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import cv2
import numpy as np
def insert_person(left_image_path, right_image_path, person_image_path, depth="medium"):
# Load left and right stereoscopic images
left_image = cv2.imread(left_image_path)
right_image = cv2.imread(right_image_path)
# Load the segmented person image with alpha channel (transparency)
person = cv2.imread(person_image_path, cv2.IMREAD_UNCHANGED)
# Define scaling and disparity values for each depth level
depth_settings = {
"close": {
"scale": 1.2,
"disparity": 15,
}, # Larger size and greater disparity for closer placement
"medium": {
"scale": 1.0,
"disparity": 10,
}, # Moderate size and disparity for medium placement
"far": {
"scale": 0.7,
"disparity": 5,
}, # Smaller size and lesser disparity for farther placement
}
# Retrieve scale and disparity based on depth level
scale_factor = depth_settings[depth]["scale"]
disparity = depth_settings[depth]["disparity"]
# Resize the person image based on the scale factor
person_h, person_w = person.shape[:2]
new_size = (int(person_w * scale_factor), int(person_h * scale_factor))
person_resized = cv2.resize(person, new_size, interpolation=cv2.INTER_AREA)
# Determine the positions for placing the person in left and right images
# (You may adjust these values to match specific surfaces or alignments in your background)
left_x, left_y = (
50,
left_image.shape[0] - person_resized.shape[0] - 50,
) # Place near bottom for realism
right_x = left_x + disparity # Horizontal offset for depth effect
# Overlay the person onto both left and right images with adjusted position
for img, x in zip((left_image, right_image), (left_x, right_x)):
# Ensure the person fits within the bounds of the image
y, x = max(0, left_y), max(0, x)
y_end = min(y + person_resized.shape[0], img.shape[0])
x_end = min(x + person_resized.shape[1], img.shape[1])
# Extract the region of interest (ROI)
roi = img[y:y_end, x:x_end]
# Apply alpha blending to combine person image with background
person_alpha = (
person_resized[: y_end - y, : x_end - x, 3] / 255.0
) # Alpha channel mask
for c in range(3):
roi[:, :, c] = (1 - person_alpha) * roi[
:, :, c
] + person_alpha * person_resized[: y_end - y, : x_end - x, c]
# Insert modified ROI back into the original image
img[y:y_end, x:x_end] = roi
# Return the modified left and right images
return left_image, right_image
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