141 lines
4.5 KiB
Python
141 lines
4.5 KiB
Python
import torch
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import cv2
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import numpy as np
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from backbones import iresnet50,iresnet18,iresnet100
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# print(torch.cuda.is_available())
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def load_image(img_path):
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img = cv2.imread(img_path)
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#print(img)
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#img2 = np.fliplr(img)
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#print(img2.shape)
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img = img.transpose((2, 0, 1))
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#img2 = img2.transpose((2, 0, 1))
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img = img[np.newaxis, :, :, :]
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#img2 = img2[np.newaxis, :, :, :]
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#img = np.concatenate((img,img2))
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img = np.array(img, dtype=np.float32)
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img -= 127.5
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img /= 127.5
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return img
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def findEuclideanDistance(source_representation, test_representation):
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euclidean_distance = source_representation - test_representation
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euclidean_distance = np.sum(np.multiply(euclidean_distance, euclidean_distance))
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euclidean_distance = np.sqrt(euclidean_distance)
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return euclidean_distance
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def findCosineDistance(source_representation, test_representation):
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a = np.matmul(np.transpose(source_representation), test_representation)
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b = np.sum(np.multiply(source_representation, source_representation))
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c = np.sum(np.multiply(test_representation, test_representation))
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return 1 - (a / (np.sqrt(b) * np.sqrt(c)))
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def l2_normalize(x):
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return x / np.sqrt(np.sum(np.multiply(x, x))-1)
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def cosin_metric(x1, x2):
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return np.dot(x1, x2) / (np.linalg.norm(x1) * np.linalg.norm(x2))
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def get_file_list(file_name):
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file1 = []
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file2 = []
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code = []
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with open(file_name,"r") as f:
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file_list = f.readlines()
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for i in file_list:
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data = i.replace("\n","").split(" ")
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file1.append(data[0])
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file2.append(data[1])
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code.append(data[2])
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return file1,file2,code
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def load_list_images(file_list):
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all = np.zeros((len(file_list),3,112,112),dtype=np.float32)
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count = 0
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for img_path in file_list:
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img = cv2.imread(img_path)
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img = img.transpose((2, 0, 1))
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img = img[np.newaxis, :, :, :]
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all[count] = img
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count = count + 1
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all -= 127.5
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all /= 127.5
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return all
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def threshold_acc(distance,code):
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thresholds = np.arange(0.5,2,0.1)
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total = len(code)
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max_acc = 0
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threshold = 0
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code_net = []
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for i in thresholds:
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for ds in distance:
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if ds < i:
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code_net.append(1)
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else:
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code_net.append(0)
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right = np.sum(code==code_net)
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#print(threshold, max_acc)
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if right/total > max_acc:
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max_acc = right / total
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threshold = i
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code_net = []
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return threshold,max_acc
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# if __name__=='__main__':
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# img1 = load_image("D:\Download\out\output\Aaron_Peirsol\Aaron_Peirsol_0001.jpg")
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# img1 = torch.from_numpy(img1)
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# print(img1.shape)
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# #print(img1)
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# img2 = load_image("D:\Download\out\output\Aaron_Peirsol\Aaron_Peirsol_0002.jpg")
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# img2 = torch.from_numpy(img2)
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# model = iresnet100()
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# model.load_state_dict(torch.load("./model/backbone100.pth",map_location="cpu"))
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# #print(model)
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# model.eval()
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# with torch.no_grad():
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# pred1 = model(img1)
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# print(pred1.shape)
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# #print(pred1)
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# pred2 = model(img2)
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# pred1 = pred1.numpy()
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# print(pred1.shape)
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# pred2 = pred2.numpy()
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# print("EuclideanDistance is :"+str(findEuclideanDistance(l2_normalize(pred1),l2_normalize(pred2))))
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# print(findCosineDistance(pred1[0],pred2[0]))
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if __name__=='__main__':
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model = iresnet100()
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model.load_state_dict(torch.load("./model/backbone100.pth", map_location="cpu"))
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model.eval()
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file1, file2, code = get_file_list("pairs.txt")
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code = np.array(code,dtype=np.int)
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img = load_list_images(file1)
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print(img.shape)
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img = torch.from_numpy(img)
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img2 = load_list_images(file2)
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img2 = torch.from_numpy(img2)
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batch_size = 64
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now = 0
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number = len(code)
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distance = []
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with torch.no_grad():
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while now < number:
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if now + batch_size < number:
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pred1 = model(img[now:now + batch_size])
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pred2 = model(img2[now:now + batch_size])
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else:
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pred1 = model(img[now:])
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pred2 = model(img2[now:])
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now = now + batch_size
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pred1 = pred1.numpy()
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pred2 = pred2.numpy()
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print(pred1.shape)
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for i in range(len(pred1)):
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distance.append(findEuclideanDistance(l2_normalize(pred1[i]), l2_normalize(pred2[i])))
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distance = np.array(distance)
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print(distance[:100])
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threshold,max_acc = threshold_acc(distance,code)
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print(threshold,max_acc)
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