49 lines
1.6 KiB
Python
49 lines
1.6 KiB
Python
import cv2
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import matplotlib.pyplot as plt
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import numpy as np
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from keras.preprocessing import image
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def load_image(image_path, grayscale=False, target_size=None):
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pil_image = image.load_img(image_path, grayscale, target_size)
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return image.img_to_array(pil_image)
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def load_detection_model(model_path):
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# detection_model = cv2.CascadeClassifier(model_path)
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detection_model = cv2.dnn.readNetFromCaffe("data/data_opencv/deploy.prototxt.txt", model_path)
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return detection_model
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def detect_faces(detection_model, gray_image_array):
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blob = cv2.dnn.blobFromImage(cv2.resize(gray_image_array, (300, 300)), 1.0,
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(300, 300), (104.0, 177.0, 123.0))
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detection_model.setInput(blob)
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return detection_model.forward()
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# return detection_model.detectMultiScale(gray_image_array, 1.3, 5)
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def draw_bounding_box(face_coordinates, image_array, color):
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x, y, w, h = face_coordinates
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cv2.rectangle(image_array, (x, y), (x + w, y + h), color, 2)
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def apply_offsets(face_coordinates, offsets):
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x, y, width, height = face_coordinates
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x_off, y_off = offsets
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return (x - x_off, x + width + x_off, y - y_off, y + height + y_off)
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def draw_text(coordinates, image_array, text, color, x_offset=0, y_offset=0,
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font_scale=2, thickness=2):
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x, y = coordinates[:2]
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cv2.putText(image_array, text, (x + x_offset, y + y_offset),
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cv2.FONT_HERSHEY_SIMPLEX,
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font_scale, color, thickness, cv2.LINE_AA)
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def get_colors(num_classes):
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colors = plt.cm.hsv(np.linspace(0, 1, num_classes)).tolist()
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colors = np.asarray(colors) * 255
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return colors
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