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frame.py
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frame.py
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from camera import CAMERA
from yolo_model import BoundBox, YOLO
from utils.bbox import bbox_iou
from lane_detection import LANE_DETECTION, OBSTACLE,obstructions,create_queue, plt
import numpy as np
import cv2
from datetime import datetime
from tqdm import tqdm
class FRAME :
fps:float
camera : CAMERA
yolo : classmethod
PERSP_PERIOD = 100000
YOLO_PERIOD = 2 # SECONDS
verbose = 3
yellow_lower = np.uint8([ 20, 50, 50]),
yellow_upper = np.uint8([35, 255, 255]),
white_lower = np.uint8([ 0, 200, 0]),
white_upper = np.uint8([180, 255, 100]),
lum_factor = 150,
max_gap_th = 2/5,
lane_start=[0.35,0.75]
ego_vehicle_offset = 0
time = datetime.utcnow().timestamp()
l_gap_skipped = 0
l_breached = 0
l_reset = 0
l_appended = 0
n_gap_skipped = 0
n_breached = 0
n_reset = 0
n_appended = 0
_defaults = {
"id": 0,
"first": True,
"speed": 0,
"n_objects" :0,
"camera" : CAMERA(),
"image" : [],
"LANE_WIDTH" : 3.66,
"fps" :22,
"ego_vehicle_offset" : 0,
'verbose' : 3,
'YOLO_PERIOD' : 2,
"yellow_lower" : np.uint8([ 20, 50, 50]),
"yellow_upper" : np.uint8([35, 255, 255]),
"white_lower" : np.uint8([ 0, 200, 0]),
"white_upper" : np.uint8([180, 255, 100]),
"lum_factor" : 150,
"max_gap_th" : 2/5,
"lane_start":[0.35,0.75] ,
"verbose" : 3
}
@classmethod
def get_defaults(cls, n):
if n in cls._defaults:
return cls._defaults[n]
else:
return "Unrecognized attribute name '" + n + "'"
def __init__(self, **kwargs):
# calc pers => detect cars and dist > detect lanes
self.__dict__.update(self._defaults) # set up default values
self.__dict__.update(kwargs) # and update with user overrides
self.speed = self.get_speed()
### IMAGE PROPERTIES
self.image : np.ndarray
# if self.image.size ==0 :
# raise ValueError("No Image")
self.img_shp : (int, int)
self.area : int
self.temp_dir = './images/detection/'
self.perspective_done_at = datetime.utcnow().timestamp()
# self.image = self.camera.undistort(self.image)
### OBJECT DETECTION AND TRACKING
self.yolo = YOLO()
self.first_detect = True
self.obstacles :[OBSTACLE] =[]
self.__yp = int(self.YOLO_PERIOD*self.fps)
### LANE FINDER
self.count = 0
self.lane :LANE_DETECTION = None
def perspective_tfm(self , pos) :
now = datetime.utcnow().timestamp()
if now - self.perspective_done_at > self.PERSP_PERIOD :
self.lane = LANE_DETECTION(self.image,self.fps,verbose=self.verbose)
return cv2.perspectiveTransform(pos, self.lane.trans_mat)
def determine_stats(self):
n = 30
t = datetime.utcnow().timestamp()
dt = int(t - self.time)
if self.count % (self.fps * n) == 0:
self.n_gap_skipped = int((self.lane.n_gap_skip - self.l_gap_skipped) *100 / (self.fps * n))
self.n_appended = int((self.lane.lane.appended - self.l_appended) *100 / (self.fps * n))
self.n_breached = int((self.lane.lane.breached - self.l_breached) *100 / (self.fps * n))
self.n_reset = int((self.lane.lane.reset - self.l_reset) *100 / (self.fps * n))
self.l_gap_skipped = self.lane.n_gap_skip
self.l_appended = self.lane.lane.appended
self.l_breached = self.lane.lane.breached
self.l_reset = self.lane.lane.reset
print("SKIPPED {:d}% BREACHED {:d}% RESET {:d}% APPENDED {:d}% | Time {:d}s , Processing FPS {:.2f} vs Desired FPS {:.2f} "\
.format(self.n_gap_skipped, self.n_breached, self.n_reset, self.n_appended,\
dt, self.fps * n / dt, self.fps ))
self.time=t
def get_speed(self):
return 30
def process_and_plot(self,image):
self.update_trackers(image)
lane_img = self.lane.process_image( image, self.obstacles)
self.determine_stats()
return lane_img
@staticmethod
def corwh2box(corwh):
box=BoundBox( int(corwh[0]), int(corwh[1]), int(corwh[0] + corwh[2]), int(corwh[1] + corwh[3]))
return box
def tracker2object(self, boxes : [OBSTACLE], th = 0.5) :
n_b = len(boxes)
n_o = len(self.obstacles)
iou_mat = np.zeros((n_o,n_b))
for i in range(n_o):
for j in range(n_b):
iou_mat[i,j] = bbox_iou(self.obstacles[i],boxes[j])
count = min(n_b,n_o)
used = []
idmax = 0
obstacles =[]
while count >0 :
r,k = np.unravel_index(np.argmax(iou_mat, axis=None), iou_mat.shape)
if iou_mat[r,k] > th :
used.append(k)
obstacle = self.obstacles[r]
box = boxes[k]
if idmax < obstacle._id :
idmax = obstacle._id
obstacle.update_box(box)
obstacles.append(obstacle)
iou_mat[r,:] = -99
iou_mat[:,k] = -99
count = count -1
idx = range(n_b)
idx = [elem for elem in idx if elem not in used]
self.obstacles = obstacles
for i, c in enumerate(idx):
# dst = self.calculate_position(boxes[c])
obstacle = OBSTACLE(boxes[c],i+idmax+1)
self.obstacles.append(obstacle)
return
def update_trackers(self, img):
image = img.copy()
for n, obs in enumerate(self.obstacles):
success, corwh = obs.tracker.update(image)
if not success :
del self.obstacles[n]
continue
box = self.corwh2box(corwh)
# dst = self.calculate_position( box)
self.obstacles[n].update_coord(box)
if self.count% self.__yp == 0 :
boxes= self.yolo.make_predictions(image,obstructions = obstructions,plot=True)
self.tracker2object(boxes)
image = cv2.cvtColor(np.array(image), cv2.COLOR_RGB2BGR)
n_obs = len(self.obstacles)
for i in range(n_obs):
tracker = cv2.TrackerKCF_create()#
# tracker = cv2.TrackerMIL_create()# # Note: Try comparing KCF with MIL
box = self.obstacles[i]
bbox = (box.xmin, box.ymin, box.xmax-box.xmin, box.ymax-box.ymin)
# print(bbox)
success = tracker.init(image, bbox )
if success :
self.obstacles[i].tracker=tracker
self.count +=1
return
def warp(self, img):
now = datetime.utcnow().timestamp()
if now - self.perspective_done_at > self.PERSP_PERIOD :
self.lane = LANE_DETECTION(self.image,self.fps)
return cv2.warpPerspective(img, self.lane.trans_mat, self.lane.UNWARPED_SIZE, flags=cv2.WARP_FILL_OUTLIERS+cv2.INTER_CUBIC)
def unwarp(self, img):
now = datetime.utcnow().timestamp()
if now - self.perspective_done_at > self.PERSP_PERIOD :
self.lane = LANE_DETECTION(self.image,self.fps)
return cv2.warpPerspective(img, self.lane.trans_mat, self.img_shp, flags=cv2.WARP_FILL_OUTLIERS +
cv2.INTER_CUBIC+cv2.WARP_INVERSE_MAP)
def process_video(self, file_path, fps_factor,\
video_out = "videos/output11.mov",pers_frame_time =14,\
t0 =None , t1 =None ):
video_reader = cv2.VideoCapture(file_path)
fps_actual = video_reader.get(cv2.CAP_PROP_FPS)
self.fps = fps_actual//fps_factor
nb_frames = int(video_reader.get(cv2.CAP_PROP_FRAME_COUNT))
frame_h = int(video_reader.get(cv2.CAP_PROP_FRAME_HEIGHT))
frame_w = int(video_reader.get(cv2.CAP_PROP_FRAME_WIDTH))
frame_h_out = int(frame_h*(1-self.ego_vehicle_offset))
print("{:s} WIDTH {:d} HEIGHT {:d} FPS {:.2f} DUR {:.1f} s".format(\
file_path,frame_w,frame_h,fps_actual,nb_frames//fps_actual
))
video_writer = cv2.VideoWriter(video_out,cv2.VideoWriter_fourcc('m', 'p', '4', 'v'),self.fps, (frame_w, frame_h_out))
#180# 310# seconds
pers_frame = int(pers_frame_time *fps_actual)
video_reader.set(1,pers_frame)
_, self.image = video_reader.read()
self.image = self.image[:frame_h_out,:,:]
self.img_shp = (self.image.shape[1], self.image.shape[0] )
# self.ego_vehicle_offset = self.img_shp[0]*int(1-self.ego_vehicle_offset)
self.area = self.img_shp[0]*self.img_shp[1]
self.lane = LANE_DETECTION(self.image, self.fps,\
verbose=self.verbose,
yellow_lower =self.yellow_lower,
yellow_upper = self.yellow_upper,
white_lower = self.white_lower,
white_upper = self.white_upper,
lum_factor = self.lum_factor,
max_gap_th = self.max_gap_th,
lane_start=self.lane_start ,
)
t1 = t1 if t1 is not None else nb_frames/fps_actual
t0 = t0 if t0 is not None else pers_frame_time
video_reader.set(1,t0*fps_actual)
for i in tqdm(range(int(t0*fps_actual), int(t1*fps_actual)),mininterval=3):
status, image = video_reader.read()
if status and (i % fps_factor == 0 ) :
image = image[:frame_h_out,:,:]
try :
procs_img = self.process_and_plot(image)
video_writer.write(procs_img)
except :
print("\n\rGOT EXEPTION TO PROCES THE IMAGE\033[F", self.count)
# l1 = self.lane.white_lower[1]
# self.lane.compute_bounds(image)
# print(l1,"->",self.lane.white_lower[1])
print("SKIPPED {:d} BREACHED {:d} RESET {:d} APPENDED {:d} | Total {:d} ".\
format(self.lane.n_gap_skip, self.lane.lane.breached,\
self.lane.lane.reset,self.lane.lane.appended, self.count))
print("SAVED TO ", video_out)
video_reader.release()
video_writer.release()
cv2.destroyAllWindows()
def vehicle_speed(self) :
return
if __name__ == "__main__":
# file_path = "videos/challenge_video.mp4" # 145
# file_path = "videos/challenge_video_edit.mp4" #145
# file_path = "videos/harder_challenge_video.mp4"
# file_path = "videos/nice_road.mp4" #110 62
file_path = "videos/us-highway.mp4" #118 143
# file_path = "videos/nh60.mp4" # 118 18
video_out = "videos/output11.mov"
frame = FRAME(
ego_vehicle_offset = .15,
yellow_lower = np.uint8([ 20, 50, 100]),
yellow_upper = np.uint8([35, 255, 255]),
white_lower = np.uint8([ 0, 200, 0]),
white_upper = np.uint8([180, 255, 100]),
lum_factor = 118,
max_gap_th = 0.45,
YOLO_PERIOD = .25,
lane_start=[0.35,0.75] ,
verbose = 3)
frame.process_video(file_path, 1,\
video_out = video_out,pers_frame_time =144,\
t0 =144 , t1 =150)#None)