90 lines
3.1 KiB
Python
90 lines
3.1 KiB
Python
import sys
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import cv2
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from keras.models import load_model
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import numpy as np
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from utils.datasets import get_labels
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from utils.inference import detect_faces
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from utils.inference import draw_text
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from utils.inference import draw_bounding_box
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from utils.inference import apply_offsets
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from utils.inference import load_detection_model
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from utils.inference import load_image
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from utils.preprocessor import preprocess_input
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from PIL import Image
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# parameters for loading data and images
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# image_path = sys.argv[1]
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image_path = r"E:\yuxin\test-images\girl-16.jpg"
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detection_model_path = '../trained_models/detection_models/haarcascade_frontalface_default.xml'
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emotion_model_path = '../trained_models/emotion_models/fer2013_mini_XCEPTION.102-0.66.hdf5'
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gender_model_path = '../trained_models/gender_models/simple_CNN.81-0.96.hdf5'
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emotion_labels = get_labels('fer2013')
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gender_labels = get_labels('imdb')
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font = cv2.FONT_HERSHEY_SIMPLEX
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# hyper-parameters for bounding boxes shape
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gender_offsets = (30, 60)
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gender_offsets = (10, 10)
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emotion_offsets = (20, 40)
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emotion_offsets = (0, 0)
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# loading models
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face_detection = load_detection_model(detection_model_path)
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emotion_classifier = load_model(emotion_model_path, compile=False)
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gender_classifier = load_model(gender_model_path, compile=False)
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# getting input model shapes for inference
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emotion_target_size = emotion_classifier.input_shape[1:3]
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gender_target_size = gender_classifier.input_shape[1:3]
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# loading images
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rgb_image = load_image(image_path, grayscale=False)
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gray_image = load_image(image_path, grayscale=True)
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gray_image = np.squeeze(gray_image)
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gray_image = gray_image.astype('uint8')
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faces = detect_faces(face_detection, gray_image)
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for face_coordinates in faces:
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x1, x2, y1, y2 = apply_offsets(face_coordinates, gender_offsets)
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rgb_face = rgb_image[y1:y2, x1:x2]
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x1, x2, y1, y2 = apply_offsets(face_coordinates, emotion_offsets)
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gray_face = gray_image[y1:y2, x1:x2]
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try:
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# rgb_face = cv2.resize(rgb_face, (gender_target_size))
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gray_face = cv2.resize(gray_face, (emotion_target_size))
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except:
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continue
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# rgb_face = preprocess_input(rgb_face, False)
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# rgb_face = np.expand_dims(rgb_face, 0)
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# gender_prediction = gender_classifier.predict(rgb_face)
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# gender_label_arg = np.argmax(gender_prediction)
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# gender_text = gender_labels[gender_label_arg]
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gray_face = preprocess_input(gray_face, True)
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gray_face = np.expand_dims(gray_face, 0)
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gray_face = np.expand_dims(gray_face, -1)
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emotion_label_arg = np.argmax(emotion_classifier.predict(gray_face))
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emotion_text = emotion_labels[emotion_label_arg]
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# if gender_text == gender_labels[0]:
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# color = (0, 0, 255)
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# else:
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color = (255, 0, 0)
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draw_bounding_box(face_coordinates, rgb_image, color)
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# draw_text(face_coordinates, rgb_image, gender_text, color, 0, -20, 1, 2)
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draw_text(face_coordinates, rgb_image, emotion_text, color, 0, -20, 1, 2)
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# print(face_coordinates)
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# print(emotion_text)
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bgr_image = cv2.cvtColor(rgb_image, cv2.COLOR_RGB2BGR)
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img_path = '../images/predicted_test_image.png'
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cv2.imwrite(img_path, bgr_image)
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img = Image.open(img_path)
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img.show()
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