150 lines
5.4 KiB
Python
Executable File
150 lines
5.4 KiB
Python
Executable File
#! /usr/bin/env python
|
|
|
|
import time
|
|
import os
|
|
import argparse
|
|
import json
|
|
import cv2
|
|
import sys
|
|
sys.path += [os.path.abspath('keras-yolo3-master')]
|
|
|
|
from utils.utils import get_yolo_boxes, makedirs
|
|
from utils.bbox import draw_boxes
|
|
from keras.models import load_model
|
|
from tqdm import tqdm
|
|
import numpy as np
|
|
|
|
|
|
def _main_(args):
|
|
|
|
config_path = args.conf
|
|
input_path = args.input
|
|
output_path = args.output
|
|
|
|
with open(config_path) as config_buffer:
|
|
config = json.load(config_buffer)
|
|
|
|
makedirs(output_path)
|
|
|
|
###############################
|
|
# Set some parameter
|
|
###############################
|
|
net_h, net_w = 416, 416 # a multiple of 32, the smaller the faster
|
|
obj_thresh, nms_thresh = 0.8, 0.3
|
|
|
|
###############################
|
|
# Load the model
|
|
###############################
|
|
os.environ['CUDA_VISIBLE_DEVICES'] = config['train']['gpus']
|
|
infer_model = load_model(config['train']['saved_weights_name'])
|
|
|
|
###############################
|
|
# Predict bounding boxes
|
|
###############################
|
|
if 'webcam' in input_path: # do detection on the first webcam
|
|
video_reader = cv2.VideoCapture(0)
|
|
|
|
# the main loop
|
|
batch_size = 1
|
|
images = []
|
|
while True:
|
|
ret_val, image = video_reader.read()
|
|
if ret_val == True: images += [image]
|
|
|
|
if (len(images)==batch_size) or (ret_val==False and len(images)>0):
|
|
batch_boxes = get_yolo_boxes(infer_model, images, net_h, net_w, config['model']['anchors'], obj_thresh, nms_thresh)
|
|
|
|
for i in range(len(images)):
|
|
draw_boxes(images[i], batch_boxes[i], config['model']['labels'], obj_thresh)
|
|
cv2.imshow('video with bboxes', images[i])
|
|
images = []
|
|
if cv2.waitKey(1) == 27:
|
|
break # esc to quit
|
|
cv2.destroyAllWindows()
|
|
elif input_path[-4:] == '.mp4': # do detection on a video
|
|
video_out = output_path + input_path.split('/')[-1]
|
|
video_reader = cv2.VideoCapture(input_path)
|
|
|
|
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))
|
|
|
|
video_writer = cv2.VideoWriter(video_out,
|
|
cv2.VideoWriter_fourcc(*'MPEG'),
|
|
50.0,
|
|
(frame_w, frame_h))
|
|
# the main loop
|
|
batch_size = 1
|
|
images = []
|
|
start_point = 0 #%
|
|
show_window = False
|
|
for i in tqdm(range(nb_frames)):
|
|
_, image = video_reader.read()
|
|
|
|
if (float(i+1)/nb_frames) > start_point/100.:
|
|
images += [image]
|
|
|
|
if (i%batch_size == 0) or (i == (nb_frames-1) and len(images) > 0):
|
|
# predict the bounding boxes
|
|
batch_boxes = get_yolo_boxes(infer_model, images, net_h, net_w, config['model']['anchors'], obj_thresh, nms_thresh)
|
|
|
|
for i in range(len(images)):
|
|
# draw bounding boxes on the image using labels
|
|
draw_boxes(images[i], batch_boxes[i], config['model']['labels'], obj_thresh)
|
|
|
|
# show the video with detection bounding boxes
|
|
if show_window: cv2.imshow('video with bboxes', images[i])
|
|
|
|
# write result to the output video
|
|
video_writer.write(images[i])
|
|
images = []
|
|
|
|
if show_window and cv2.waitKey(1) == 27: break # esc to quit
|
|
|
|
if show_window: cv2.destroyAllWindows()
|
|
video_reader.release()
|
|
video_writer.release()
|
|
else: # do detection on an image or a set of images
|
|
|
|
|
|
|
|
image_paths = []
|
|
|
|
if os.path.isdir(input_path):
|
|
for inp_file in os.listdir(input_path):
|
|
image_paths += [input_path + inp_file]
|
|
else:
|
|
image_paths += [input_path]
|
|
|
|
image_paths = [inp_file for inp_file in image_paths if (inp_file[-4:] in ['.jpg', '.png', 'JPEG'])]
|
|
|
|
# the main loop
|
|
times = []
|
|
|
|
for image_path in image_paths:
|
|
image = cv2.imread(image_path)
|
|
print(image_path)
|
|
start = time.time()
|
|
# predict the bounding boxes
|
|
boxes = get_yolo_boxes(infer_model, [image], net_h, net_w, config['model']['anchors'], obj_thresh, nms_thresh)[0]
|
|
print('Elapsed time = {}'.format(time.time() - start))
|
|
times.append(time.time() - start)
|
|
# draw bounding boxes on the image using labels
|
|
draw_boxes(image, boxes, config['model']['labels'], obj_thresh)
|
|
|
|
# write the image with bounding boxes to file
|
|
cv2.imwrite(output_path + image_path.split('/')[-1], np.uint8(image))
|
|
|
|
file = open(args.output + '/time.txt','w')
|
|
file.write('Tiempo promedio:' + str(np.mean(times)))
|
|
file.close()
|
|
|
|
if __name__ == '__main__':
|
|
argparser = argparse.ArgumentParser(description='Predict with a trained yolo model')
|
|
argparser.add_argument('-c', '--conf', help='path to configuration file')
|
|
argparser.add_argument('-i', '--input', help='path to an image, a directory of images, a video, or webcam')
|
|
argparser.add_argument('-o', '--output', default='output/', help='path to output directory')
|
|
|
|
args = argparser.parse_args()
|
|
_main_(args)
|