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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) |