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detection.py
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detection.py
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import os
import torch
from torch.autograd import Variable
import numpy as np
import cv2
import matplotlib
from data import VOC_CLASSES as labels
import time
import data.config as cfg
from ssd_mobilenetv2 import build_ssd
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
os.environ["CUDA_VISIBLE_DEVICES"] = "3"
if torch.cuda.is_available():
torch.set_default_tensor_type('torch.cuda.FloatTensor')
num_classes = 21
top_k=20
image_size = 300
def detection_image(path,weight):
# cv2.namedWindow("result", 0)
global image_size
net = build_ssd('test', 300, num_classes)
net.eval()
net.load_weights(weight)
image = cv2.imread(path, cv2.IMREAD_COLOR)
rgb_image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
# rgb_image = cv2.resize(rgb_image, (512, 512))
resize_image = cv2.resize(image, (300, 300)).astype(np.float32)
resize_image -= (104, 117, 123)
resize_image = resize_image.astype(np.float32)
resize_image = resize_image[:, :, ::-1].copy()
torch_image = torch.from_numpy(resize_image).permute(2, 0, 1)
input_image = Variable(torch_image.unsqueeze(0))
if torch.cuda.is_available():
input_image = input_image.cuda()
out = net(input_image)
colors = cfg.COLORS
detections = out.data
scale = torch.Tensor(rgb_image.shape[1::-1]).repeat(2)
rgb_image = cv2.cvtColor(rgb_image, cv2.COLOR_RGB2BGR)
idx_obj = -1
for i in range(detections.size(1)):
j = 0
while detections[0, i, j, 0] >= 0.45:
idx_obj += 1
score = detections[0, i, j, 0]
label_name = labels[i - 1]
display_txt = '%s %.2f' % (label_name, score)
pt = (detections[0, i, j, 1:] * scale).cpu().numpy()
j += 1
pt[0] = max(pt[0], 0)
pt[1] = max(pt[1], 0)
pt[2] = min(pt[2], rgb_image.shape[1])
pt[3] = min(pt[3], rgb_image.shape[0])
color = colors[idx_obj%(len(colors))]
textsize = cv2.getTextSize(display_txt, cv2.FONT_HERSHEY_COMPLEX, 1, 2)[0]
text_x = int(pt[0])
text_y = int(pt[1])
if (int(pt[1]) - textsize[1] < 0):
text_y = int(pt[1]) + textsize[1] + 2
cv2.rectangle(rgb_image, (int(pt[0]), int(pt[1])),
(int(pt[0]) + textsize[0] + 8, int(pt[1]) + textsize[1] + 10),
(color[0], color[1], color[2], 125), -1)
else:
text_y -= 6
cv2.rectangle(rgb_image, (int(pt[0]) - 2, int(pt[1]) - textsize[1] - 10),
(int(pt[0]) + textsize[0] + 8, int(pt[1])), (color[0], color[1], color[2], 125), -1)
cv2.rectangle(rgb_image, (int(pt[0]), int(pt[1])), (int(pt[2]), int(pt[3])), color, 4)
cv2.putText(rgb_image, display_txt, (text_x + 4, text_y), cv2.FONT_HERSHEY_COMPLEX, 1,
(255 - color[0], 255 - color[1], 255 - color[2]), 2)
cv2.putText(rgb_image, 'x', (int((pt[2] + pt[0]) // 2 - 5), int((pt[3] + pt[1]) // 2)),
cv2.FONT_HERSHEY_COMPLEX, 1, color)
# cv2.imshow("result", rgb_image)
# cv2.waitKey(0)
print(path.replace("test_images", "out_images"))
cv2.imwrite(path.replace("test_images","out_images"),rgb_image)
def detection_video(path,weight):
global image_size
flag = 0
net = build_ssd('test', 300, num_classes)
net.eval()
net.load_weights(weight)
cap = cv2.VideoCapture(path)
frameNumber = cap.get(7)
fps = cap.get(cv2.CAP_PROP_FPS)
size = (int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)),int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)))
fourcc = cv2.VideoWriter_fourcc('M', 'P', '4', '2')
outVideo = cv2.VideoWriter('output_videos/out_%s.avi'%(path.split("/")[-1].split(".")[0]), fourcc, fps, size)
cv2.namedWindow("result", 0)
# image_size = size[0]
while cap.isOpened():
ret,image = cap.read()
flag += 1
if ret == False:
print("video is over!")
break
if flag % 3 != 0:
continue
t0 = time.time()
rgb_image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
#rgb_image = cv2.resize(rgb_image, (512, 512))
resize_image = cv2.resize(image, (300, 300)).astype(np.float32)
resize_image -= (104, 117, 123)
resize_image = resize_image.astype(np.float32)
resize_image = resize_image[:, :, ::-1].copy()
torch_image = torch.from_numpy(resize_image).permute(2, 0, 1)
input_image = Variable(torch_image.unsqueeze(0))
if torch.cuda.is_available():
input_image = input_image.cuda()
out = net(input_image)
colors = cfg.COLORS
detections = out.data
scale = torch.Tensor(rgb_image.shape[1::-1]).repeat(2)
rgb_image = cv2.cvtColor(rgb_image, cv2.COLOR_RGB2BGR)
idx_obj = -1
for i in range(detections.size(1)):
j = 0
while detections[0,i,j,0] >= 0.45:
idx_obj += 1
score = detections[0,i,j,0]
label_name = labels[i-1]
display_txt = '%s %.2f'%(label_name, score)
pt = (detections[0,i,j,1:]*scale).cpu().numpy()
j += 1
# TODO revise solutions
pt[0] = max(pt[0],0)
pt[1] = max(pt[1],0)
pt[2] = min(pt[2],size[1])
pt[3] = min(pt[3],size[0])
color = colors[idx_obj%len(colors)]
textsize = cv2.getTextSize(display_txt, cv2.FONT_HERSHEY_COMPLEX, 1, 2)[0]
text_x = int(pt[0])
text_y = int(pt[1])
if (int(pt[1])-textsize[1]<0):
text_y = int(pt[1]) + textsize[1] + 2
cv2.rectangle(rgb_image,(int(pt[0]),int(pt[1])),(int(pt[0])+textsize[0]+8,int(pt[1])+textsize[1]+10),(color[0],color[1],color[2],125),-1)
else:
text_y -= 6
cv2.rectangle(rgb_image, (int(pt[0])-2, int(pt[1])-textsize[1]-10),(int(pt[0]) + textsize[0]+8, int(pt[1])), (color[0],color[1],color[2],125), -1)
cv2.rectangle(rgb_image,(int(pt[0]), int(pt[1])),(int(pt[2]), int(pt[3])),color,4)
cv2.putText(rgb_image, display_txt, (text_x + 4, text_y), cv2.FONT_HERSHEY_COMPLEX, 1,(255 - color[0], 255 - color[1], 255 - color[2]), 2)
t1 = time.time()
cv2.putText(rgb_image, "FPS: %.2f" % (1 / (t1 - t0)), (5, 30), cv2.FONT_HERSHEY_COMPLEX, 1.2, (255, 255, 255), 2)
# cv2.imshow("result",rgb_image)
outVideo.write(rgb_image)
if cv2.waitKey(1) & 0xFF == ord('q'):
outVideo.release()
cap.release()
cv2.destroyAllWindows()
def detection(path,weight):
if path[-3:] == "jpg":
if os.path.isfile(path):
detection_image(path,weight)
else:
print("not finding %s"%(path))
elif path[-3:] == "avi":
if os.path.isfile(path):
detection_video(path,weight)
else:
print("not finding %s"%(path))
else:
print("format is %s\n"%path[-3:])
print("%s is not a image or video!"%path)
import os
if __name__ == "__main__":
weight = 'weights/ssd_mobilenetv2_300/mobilenetv2_final.pth'
path = r"test_images/2007_000256.jpg"
detection(path,weight)