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yo-siam-1.py
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from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
import os
import argparse
import csv
import pandas as pd
import collections
import cv2
import sys
import torch
import datetime
from itertools import count
import numpy as np
from glob import glob1, glob
import json
import time
from pysot.core.config import cfg
from pysot.models.model_builder import ModelBuilder
from pysot.tracker.tracker_builder import build_tracker
import re
from pysot.utils import bbox as bbx
import argparse
import time
from sys import platform
import csv
from models import *
from utils.datasets import *
from utils.utils import *
import itertools
torch.set_num_threads(5)
def get_frames(video_name):
if video_name.endswith('avi') or \
video_name.endswith('mp4'):
vid_name = "./videos1/{}".format(video_name)
cap = cv2.VideoCapture(vid_name)#(args.video_name)
while True:
ret, frame = cap.read()
if ret:
yield frame
else:
break
else:
print('Video read fail')
# images = glob(os.path.join(video_name, '*.jp*'))
# images = sorted(images,key=lambda x: int(x.split('/')[-1].split('.')[0]))
# for img in images:
# frame = cv2.imread(img)
# yield frame
# def detect(cfg1,
# data_cfg,
# weights,
# images='data/samples', # input folder
# output='output', # output folder
# fourcc='mp4v', # video codec
# img_size=416,
# conf_thres=0.5,
# nms_thres=0.5,
# save_txt=True,
# save_images=True,
# webcam=False
def main():
cfg1 = './cfg/yolov3-spp.cfg'
data_cfg = './data/coco.data'
weights = './weights/yolov3-spp.weights'
images = './videos1'
output='output'
fourcc='mp4v'
img_size=416
conf_thres=0.5
nms_thres = 0.5
save_images=True
#yolo Initialize
device = torch_utils.select_device()
torch.backends.cudnn.benchmark = False # set False for reproducible results
if os.path.exists(output):
shutil.rmtree(output) # delete output folder
os.makedirs(output) # make new output folder
#yolo Initialize model
if ONNX_EXPORT:
s = (320, 192) # (320, 192) or (416, 256) or (608, 352) onnx model image size (height, width)
model = Darknet(cfg1, s)
else:
model = Darknet(cfg1, img_size)
#yolo Load weights
if weights.endswith('.pt'): # pytorch format
model.load_state_dict(torch.load(weights, map_location=device)['model'])
else: # darknet format
_ = load_darknet_weights(model, weights)
#yolo Fuse Conv2d + BatchNorm2d layers
model.fuse()
#yolo Eval mode
model.to(device).eval()
if ONNX_EXPORT:
img = torch.zeros((1, 3, s[0], s[1]))
torch.onnx.export(model, img, 'weights/export.onnx', verbose=True)
return
#Set Dataloader
vid_path, vid_writer = None, None
dataloader = LoadImages(images, img_size=img_size)
# Get classes and colors
classes = load_classes(parse_data_cfg(data_cfg)['names'])
colors = [[random.randint(0, 255) for _ in range(3)] for _ in range(len(classes))]
for i, (path, img, im0, vid_cap) in enumerate(dataloader):
the_list = []
t = time.time()
save_path = str(Path(output) / Path(path).name)
video_name_yolo = os.path.basename(save_path)
frame_count = 0
ignore = 15
# Get detections
frame_count = frame_count + 15
img = torch.from_numpy(img).unsqueeze(0).to(device)
pred, _ = model(img)
det = non_max_suppression(pred, conf_thres, nms_thres)[0]
if det is not None and len(det) > 0:
# Rescale boxes from 416 to true image size
det[:, :4] = scale_coords(img.shape[2:], det[:, :4], im0.shape).round()
for *xyxy,conf,cls_conf,cls in det:
if cls == 0:
if conf >= 0.75:
my_list_det = ('%g,' * 4) % (*xyxy,)
my_list_list = [my_list_det]
my_list_strp = my_list_list[0][:-1]
my_list_int = np.array(my_list_strp.split(",")).astype('int').tolist()
my_int = np.array(my_list_strp.split(","))
the_list.append(my_list_int)
with open('./videos1/'+video_name_yolo+'_yolo.csv', 'a', newline='') as csvfile:
writer = csv.writer(csvfile, quoting=0, delimiter = ",")#,quotechar='',escapechar='')
writer.writerow(my_int)
else:
continue
else:
continue
# if save_images: # Save image with detections
# if dataloader.mode == 'images':
# cv2.imwrite(save_path, im0)
# else:
# if vid_path != save_path: # new video
# vid_path = save_path
# if isinstance(vid_writer, cv2.VideoWriter):
# vid_writer.release() # release previous video writer
# fps = vid_cap.get(cv2.CAP_PROP_FPS)
# width = int(vid_cap.get(cv2.CAP_PROP_FRAME_WIDTH))
# height = int(vid_cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
# vid_writer = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*fourcc), fps, (width, height))
# vid_writer.write(im0)
# def remove(string):
# return string.replace(" ", ",")
# the_list_app = []
# for x in the_list:
# the_list_r = remove(x)
# the_list_app.append(the_list_r)
# print(the_list_app)
# if len(master_tracking_list) == 0:
# a = [the_list[0]]
# for track_cor in master_tracking_list:
# a = [remove(cordinate) if cordinate <= 0.20 else print('ignored') for cordinate in [bbox.IoU(track_cor,cor) for cor in the_list]]
#siam load config
cfg.merge_from_file('./experiments/siamrpn_r50_l234_dwxcorr_8gpu/config.yaml')
cfg.CUDA = torch.cuda.is_available()
#device = torch.device('cuda' if cfg.CUDA else 'cpu')
#siam create model
model = ModelBuilder()
#siam load model
model.load_state_dict(torch.load('./experiments/siamrpn_r50_l234_dwxcorr_8gpu/model.pth', map_location=lambda storage, loc: storage.cpu()))
model.eval().to(device)
#siam build tracker
tracker = build_tracker(model)
#video_list = []
video_list_mp4 = glob1("./videos1", "*.mp4")
#video_list.append(video_list_mp4)
video_list_avi = glob1("./videos1","*.avi")
#video_list.append(video_list_avi)
#print(video_list)
for video_name in itertools.chain(video_list_mp4, video_list_avi):
print(video_name)
#with open('./demo/vids/'+video_name+'_yolo.csv') as vid:
#df = pd.read_csv('./demo/vids/'+video_name+'_yolo.csv', delimiter = ',', dtype=int)
# read = csv.reader(vid, delimiter = ',', newline='')
# for row in read:
# cords = row[0]
#print(cords)
#cords = [list(x) for x in df.values]
#cords = [1,2,3,4]
try:
df = pd.read_csv('./videos1/'+video_name+'_yolo.csv', delimiter=',', header=None)
cords = [list(x) for x in df.values]
except:
continue
master_tracking_list = []
object_counter = 0
for cord in cords:
cord = [cord[0],cord[1],cord[2]-cord[0],cord[3]-cord[1]]
try:
iou = [bbx.IoU(track_cor,cord) for track_cor in master_tracking_list]
except:
pass
if len(iou) == 0:
master_tracking_list.append(cord)
iou = [0.0]
if max(iou) <= 0.3:
object_counter = object_counter + 1
first_frame = True
# if video_name:#args.video_name:
# video_name = video_name.split('/')[-1].split('.')[0]
# print(video_name)
# #video_name = args.video_name.split('/')[-1].split('.')[0]
# else:
# exit()
#print(cord)
frame_count = 0
#mylist = [frame_count,object_counter,cord[0],cord[1],cord[2],cord[3],video_name]
for frame in get_frames(video_name):#(args.video_name):
if first_frame:
try:
init_rect = cord
except:
exit()
tracker.init(frame, init_rect)
first_frame = False
else:
outputs = tracker.track(frame)
if 'polygon' in outputs:
exit()
else:
#crds = map(int,outputs['bbox'])
bbox = list(map(int,outputs['bbox']))
master_tracking_list.append(bbox)
#cv2.rectangle(frame,(bbox[0],bbox[1]),(bbox[0]+bbox[2],bbox[1]+bbox[3]),(0,255,0),3)
frame_count = frame_count + 1
mylist = [frame_count,object_counter,bbox[0],bbox[1],bbox[2],bbox[3],video_name]
#cv2.imshow(video_name, frame)
#cv2.waitKey(40)
with open('./videos1/vid-'+str(video_name)+'-tracking-'+str(object_counter)+'-object-'+str(cord)+'.csv', 'a', newline='') as csvfile:
writer = csv.writer(csvfile, quoting=0)#,quotechar='',escapechar='')
writer.writerow(mylist)
else:
print("Coordinate Ignored")
continue
if __name__ == '__main__':
main()
# if __name__ == '__main__':
# parser = argparse.ArgumentParser()
# parser.add_argument('--cfg1', type=str, default='cfg/yolov3-spp.cfg', help='cfg file path')
# parser.add_argument('--data-cfg', type=str, default='data/coco.data', help='coco.data file path')
# parser.add_argument('--weights', type=str, default='weights/yolov3-spp.weights', help='path to weights file')
# parser.add_argument('--images', type=str, default='data/samples', help='path to images')
# parser.add_argument('--img-size', type=int, default=416, help='inference size (pixels)')
# parser.add_argument('--conf-thres', type=float, default=0.5, help='object confidence threshold')
# parser.add_argument('--nms-thres', type=float, default=0.5, help='iou threshold for non-maximum suppression')
# parser.add_argument('--fourcc', type=str, default='mp4v', help='fourcc output video codec (verify ffmpeg support)')
# parser.add_argument('--output', type=str, default='output', help='specifies the output path for images and videos')
# opt = parser.parse_args()
# print(opt)
# with torch.no_grad():
# detect(opt.cfg1,
# opt.data_cfg,
# opt.weights,
# images=opt.images,
# img_size=opt.img_size,
# conf_thres=opt.conf_thres,
# nms_thres=opt.nms_thres,
# fourcc=opt.fourcc,
# output=opt.output)