def main():
input_file_name, model_name, show_video = check_args()
print(f"Input Video: {input_file_name}")
print(f"Model Name: {model_name}")
detector = Detector()
detector.initialize(model_name, input_file_name)
can_read_frames = True
current_frame = 0
start_time = time.time()
while can_read_frames:
current_frame += 1
can_read_frames, frame = detector.analyze_frame(current_frame)
print(f"\rAnalyzing Frame {current_frame}", end='')
if current_frame % 3 == 0:
print(" \\", end='')
elif current_frame % 3 == 1:
print(" |", end='')
elif current_frame % 3 == 2:
print(" /", end='')
if show_video and frame is not None:
cv2.imshow("Frame", frame)
cv2.waitKey(1)
analyze_time = int(time.time() - start_time)
print()
print(f"Total Frames: {current_frame}")
print(f"Analyze Time: {analyze_time}s")
print(f"Average Frames Per Second: {current_frame // analyze_time}")
class DetectorThread(QThread):
update_image_signal = pyqtSignal(np.ndarray)
video_finished_signal = pyqtSignal()
def __init__(self):
super().__init__()
self.Detector = Detector()
self.current_frame_count = 0
self.is_running = False
self.is_paused = False
self.is_video_finished = True
self.video_path = ""
self.model_name = ""
def set_video_path(self, video_path):
self.reset()
self.video_path = video_path
def set_model_name(self, model_name):
self.reset()
self.model_name = model_name
def initialize_detector(self):
if self.is_video_finished:
self.Detector.initialize(self.model_name, self.video_path)
def run(self):
self.is_running = True
while self.is_running:
while not self.is_paused:
can_analyze, analyzed_frame = self.Detector.analyze_frame(
self.current_frame_count)
self.current_frame_count += 1
if can_analyze:
self.is_video_finished = False
if analyzed_frame is not None:
self.update_image_signal.emit(analyzed_frame)
else:
self.video_finished_signal.emit()
self.is_paused = True
def reset(self):
self.is_video_finished = True
self.current_frame_count = 0
def pause(self):
self.is_paused = True
from Detector import Detector
import os
import cv2
import utils
import numpy as np
dirimgs = os.path.join('/home/xian/imaxes_politicos/Imaxes')
dircaras = os.path.join('/home/xian/imaxes_politicos/Caras')
factor = 2
detector = Detector('SSD-mobilenet-face', threshold=0.5)
detector.initialize()
people = os.listdir(dirimgs)
for person in people:
imgs = os.listdir(os.path.join(dirimgs, person))
for imgname in imgs:
print(person + ' - ' + imgname)
imaxe = cv2.imread(os.path.join(dirimgs, person, imgname))
result = detector.detect(imaxe)
if len(result) == 0:
continue
elif len(result) == 1:
detection = result[0]
else:
conf = []
for det in result:
conf.append(det.conf)
idx = np.argmax(np.float32(conf))
detection = result[idx]
class VideoProcessor:
def __init__(self, keep_history=False):
self.keep_history = keep_history
self.tracker = Tracker()
self.tracks_history = []
self.frames = []
self.detections = []
def initialize(self):
#self.detector = Detector('YOLO')
# self.detector = Detector('SSD-mobilenet-face')
self.detector = Detector('SSD-brainlab')
self.detector.initialize()
def process_frame(self, frame):
print('')
# Detect in the current frame:
self.image = frame
self.detections = self.detector.detect(frame)
# Update tracker:
self.tracker.update_tracks(self.detections)
# Update history:
if self.keep_history:
self.frames.append(frame)
self.update_history()
def get_last_frame_with_detections(self):
img_cp = self.image.copy()
print(str(len(self.tracker.get_tracks())) + ' tracks')
for tr in self.tracker.get_tracks():
detection = tr.get_last_detection()
x = int(detection.x_center)
y = int(detection.y_center)
w = int(detection.width)//2
h = int(detection.height)//2
if tr.get_nframes_missing() > 0:
print('missing frame for track ' + str(tr.get_id()) + ' - nframes_missing: ' + str(tr.get_nframes_missing()))
color = (255, 0, 0)
else:
color = (0, 255, 0)
cv2.rectangle(img_cp, (x-w,y-h), (x+w,y+h), color, 2)
cv2.rectangle(img_cp, (x-w,y-h-20), (x+w,y-h), (125,125,125), -1)
cv2.putText(img_cp, detection.class_name + ' ' + str(tr.get_id()) + ' : %.2f' % detection.conf, (x-w+5,y-h-7), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0,0,0), 1)
return img_cp
def clear(self):
self.tracker.clear()
self.tracks_history = []
self.frames = []
self.detections = []
def update_history(self):
for track in self.tracker.tracks:
exists = False
for i in range(len(self.tracks_history)):
if self.tracks_history[i].id == track.id:
exists = True
self.tracks_history[i].update(track)
break
if not exists:
self.tracks_history.append(TrackHistory(track))
