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106 lines
3.1 KiB
Python
106 lines
3.1 KiB
Python
import matplotlib.pyplot as plt
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import numpy as np
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import cv2
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from deepface import DeepFace
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from deepface.modules import verification
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from deepface.models.FacialRecognition import FacialRecognition
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from deepface.commons.logger import Logger
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logger = Logger()
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# ----------------------------------------------
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# build face recognition model
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model_name = "VGG-Face"
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model: FacialRecognition = DeepFace.build_model(model_name=model_name)
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target_size = model.input_shape
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logger.info(f"target_size: {target_size}")
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# ----------------------------------------------
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# load images and find embeddings
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img1 = DeepFace.extract_faces(img_path="dataset/img1.jpg")[0]["face"]
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img1 = cv2.resize(img1, target_size)
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img1 = np.expand_dims(img1, axis=0) # to (1, 224, 224, 3)
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img1_representation = model.forward(img1)
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img2 = DeepFace.extract_faces(img_path="dataset/img3.jpg")[0]["face"]
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img2 = cv2.resize(img2, target_size)
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img2 = np.expand_dims(img2, axis=0)
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img2_representation = model.forward(img2)
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img1_representation = np.array(img1_representation)
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img2_representation = np.array(img2_representation)
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# ----------------------------------------------
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# distance between two images - euclidean distance formula
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distance_vector = np.square(img1_representation - img2_representation)
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current_distance = np.sqrt(distance_vector.sum())
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logger.info(f"Euclidean distance: {current_distance}")
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threshold = verification.find_threshold(model_name=model_name, distance_metric="euclidean")
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logger.info(f"Threshold for {model_name}-euclidean pair is {threshold}")
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if current_distance < threshold:
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logger.info(
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f"This pair is same person because its distance {current_distance}"
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f" is less than threshold {threshold}"
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)
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else:
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logger.info(
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f"This pair is different persons because its distance {current_distance}"
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f" is greater than threshold {threshold}"
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)
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# ----------------------------------------------
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# expand vectors to be shown better in graph
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img1_graph = []
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img2_graph = []
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distance_graph = []
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for i in range(0, 200):
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img1_graph.append(img1_representation)
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img2_graph.append(img2_representation)
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distance_graph.append(distance_vector)
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img1_graph = np.array(img1_graph)
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img2_graph = np.array(img2_graph)
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distance_graph = np.array(distance_graph)
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# ----------------------------------------------
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# plotting
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fig = plt.figure()
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ax1 = fig.add_subplot(3, 2, 1)
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plt.imshow(img1[0])
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plt.axis("off")
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ax2 = fig.add_subplot(3, 2, 2)
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im = plt.imshow(img1_graph, interpolation="nearest", cmap=plt.cm.ocean)
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plt.colorbar()
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ax3 = fig.add_subplot(3, 2, 3)
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plt.imshow(img2[0])
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plt.axis("off")
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ax4 = fig.add_subplot(3, 2, 4)
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im = plt.imshow(img2_graph, interpolation="nearest", cmap=plt.cm.ocean)
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plt.colorbar()
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ax5 = fig.add_subplot(3, 2, 5)
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plt.text(0.35, 0, f"Distance: {current_distance}")
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plt.axis("off")
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ax6 = fig.add_subplot(3, 2, 6)
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im = plt.imshow(distance_graph, interpolation="nearest", cmap=plt.cm.ocean)
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plt.colorbar()
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plt.show()
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# ----------------------------------------------
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