使用YOLO11分割和高斯模糊创建人像效果

pip install ultralytics

import urllib.requestimport cv2
# Download the imageurl, filename = ("https://images.unsplash.com/photo-1634646493821-9fca74f85f59?w=600&auto=format&fit=crop&q=60&ixlib=rb-4.0.3&ixid=M3wxMjA3fDB8MHxzZWFyY2h8Mzk0fHx1bmJsdXJyZWQlMjBwb3J0YWl0fGVufDB8fDB8fHww", "scene.jpg")urllib.request.urlretrieve(url, filename)
# Load the input image using OpenCVimage = cv2.imread(filename)

import urllib.requestimport cv2from ultralytics import YOLOimport numpy as np
def segment_image(image, model):    # Predict with the model    results = model(filename)  # predict on an image 
    # Create an empty mask for segmentation    segmentation_mask = np.zeros_like(image, dtype=np.uint8)        # Iterate over the results    for i, r in enumerate(results):        # Iterate through the detected masks        for j, mask in enumerate(r.masks.xy):            # Convert the class tensor to an integer            class_id = int(r.boxes.cls[j].item())  # Extract the class ID as an integer                        # Check if the detected class corresponds to 'person' (class ID 0)            if class_id == 0:                # Convert mask coordinates to an integer format for drawing                mask = np.array(mask, dtype=np.int32)                                # Fill the segmentation mask with color (e.g., white for people)                cv2.fillPoly(segmentation_mask, [mask], (255, 255, 255))        return segmentation_mask
# Download the imageurl, filename = ("https://images.unsplash.com/photo-1634646493821-9fca74f85f59?w=600&auto=format&fit=crop&q=60&ixlib=rb-4.0.3&ixid=M3wxMjA3fDB8MHxzZWFyY2h8Mzk0fHx1bmJsdXJyZWQlMjBwb3J0YWl0fGVufDB8fDB8fHww", "scene.jpg")urllib.request.urlretrieve(url, filename)
# Load the input image using OpenCVimage = cv2.imread(filename)
# Load the modelmodel = YOLO("yolo11n-seg.pt")  # load an official YOLO model # Generate the segmentation mask   segmentation_mask = segment_image(image, model)
# Visualize the segmentation mask before combining it with the original imagecv2.imwrite("mask.jpg", segmentation_mask)

import urllib.requestimport cv2from ultralytics import YOLOimport numpy as np
def segment_image(image, model):    # Predict with the model    results = model(filename)  # predict on an image 
    # Create an empty mask for segmentation    segmentation_mask = np.zeros_like(image, dtype=np.uint8)        # Iterate over the results    for i, r in enumerate(results):        # Iterate through the detected masks        for j, mask in enumerate(r.masks.xy):            # Convert the class tensor to an integer            class_id = int(r.boxes.cls[j].item())  # Extract the class ID as an integer                        # Check if the detected class corresponds to 'person' (class ID 0)            if class_id == 0:                # Convert mask coordinates to an integer format for drawing                mask = np.array(mask, dtype=np.int32)                                # Fill the segmentation mask with color (e.g., white for people)                cv2.fillPoly(segmentation_mask, [mask], (255, 255, 255))        return segmentation_mask
def apply_blur_using_mask(image, mask, blur_strength=(25, 25)):    # Apply Gaussian blur to the entire image    blurred_image = cv2.GaussianBlur(image, blur_strength, 0)
    # Create an inverted mask where the background is white and the person is black    inverted_mask = cv2.bitwise_not(mask)
    # Use the mask to keep the person sharp and blur the background    background_blur = cv2.bitwise_and(blurred_image, blurred_image, mask=inverted_mask[:, :, 0])    person_region = cv2.bitwise_and(image, image, mask=mask[:, :, 0])
    # Combine the sharp person region with the blurred background    final_image = cv2.add(person_region, background_blur)        return final_image
# Download the imageurl, filename = ("https://images.unsplash.com/photo-1634646493821-9fca74f85f59?w=600&auto=format&fit=crop&q=60&ixlib=rb-4.0.3&ixid=M3wxMjA3fDB8MHxzZWFyY2h8Mzk0fHx1bmJsdXJyZWQlMjBwb3J0YWl0fGVufDB8fDB8fHww", "scene.jpg")urllib.request.urlretrieve(url, filename)
# Load the input image using OpenCVimage = cv2.imread(filename)
# Load the modelmodel = YOLO("yolo11n-seg.pt")  # load an official YOLO model # Generate the segmentation mask   segmentation_mask = segment_image(image, model)
# Call the function to apply the blur and save the resultfinal_image = apply_blur_using_mask(image, segmentation_mask)
# Visualize the segmentation mask before combining it with the original imagecv2.imwrite("mask.jpg", segmentation_mask)
# Save the resultcv2.imwrite("blurred_image.jpg", final_image)
# Optionally display the image (make sure you're running in a GUI environment)cv2.imshow("Blurred Image Result", final_image)cv2.waitKey(0)cv2.destroyAllWindows()