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faces.py
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faces.py
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#!/usr/bin/env python
# Copyright 2015 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Draws squares around detected faces in the given image."""
import argparse
# [START vision_face_detection_tutorial_imports]
from google.cloud import vision
from PIL import Image, ImageDraw
# [END vision_face_detection_tutorial_imports]
# [START vision_face_detection_tutorial_send_request]
def detect_face(face_file, max_results=4):
"""Uses the Vision API to detect faces in the given file.
Args:
face_file: A file-like object containing an image with faces.
Returns:
An array of Face objects with information about the picture.
"""
# [START vision_face_detection_tutorial_client]
client = vision.ImageAnnotatorClient()
# [END vision_face_detection_tutorial_client]
content = face_file.read()
image = vision.Image(content=content)
return client.face_detection(image=image, max_results=max_results).face_annotations
# [END vision_face_detection_tutorial_send_request]
# [START vision_face_detection_tutorial_process_response]
def highlight_faces(image, faces, output_filename):
"""Draws a polygon around the faces, then saves to output_filename.
Args:
image: a file containing the image with the faces.
faces: a list of faces found in the file. This should be in the format
returned by the Vision API.
output_filename: the name of the image file to be created, where the
faces have polygons drawn around them.
"""
im = Image.open(image)
draw = ImageDraw.Draw(im)
# Sepecify the font-family and the font-size
for face in faces:
box = [(vertex.x, vertex.y) for vertex in face.bounding_poly.vertices]
draw.line(box + [box[0]], width=5, fill="#00ff00")
# Place the confidence value/score of the detected faces above the
# detection box in the output image
draw.text(
(
(face.bounding_poly.vertices)[0].x,
(face.bounding_poly.vertices)[0].y - 30,
),
str(format(face.detection_confidence, ".3f")) + "%",
fill="#FF0000",
)
im.save(output_filename)
# [END vision_face_detection_tutorial_process_response]
# [START vision_face_detection_tutorial_run_application]
def main(input_filename, output_filename, max_results):
with open(input_filename, "rb") as image:
faces = detect_face(image, max_results)
print("Found {} face{}".format(len(faces), "" if len(faces) == 1 else "s"))
print(f"Writing to file {output_filename}")
# Reset the file pointer, so we can read the file again
image.seek(0)
highlight_faces(image, faces, output_filename)
# [END vision_face_detection_tutorial_run_application]
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="Detects faces in the given image.")
parser.add_argument("input_image", help="the image you'd like to detect faces in.")
parser.add_argument(
"--out",
dest="output",
default="out.jpg",
help="the name of the output file.",
)
parser.add_argument(
"--max-results",
dest="max_results",
default=4,
type=int,
help="the max results of face detection.",
)
args = parser.parse_args()
main(args.input_image, args.output, args.max_results)