基于Python和命令行人脸识别项目（系列四）

基于Python和命令行人脸识别项目（系列四）

接着系列三，继续开始我们face_recognition项目.

下面介绍本项目其他一些功能。

案例一：让系统确定人脸（是鞠婧祎还是特朗普）

在knowe_people文件夹中创建recognize_faces_in_pictures.py文件并写入以下代码：import face_recognition# Load the jpg files into numpy arraysjujingyi_image = face_recognition.load_image_file("鞠婧祎.jpg")telangpu_image = face_recognition.load_image_file("特朗普.jpeg")unknown_image = face_recognition.load_image_file("unknown.jpeg")# Get the face encodings for each face in each image file# Since there could be more than one face in each image, it returns a list of encodings.# But since I know each image only has one face, I only care about the first encoding in each #image, so I grab index 0.try: jujingyi_face_encoding = face_recognition.face_encodings(jujingyi_image)[0] telangpu_face_encoding = face_recognition.face_encodings(telangpu_image)[0] unknown_face_encoding = face_recognition.face_encodings(unknown_image)[0]except IndexError: print("I wasn't able to locate any faces in at least one of the images. Check the image files. Aborting...") quit()known_faces = [ jujingyi_face_encoding, telangpu_face_encoding]# results is an array of True/False telling if the unknown face matched anyone in the known_faces arrayresults = face_recognition.compare_faces(known_faces, unknown_face_encoding)print("Is the unknown face a picture of jujingyi? {}".format(results[0]))print("Is the unknown face a picture of telangpu? {}".format(results[1]))print("Is the unknown face a new person that we've never seen before? {}".format(not True in results))然后我们把一张在knowe_people文件夹中放入一张以前没有的特朗普照片命名为unknown.jpeg

然后终端切换到knowe_people目录下，输入以下命令，弹出窗口如下：

可见终端可以确定未知图片是特朗普，终端输出如下：Is the unknown face a picture of jujingyi? False                          Is the unknown face a picture of telangpu? True                          Is the unknown face a new person that we've never seen before? False

案例二：人脸识别之后在原图画上框并标注姓名

在knowe_people文件夹中创建identify_and_draw_boxes_on_faces.py文件并写入以下代码：import face_recognitionfrom PIL import Image, ImageDrawimport numpy as np# This is an example of running face recognition on a single image# and drawing a box around each person that was identified.# Load a sample picture and learn how to recognize it.jujingyi_image = face_recognition.load_image_file("鞠婧祎1.jpeg")jujingyi_face_encoding = face_recognition.face_encodings(jujingyi_image)[0]# Load a second sample picture and learn how to recognize it.obama_image = face_recognition.load_image_file("obama.jpg")obama_face_encoding = face_recognition.face_encodings(obama_image)[0]# Create arrays of known face encodings and their namesknown_face_encodings = [ jujingyi_face_encoding, obama_face_encoding]known_face_names = [ "鞠婧祎", "obama"]# Load an image with an unknown faceunknown_image = face_recognition.load_image_file("two_people.jpg")# Find all the faces and face encodings in the unknown imageface_locations = face_recognition.face_locations(unknown_image)face_encodings = face_recognition.face_encodings(unknown_image, face_locations)# Convert the image to a PIL-format image so that we can draw on top of it with the Pillow library# See for more about PIL/Pillowpil_image = Image.fromarray(unknown_image)# Create a Pillow ImageDraw Draw instance to draw withdraw = ImageDraw.Draw(pil_image)# Loop through each face found in the unknown imagefor (top, right, bottom, left), face_encoding in zip(face_locations, face_encodings): # See if the face is a match for the known face(s) matches = face_recognition.compare_faces(known_face_encodings, face_encoding) name = "Unknown" # If a match was found in known_face_encodings, just use the first one. # if True in matches: # first_match_index = matches.index(True) # name = known_face_names[first_match_index] # Or instead, use the known face with the smallest distance to the new face face_distances = face_recognition.face_distance(known_face_encodings, face_encoding) best_match_index = np.argmin(face_distances) if matches[best_match_index]: name = known_face_names[best_match_index] # Draw a box around the face using the Pillow module draw.rectangle(((left, top), (right, bottom)), outline=(0, 0, 255)) # Draw a label with a name below the face text_width, text_height = draw.textsize(name) draw.rectangle(((left, bottom - text_height - 10), (right, bottom)), fill=(0, 0, 255), outline=(0, 0, 255)) draw.text((left + 6, bottom - text_height - 5), name, fill=(255, 255, 255, 255))# Remove the drawing library from memory as per the Pillow docsdel draw# Display the resulting imagepil_image.show()# You can also save a copy of the new image to disk if you want by uncommenting this line# pil_image.save("image_with_boxes.jpg")

再在knowe_people放入一张未知图片，如图，命名为——two_people.jpg。

然后终端切换到knowe_people目录下，输入以下命令，弹出窗口如下：

由于右边是未知人物，在数据集中没有对应的信息（数据集中只有obama和鞠婧祎信息），输出unknown，修改代码：

import face_recognitionfrom PIL import Image, ImageDrawimport numpy as np# This is an example of running face recognition on a single image# and drawing a box around each person that was identified.# Load a sample picture and learn how to recognize it.obama_image = face_recognition.load_image_file("obama.jpg")obama_face_encoding = face_recognition.face_encodings(obama_image)[0]# Load a second sample picture and learn how to recognize it.biden_image = face_recognition.load_image_file("biden.jpg")biden_face_encoding = face_recognition.face_encodings(biden_image)[0]# Create arrays of known face encodings and their namesknown_face_encodings = [    obama_face_encoding,    biden_face_encoding]known_face_names = [    "Barack Obama",    "Joe Biden"]# Load an image with an unknown faceunknown_image = face_recognition.load_image_file("two_people.jpg")# Find all the faces and face encodings in the unknown imageface_locations = face_recognition.face_locations(unknown_image)face_encodings = face_recognition.face_encodings(unknown_image, face_locations)# Convert the image to a PIL-format image so that we can draw on top of it with the Pillow library# See for more about PIL/Pillowpil_image = Image.fromarray(unknown_image)# Create a Pillow ImageDraw Draw instance to draw withdraw = ImageDraw.Draw(pil_image)# Loop through each face found in the unknown imagefor (top, right, bottom, left), face_encoding in zip(face_locations, face_encodings):    # See if the face is a match for the known face(s)    matches = face_recognition.compare_faces(known_face_encodings, face_encoding)    name = "Unknown"    # If a match was found in known_face_encodings, just use the first one.    # if True in matches:    #     first_match_index = matches.index(True)    #     name = known_face_names[first_match_index]    # Or instead, use the known face with the smallest distance to the new face    face_distances = face_recognition.face_distance(known_face_encodings, face_encoding)    best_match_index = np.argmin(face_distances)    if matches[best_match_index]:        name = known_face_names[best_match_index]    # Draw a box around the face using the Pillow module    draw.rectangle(((left, top), (right, bottom)), outline=(0, 0, 255))    # Draw a label with a name below the face    text_width, text_height = draw.textsize(name)    draw.rectangle(((left, bottom - text_height - 10), (right, bottom)), fill=(0, 0, 255), outline=(0, 0, 255))    draw.text((left + 6, bottom - text_height - 5), name, fill=(255, 255, 255, 255))# Remove the drawing library from memory as per the Pillow docsdel draw# Display the resulting imagepil_image.show()# You can also save a copy of the new image to disk if you want by uncommenting this line# pil_image.save("image_with_boxes.jpg")

然后终端切换到knowe_people目录下，输入以下命令，弹出窗口如下：

由于数据集中有未知人物拜登的信息，故能输出人名。

案例三：在不同精度上比较两张人脸是否是同一个人

通常情况下，我们不一定检查两张脸是否匹配(对或错)，而且看看它们有多相似。您可以使用face_distance函数来实现这一点。该模型的训练方式是，距离为0.6或更小的面孔应该是匹配的。但如果你想更严格一点，你可以找一个较小的距离。例如，使用0.55将更加减少false，正面匹配有更多假阴性的风险。注意:这与“百分比匹配”并不完全相同。比例不是线性的。但是你可以假设距离较小的图像比距离较大的图像更相似。首先在known_people文件夹中加入带匹配的照片obama2.jpg,如图。

在knowe_people文件夹中创建face_distance.py文件并写入以下代码：

import face_recognition# Often instead of just checking if two faces match or not (True or False), it's helpful to see how similar they are.# You can do that by using the face_distance function.# The model was trained in a way that faces with a distance of 0.6 or less should be a match. But if you want to# be more strict, you can look for a smaller face distance. For example, using a 0.55 cutoff would reduce false# positive matches at the risk of more false negatives.# Note: This isn't exactly the same as a "percent match". The scale isn't linear. But you can assume that images with a# smaller distance are more similar to each other than ones with a larger distance.# Load some images to compare againstknown_obama_image = face_recognition.load_image_file("obama.jpg")known_biden_image = face_recognition.load_image_file("biden.jpg")# Get the face encodings for the known imagesobama_face_encoding = face_recognition.face_encodings(known_obama_image)[0]biden_face_encoding = face_recognition.face_encodings(known_biden_image)[0]known_encodings = [    obama_face_encoding,    biden_face_encoding]# Load a test image and get encondings for itimage_to_test = face_recognition.load_image_file("obama2.jpg")image_to_test_encoding = face_recognition.face_encodings(image_to_test)[0]# See how far apart the test image is from the known facesface_distances = face_recognition.face_distance(known_encodings, image_to_test_encoding)for i, face_distance in enumerate(face_distances):    print("The test image has a distance of {:.2} from known image #{}".format(face_distance, i))    print("- With a normal cutoff of 0.6, would the test image match the known image? {}".format(face_distance < 0.6))    print("- With a very strict cutoff of 0.5, would the test image match the known image? {}".format(face_distance < 0.5))    print()

然后终端切换到knowe_people目录下，输入以下命令，弹出窗口如下：

终端输出说明：上述测试图像（obama）与已知图像#0(obama)之间的距离为0.35，也就是说匹配可能性比较高（正常的截断为0.6），测试图像是否与已知图像匹配。在非常严格的0.5截断下，测试图像会与已知图像匹配吗?会测试图像到已知图像#1(biden)的距离为0.82-正常截断距离为0.6，测试图像是否与已知图像不匹配，在非常严格的0.5截断下，测试图像会与已知图像匹配吗?不会

小结：这次主要展示了本项目三个功能，更多内容见其他系列。

案例一：让系统确定人脸（是鞠婧祎还是特朗普）

案例二：人脸识别之后在原图画上框并标注姓名

案例三：在不同精度上比较两张人脸是否是同一个人

源码：​​https://github.com/ageitgey/face_recognition​​

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