ContraCLIP/lib/evaluation/sfd/detect.py

import torch
import torch.nn.functional as F

import os
import sys
import cv2
import random
import datetime
import math
import argparse
import numpy as np

import scipy.io as sio
import zipfile
from .net_s3fd import s3fd
from .bbox import *
import matplotlib.pyplot as plt


def detect(net, img, device):
	img = img - np.array([104, 117, 123])
	img = img.transpose(2, 0, 1)
	img = img.reshape((1,) + img.shape)

	# if torch.cuda.current_device() == 0:
	# 	torch.backends.cudnn.benchmark = True

	img = torch.from_numpy(img).float().to(device)

	return batch_detect(net, img, device)


def batch_detect(net, img_batch, device):
	"""
	Inputs:
		- img_batch: a torch.Tensor of shape (Batch size, Channels, Height, Width)
	"""
	# if torch.cuda.current_device() == 0:
	# 	torch.backends.cudnn.benchmark = True

	BB, CC, HH, WW = img_batch.size()
	with torch.no_grad():
		olist = net(img_batch.float())  # patched uint8_t overflow error

	for i in range(len(olist) // 2):
		olist[i * 2] = F.softmax(olist[i * 2], dim=1)

	bboxlists = []
	olist = [oelem.data.cpu() for oelem in olist]
	for j in range(BB):
		bboxlist = []
		for i in range(len(olist) // 2):
			ocls, oreg = olist[i * 2], olist[i * 2 + 1]
			FB, FC, FH, FW = ocls.size()  # feature map size
			stride = 2**(i + 2)    # 4,8,16,32,64,128
			anchor = stride * 4
			poss = zip(*np.where(ocls[:, 1, :, :] > 0.05))
			
			for Iindex, hindex, windex in poss:
				axc, ayc = stride / 2 + windex * stride, stride / 2 + hindex * stride
				score = ocls[j, 1, hindex, windex]
				loc = oreg[j, :, hindex, windex].contiguous().view(1, 4)
				priors = torch.Tensor([[axc / 1.0, ayc / 1.0, stride * 4 / 1.0, stride * 4 / 1.0]])
				variances = [0.1, 0.2]
				box = decode(loc, priors, variances)
				x1, y1, x2, y2 = box[0] * 1.0
				bboxlist.append([x1, y1, x2, y2, score])
		bboxlists.append(bboxlist)

	bboxlists = np.array(bboxlists)

	if 0 == len(bboxlists):
		bboxlists = np.zeros((1, 1, 5))

	return bboxlists


def flip_detect(net, img, device):
	img = cv2.flip(img, 1)
	b = detect(net, img, device)

	bboxlist = np.zeros(b.shape)
	bboxlist[:, 0] = img.shape[1] - b[:, 2]
	bboxlist[:, 1] = b[:, 1]
	bboxlist[:, 2] = img.shape[1] - b[:, 0]
	bboxlist[:, 3] = b[:, 3]
	bboxlist[:, 4] = b[:, 4]
	return bboxlist


def pts_to_bb(pts):
	min_x, min_y = np.min(pts, axis=0)
	max_x, max_y = np.max(pts, axis=0)
	return np.array([min_x, min_y, max_x, max_y])