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ip-adapter-face-full-test / crop_head_dlib5.py

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Testing ip-adapter-face-full-v15

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	import cv2
	import dlib
	import numpy as np
	from PIL import Image, ImageOps

	#https://gist.github.com/Norod/757e63802b0b28fbdab9d98b2e646ac2

	MODEL_PATH = "shape_predictor_5_face_landmarks.dat" # You need to download this file from http://dlib.net/files/shape_predictor_5_face_landmarks.dat.bz2
	detector = dlib.get_frontal_face_detector() # Initialize dlib's face detector model

	def get_face_landmarks(image_path):
	# Load the image
	image = cv2.imread(image_path)
	try:
	image = ImageOps.exif_transpose(image)
	except:
	print("exif problem, not rotating")

	gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

	# Initialize dlib's facial landmarks predictor
	predictor = dlib.shape_predictor("shape_predictor_5_face_landmarks.dat")

	# Detect faces in the image
	faces = detector(gray)

	if len(faces) > 0:
	# Assume the first face is the target, you can modify this based on your requirements
	shape = predictor(gray, faces[0])
	landmarks = np.array([[p.x, p.y] for p in shape.parts()])
	return landmarks
	else:
	return None

	def calculate_roll_and_yaw(landmarks):
	# Calculate the roll angle using the angle between the eyes
	roll_angle = np.degrees(np.arctan2(landmarks[1, 1] - landmarks[0, 1], landmarks[1, 0] - landmarks[0, 0]))

	# Calculate the yaw angle using the angle between the eyes and the tip of the nose
	yaw_angle = np.degrees(np.arctan2(landmarks[1, 1] - landmarks[2, 1], landmarks[1, 0] - landmarks[2, 0]))

	return roll_angle, yaw_angle

	def detect_and_crop_head(input_image, factor=3.0):
	# Get facial landmarks
	landmarks = get_face_landmarks(input_image)

	if landmarks is not None:
	# Calculate the center of the face using the mean of the landmarks
	center_x = int(np.mean(landmarks[:, 0]))
	center_y = int(np.mean(landmarks[:, 1]))

	# Calculate the size of the cropped region
	size = int(max(np.max(landmarks[:, 0]) - np.min(landmarks[:, 0]),
	np.max(landmarks[:, 1]) - np.min(landmarks[:, 1])) * factor)

	# Calculate the new coordinates for a 1:1 aspect ratio
	x_new = max(0, center_x - size // 2)
	y_new = max(0, center_y - size // 2)

	# Calculate roll and yaw angles
	roll_angle, yaw_angle = calculate_roll_and_yaw(landmarks)

	# Adjust the center coordinates based on the yaw and roll angles
	shift_x = int(size * 0.4 * np.sin(np.radians(yaw_angle)))
	shift_y = int(size * 0.4 * np.sin(np.radians(roll_angle)))

	#print(f'Roll angle: {roll_angle:.2f}, Yaw angle: {yaw_angle:.2f} shift_x: {shift_x}, shift_y: {shift_y}')

	center_x += shift_x
	center_y += shift_y

	# Calculate the new coordinates for a 1:1 aspect ratio
	x_new = max(0, center_x - size // 2)
	y_new = max(0, center_y - size // 2)

	# Read the input image using PIL
	image = Image.open(input_image)

	# Crop the head region with a 1:1 aspect ratio
	cropped_head = np.array(image.crop((x_new, y_new, x_new + size, y_new + size)))

	# Convert the cropped head back to PIL format
	cropped_head_pil = Image.fromarray(cropped_head)

	# Return the cropped head image
	return cropped_head_pil
	else:
	return None

	if __name__ == '__main__':
	input_image_path = 'input.jpg'
	output_image_path = 'output.jpg'

	# Detect and crop the head
	cropped_head = detect_and_crop_head(input_image_path, factor=3.0)

	# Save the cropped head image
	cropped_head.save(output_image_path)