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Commit 319f9a47 authored by Jappe Franke's avatar Jappe Franke
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added a few clear comments

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video.py
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from __future__ import division
import time
import torch
import torch.nn as nn
from torch.autograd import Variable
import numpy as np
import cv2
from util import *
from darknet import Darknet
from preprocess import prep_image, inp_to_image, letterbox_image
import pandas as pd
import pickle as pkl
import argparse
def get_test_input(input_dim, CUDA):
img = cv2.imread("dog-cycle-car.png")
img = cv2.resize(img, (input_dim, input_dim))
img_ = img[:,:,::-1].transpose((2,0,1))
img_ = img_[np.newaxis,:,:,:]/255.0
img_ = torch.from_numpy(img_).float()
img_ = Variable(img_)
if CUDA:
img_ = img_.cuda()
return img_
def prep_image(img, inp_dim):
"""
Prepare image for inputting to the neural network.
Returns a Variable
"""
orig_im = img
dim = orig_im.shape[1], orig_im.shape[0]
img = (letterbox_image(orig_im, (inp_dim, inp_dim)))
img_ = img[:,:,::-1].transpose((2,0,1)).copy()
img_ = torch.from_numpy(img_).float().div(255.0).unsqueeze(0)
return img_, orig_im, dim
def write(x, img, olen):
c1 = tuple(x[1:3].int())
c2 = tuple(x[3:5].int())
#classname readout x[-1]
cls = int(x[-1])
#confidence readout x[-2]
label = "{0}:{1:.2f}".format(classes[cls],x[-2])
strolen= "{0}".format(olen)
#just one color for every frame; no rainbowboxing
color = (255,0,0)
#color = random.choice(colors)
cv2.rectangle(img, c1, c2,color, 1)
#counter background
#cv2.rectangle(img, (0,10), (60,60),color, -1)
t_size = cv2.getTextSize(label, cv2.FONT_HERSHEY_PLAIN, 1 , 1)[0]
c2 = c1[0] + t_size[0] + 3, c1[1] - t_size[1] - 4
cv2.rectangle(img, c1, c2,color, -1)
#counter
cv2.putText(img,
strolen,
(2,50),
cv2.FONT_HERSHEY_DUPLEX,
1.4,
[0,0,255],
2,
cv2.LINE_AA)
cv2.putText(img,
label,
(c1[0], c1[1] - 2.5),
cv2.FONT_HERSHEY_DUPLEX,
0.5,
[225,255,255],
1,
cv2.LINE_AA)
return img
def arg_parse():
"""
Parse arguments to the detect module
"""
parser = argparse.ArgumentParser(description='YOLO v3 Video Detection Module')
parser.add_argument("--video", dest = 'video', help =
"Video to run detection upon",
default = "video.avi", type = str)
parser.add_argument("--dataset", dest = "dataset", help = "Dataset on which the network has been trained", default = "pascal")
parser.add_argument("--confidence", dest = "confidence", help = "Object Confidence to filter predictions", default = 0.5)
parser.add_argument("--nms_thresh", dest = "nms_thresh", help = "NMS Threshhold", default = 0.4)
parser.add_argument("--cfg", dest = 'cfgfile', help =
"Config file",
default = "cfg/yolov3.cfg", type = str)
parser.add_argument("--weights", dest = 'weightsfile', help =
"weightsfile",
default = "yolov3.weights", type = str)
parser.add_argument("--reso", dest = 'reso', help =
"Input resolution of the network. Increase to increase accuracy. Decrease to increase speed",
default = "416", type = str)
return parser.parse_args()
if __name__ == '__main__':
args = arg_parse()
confidence = float(args.confidence)
nms_thesh = float(args.nms_thresh)
start = 0
#CUDA = torch.cuda.is_available()
num_classes = 80
CUDA = torch.cuda.is_available()
bbox_attrs = 5 + num_classes
print("Loading network......")
model = Darknet(args.cfgfile)
model.load_weights(args.weightsfile)
print("Network successfully loaded")
model.net_info["height"] = args.reso
inp_dim = int(model.net_info["height"])
assert inp_dim % 32 == 0
assert inp_dim > 32
if CUDA:
model.cuda()
model(get_test_input(inp_dim, CUDA), CUDA)
model.eval()
videofile = args.video
cap = cv2.VideoCapture(videofile)
assert cap.isOpened(), 'Cannot capture source'
frames = 0
start = time.time()
while cap.isOpened():
ret, frame = cap.read()
if ret:
img, orig_im, dim = prep_image(frame, inp_dim)
im_dim = torch.FloatTensor(dim).repeat(1,2)
if CUDA:
im_dim = im_dim.cuda()
img = img.cuda()
with torch.no_grad():
output = model(Variable(img), CUDA)
output = write_results(output, confidence, num_classes, nms = True, nms_conf = nms_thesh)
if type(output) == int:
frames += 1
print("FPS:{:5.2f}".format( frames / (time.time() - start)))
cv2.imshow("frame", orig_im)
key = cv2.waitKey(1)
if key & 0xFF == ord('q'):
break
continue
im_dim = im_dim.repeat(output.size(0), 1)
scaling_factor = torch.min(inp_dim/im_dim,1)[0].view(-1,1)
output[:,[1,3]] -= (inp_dim - scaling_factor*im_dim[:,0].view(-1,1))/2
output[:,[2,4]] -= (inp_dim - scaling_factor*im_dim[:,1].view(-1,1))/2
output[:,1:5] /= scaling_factor
for i in range(output.shape[0]):
output[i, [1,3]] = torch.clamp(output[i, [1,3]], 0.0, im_dim[i,0])
output[i, [2,4]] = torch.clamp(output[i, [2,4]], 0.0, im_dim[i,1])
classes = load_classes('data/coco.names')
colors = pkl.load(open("pallete", "rb"))
list(map(lambda x: write(x, orig_im, len(output)), output))
cv2.imshow("frame", orig_im)
key = cv2.waitKey(1)
if key & 0xFF == ord('q'):
break
frames += 1
print("FPS:{:5.2f}".format( frames / (time.time() - start)))
else:
break
from __future__ import division
import time
import torch
import torch.nn as nn
from torch.autograd import Variable
import numpy as np
import cv2
from util import *
from darknet import Darknet
from preprocess import prep_image, inp_to_image, letterbox_image
import pandas as pd
import pickle as pkl
import argparse
def get_test_input(input_dim, CUDA):
img = cv2.imread("dog-cycle-car.png")
img = cv2.resize(img, (input_dim, input_dim))
img_ = img[:,:,::-1].transpose((2,0,1))
img_ = img_[np.newaxis,:,:,:]/255.0
img_ = torch.from_numpy(img_).float()
img_ = Variable(img_)
if CUDA:
img_ = img_.cuda()
return img_
def prep_image(img, inp_dim):
"""
Prepare image for inputting to the neural network.
Returns a Variable
"""
orig_im = img
dim = orig_im.shape[1], orig_im.shape[0]
img = (letterbox_image(orig_im, (inp_dim, inp_dim)))
img_ = img[:,:,::-1].transpose((2,0,1)).copy()
img_ = torch.from_numpy(img_).float().div(255.0).unsqueeze(0)
return img_, orig_im, dim
def write(x, img, olen):
c1 = tuple(x[1:3].int())
c2 = tuple(x[3:5].int())
#classname readout x[-1]
cls = int(x[-1])
#confidence readout x[-2] and add it to the label
label = "{0}:{1:.2f}".format(classes[cls],x[-2])
strolen= "{0}".format(olen)
#just one color for every frame; no rainbowboxing :)
color = (255,0,0)
#color = random.choice(colors)
cv2.rectangle(img, c1, c2,color, 1)
#counter background
#cv2.rectangle(img, (0,10), (60,60),color, -1)
t_size = cv2.getTextSize(label, cv2.FONT_HERSHEY_PLAIN, 1 , 1)[0]
c2 = c1[0] + t_size[0] + 3, c1[1] - t_size[1] - 4
cv2.rectangle(img, c1, c2,color, -1)
#counter
cv2.putText(img,
strolen,
(2,50),
cv2.FONT_HERSHEY_DUPLEX,
1.4,
[0,0,255],
2,
cv2.LINE_AA)
cv2.putText(img,
label,
(c1[0], c1[1] - 2.5),
cv2.FONT_HERSHEY_DUPLEX,
0.5,
[225,255,255],
1,
cv2.LINE_AA)
return img
def arg_parse():
"""
Parse arguments to the detect module
"""
parser = argparse.ArgumentParser(description='YOLO v3 Video Detection Module')
parser.add_argument("--video", dest = 'video', help =
"Video to run detection upon",
default = "video.avi", type = str)
parser.add_argument("--dataset", dest = "dataset", help = "Dataset on which the network has been trained", default = "pascal")
parser.add_argument("--confidence", dest = "confidence", help = "Object Confidence to filter predictions", default = 0.5)
parser.add_argument("--nms_thresh", dest = "nms_thresh", help = "NMS Threshhold", default = 0.4)
parser.add_argument("--cfg", dest = 'cfgfile', help =
"Config file",
default = "cfg/yolov3.cfg", type = str)
parser.add_argument("--weights", dest = 'weightsfile', help =
"weightsfile",
default = "yolov3.weights", type = str)
parser.add_argument("--reso", dest = 'reso', help =
"Input resolution of the network. Increase to increase accuracy. Decrease to increase speed",
default = "416", type = str)
return parser.parse_args()
if __name__ == '__main__':
args = arg_parse()
confidence = float(args.confidence)
nms_thesh = float(args.nms_thresh)
start = 0
#CUDA = torch.cuda.is_available()
num_classes = 80
CUDA = torch.cuda.is_available()
bbox_attrs = 5 + num_classes
print("Loading network......")
model = Darknet(args.cfgfile)
model.load_weights(args.weightsfile)
print("Network successfully loaded")
model.net_info["height"] = args.reso
inp_dim = int(model.net_info["height"])
assert inp_dim % 32 == 0
assert inp_dim > 32
if CUDA:
model.cuda()
model(get_test_input(inp_dim, CUDA), CUDA)
model.eval()
videofile = args.video
cap = cv2.VideoCapture(videofile)
assert cap.isOpened(), 'Cannot capture source'
frames = 0
start = time.time()
while cap.isOpened():
ret, frame = cap.read()
if ret:
img, orig_im, dim = prep_image(frame, inp_dim)
im_dim = torch.FloatTensor(dim).repeat(1,2)
if CUDA:
im_dim = im_dim.cuda()
img = img.cuda()
with torch.no_grad():
output = model(Variable(img), CUDA)
output = write_results(output, confidence, num_classes, nms = True, nms_conf = nms_thesh)
if type(output) == int:
frames += 1
print("FPS:{:5.2f}".format( frames / (time.time() - start)))
cv2.imshow("frame", orig_im)
key = cv2.waitKey(1)
if key & 0xFF == ord('q'):
break
continue
im_dim = im_dim.repeat(output.size(0), 1)
scaling_factor = torch.min(inp_dim/im_dim,1)[0].view(-1,1)
output[:,[1,3]] -= (inp_dim - scaling_factor*im_dim[:,0].view(-1,1))/2
output[:,[2,4]] -= (inp_dim - scaling_factor*im_dim[:,1].view(-1,1))/2
output[:,1:5] /= scaling_factor
for i in range(output.shape[0]):
output[i, [1,3]] = torch.clamp(output[i, [1,3]], 0.0, im_dim[i,0])
output[i, [2,4]] = torch.clamp(output[i, [2,4]], 0.0, im_dim[i,1])
classes = load_classes('data/coco.names')
colors = pkl.load(open("pallete", "rb"))
list(map(lambda x: write(x, orig_im, len(output)), output))
cv2.imshow("frame", orig_im)
key = cv2.waitKey(1)
if key & 0xFF == ord('q'):
break
frames += 1
print("FPS:{:5.2f}".format( frames / (time.time() - start)))
else:
break
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