])
balls = [] # new ball list
else: # new ball found --> add to ball list, continue
balls = new_balls
# 3. case: no balls detected so far, and no suitable triple of nodes in the last three frames
else:
balls=[] # balls stay empty
tocs2.append(time.time()-tic2)
# FIRST MOVEMENT
tic3 = time.time()
if not first_move_found and joints_array is not None:
if candidates!=[]:
# check if any of the candidates is sufficiently close to knees or ankles
motion_close_to_leg = first_movement(candidates, joints_array[t-every_x_frame])
if motion_close_to_leg:
ankle_move.append(t) # add current frame index (t) to sequence if there was a motion close to the leg
# check if sequence is long enough and fulfills the threshold requirements
if len(ankle_move)>=cfg.min_length_first and t-ankle_move[-cfg.min_length_first]<cfg.max_frames_first_move: #len(ankle_move)==3:
# print("first movement frame: ", (ankle_move[-min_length_first]))
# plot(im_t, shifted_candidates, t)
first_move_found = True
first_move_frame = ankle_move[-cfg.min_length_first]
if refine:
RADIUS_LOWER = min(cfg.refine_range, first_move_frame)
range_joints = joints_array[first_move_frame - RADIUS_LOWER: first_move_frame + cfg.refine_range]
mean_leg_position = np.mean(range_joints[:, [7,8,10,11],1], axis = 1)
### gradient plotting
# plt.plot(grad[:,:,1])
# plt.plot(mean_gradient, c="black")
# plt.title("black: mean height of knees and ankles")
# plt.show()
first_move_frame = first_move_frame - RADIUS_LOWER + np.argmin(mean_leg_position)
# print("Refined first movement", first_move_frame)
tocs3.append(time.time()-tic3)
if plotting and len(candidates)>0: #len(balls)>0: # ##
plot(im_t, candidates, t-every_x_frame)
t+=1
motion_images = np.roll(np.array(motion_images), -1, axis=0)
motion_images[-1] = diff_image
toc = time.time()
tocs.append(toc-tic)
## FOR TIMING TESTS
#print("average candidates", np.mean(balls_per_frame))
#print("insgesamt", np.mean(tocs))
#print("for ball", np.mean(tocs2))
#print("first move", np.mean(tocs3))
#print("time for %s frames"%t, (time.time() - start) * 1000)
# calculate back to normal frame size
if roi is not None:
for b in ball_trajectory:
b[0]+= roi[2]
b[1]+= roi[0]
new_cand_list = []
for i, cand_list in enumerate(candidates_per_frame[length_lists+1:]):
# cands = [[cand.bbox[:2], cand.bbox[2:]] for cand in cand_list]
cands = [[[float(cand.bbox[0]),float(cand.bbox[1])],[float(cand.bbox[2]),float(cand.bbox[3])]] for cand in cand_list]
new_cand_list.append(cands)
return np.array(ball_trajectory), first_move_frame, new_cand_list # candidates_per_frame[length_lists+1:]