Add object blurring & object cropping modules in Ultralytics docs (#7434)
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>pull/7432/head
Muhammad Rizwan Munawar
11 months ago
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GitHub
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4 changed files with 188 additions and 0 deletions
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--- |
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comments: true |
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description: Learn to blur objects using Ultralytics YOLOv8 for privacy in images and videos. |
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keywords: Ultralytics, YOLOv8, Object Detection, Object Blurring, Privacy Protection, Image Processing, Video Analysis, AI, Machine Learning |
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--- |
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|
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# Object Blurring using Ultralytics YOLOv8 🚀 |
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## What is Object Blurring? |
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Object blurring with [Ultralytics YOLOv8](https://github.com/ultralytics/ultralytics/) involves applying a blurring effect to specific detected objects in an image or video. This can be achieved using the YOLOv8 model capabilities to identify and manipulate objects within a given scene. |
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## Advantages of Object Blurring? |
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- **Privacy Protection**: Object blurring is an effective tool for safeguarding privacy by concealing sensitive or personally identifiable information in images or videos. |
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- **Selective Focus**: YOLOv8 allows for selective blurring, enabling users to target specific objects, ensuring a balance between privacy and retaining relevant visual information. |
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- **Real-time Processing**: YOLOv8's efficiency enables object blurring in real-time, making it suitable for applications requiring on-the-fly privacy enhancements in dynamic environments. |
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!!! Example "Object Blurring using YOLOv8 Example" |
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=== "Object Blurring" |
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```python |
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from ultralytics import YOLO |
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from ultralytics.utils.plotting import Annotator, colors |
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import cv2 |
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model = YOLO("yolov8n.pt") |
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names = model.names |
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cap = cv2.VideoCapture("path/to/video/file.mp4") |
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assert cap.isOpened(), "Error reading video file" |
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w, h, fps = (int(cap.get(x)) for x in (cv2.CAP_PROP_FRAME_WIDTH, cv2.CAP_PROP_FRAME_HEIGHT, cv2.CAP_PROP_FPS)) |
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# Blur ratio |
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blur_ratio = 50 |
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# Video writer |
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video_writer = cv2.VideoWriter("object_blurring_output.avi", |
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cv2.VideoWriter_fourcc(*'mp4v'), |
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fps, (w, h)) |
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while cap.isOpened(): |
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success, im0 = cap.read() |
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if not success: |
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print("Video frame is empty or video processing has been successfully completed.") |
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break |
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results = model.predict(im0, show=False) |
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boxes = results[0].boxes.xyxy.cpu().tolist() |
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clss = results[0].boxes.cls.cpu().tolist() |
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annotator = Annotator(im0, line_width=2, example=names) |
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if boxes is not None: |
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for box, cls in zip(boxes, clss): |
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annotator.box_label(box, color=colors(int(cls), True), label=names[int(cls)]) |
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obj = im0[int(box[1]):int(box[3]), int(box[0]):int(box[2])] |
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blur_obj = cv2.blur(obj, (blur_ratio, blur_ratio)) |
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im0[int(box[1]):int(box[3]), int(box[0]):int(box[2])] = blur_obj |
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cv2.imshow("ultralytics", im0) |
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video_writer.write(im0) |
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if cv2.waitKey(1) & 0xFF == ord('q'): |
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break |
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cap.release() |
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video_writer.release() |
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cv2.destroyAllWindows() |
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``` |
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### Arguments `model.predict` |
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| Name | Type | Default | Description | |
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|-----------------|----------------|------------------------|----------------------------------------------------------------------------| |
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| `source` | `str` | `'ultralytics/assets'` | source directory for images or videos | |
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| `conf` | `float` | `0.25` | object confidence threshold for detection | |
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| `iou` | `float` | `0.7` | intersection over union (IoU) threshold for NMS | |
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| `imgsz` | `int or tuple` | `640` | image size as scalar or (h, w) list, i.e. (640, 480) | |
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| `half` | `bool` | `False` | use half precision (FP16) | |
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| `device` | `None or str` | `None` | device to run on, i.e. cuda device=0/1/2/3 or device=cpu | |
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| `max_det` | `int` | `300` | maximum number of detections per image | |
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| `vid_stride` | `bool` | `False` | video frame-rate stride | |
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| `stream_buffer` | `bool` | `False` | buffer all streaming frames (True) or return the most recent frame (False) | |
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| `visualize` | `bool` | `False` | visualize model features | |
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| `augment` | `bool` | `False` | apply image augmentation to prediction sources | |
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| `agnostic_nms` | `bool` | `False` | class-agnostic NMS | |
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| `classes` | `list[int]` | `None` | filter results by class, i.e. classes=0, or classes=[0,2,3] | |
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| `retina_masks` | `bool` | `False` | use high-resolution segmentation masks | |
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| `embed` | `list[int]` | `None` | return feature vectors/embeddings from given layers | |
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--- |
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comments: true |
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description: Learn how to isolate and extract specific objects from images and videos using YOLOv8 object cropping. |
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keywords: Ultralytics, YOLOv8, Object Detection, Object Cropping, Image Analysis, Video Processing, Data Extraction, Python |
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--- |
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# Object Cropping using Ultralytics YOLOv8 🚀 |
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## What is Object Cropping? |
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Object cropping with [Ultralytics YOLOv8](https://github.com/ultralytics/ultralytics/) involves isolating and extracting specific detected objects from an image or video. The YOLOv8 model capabilities are utilized to accurately identify and delineate objects, enabling precise cropping for further analysis or manipulation. |
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## Advantages of Object Cropping? |
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- **Focused Analysis**: YOLOv8 facilitates targeted object cropping, allowing for in-depth examination or processing of individual items within a scene. |
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- **Reduced Data Volume**: By extracting only relevant objects, object cropping helps in minimizing data size, making it efficient for storage, transmission, or subsequent computational tasks. |
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- **Enhanced Precision**: YOLOv8's object detection accuracy ensures that the cropped objects maintain their spatial relationships, preserving the integrity of the visual information for detailed analysis. |
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!!! Example "Object Cropping using YOLOv8 Example" |
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=== "Object Cropping" |
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```python |
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from ultralytics import YOLO |
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from ultralytics.utils.plotting import Annotator, colors |
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import cv2 |
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import os |
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model = YOLO("yolov8n.pt") |
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names = model.names |
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cap = cv2.VideoCapture("path/to/video/file.mp4") |
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assert cap.isOpened(), "Error reading video file" |
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w, h, fps = (int(cap.get(x)) for x in (cv2.CAP_PROP_FRAME_WIDTH, cv2.CAP_PROP_FRAME_HEIGHT, cv2.CAP_PROP_FPS)) |
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crop_dir_name = "ultralytics_crop" |
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if not os.path.exists(crop_dir_name): |
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os.mkdir(crop_dir_name) |
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# Video writer |
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video_writer = cv2.VideoWriter("object_cropping_output.avi", |
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cv2.VideoWriter_fourcc(*'mp4v'), |
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fps, (w, h)) |
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idx = 0 |
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while cap.isOpened(): |
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success, im0 = cap.read() |
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if not success: |
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print("Video frame is empty or video processing has been successfully completed.") |
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break |
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results = model.predict(im0, show=False) |
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boxes = results[0].boxes.xyxy.cpu().tolist() |
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clss = results[0].boxes.cls.cpu().tolist() |
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annotator = Annotator(im0, line_width=2, example=names) |
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if boxes is not None: |
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for box, cls in zip(boxes, clss): |
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idx += 1 |
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annotator.box_label(box, color=colors(int(cls), True), label=names[int(cls)]) |
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crop_obj = im0[int(box[1]):int(box[3]), int(box[0]):int(box[2])] |
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cv2.imwrite(os.path.join(crop_dir_name, str(idx)+".png"), crop_obj) |
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cv2.imshow("ultralytics", im0) |
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video_writer.write(im0) |
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if cv2.waitKey(1) & 0xFF == ord('q'): |
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break |
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cap.release() |
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video_writer.release() |
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cv2.destroyAllWindows() |
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``` |
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### Arguments `model.predict` |
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| Name | Type | Default | Description | |
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|-----------------|----------------|------------------------|----------------------------------------------------------------------------| |
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| `source` | `str` | `'ultralytics/assets'` | source directory for images or videos | |
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| `conf` | `float` | `0.25` | object confidence threshold for detection | |
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| `iou` | `float` | `0.7` | intersection over union (IoU) threshold for NMS | |
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| `imgsz` | `int or tuple` | `640` | image size as scalar or (h, w) list, i.e. (640, 480) | |
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| `half` | `bool` | `False` | use half precision (FP16) | |
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| `device` | `None or str` | `None` | device to run on, i.e. cuda device=0/1/2/3 or device=cpu | |
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| `max_det` | `int` | `300` | maximum number of detections per image | |
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| `vid_stride` | `bool` | `False` | video frame-rate stride | |
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| `stream_buffer` | `bool` | `False` | buffer all streaming frames (True) or return the most recent frame (False) | |
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| `visualize` | `bool` | `False` | visualize model features | |
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| `augment` | `bool` | `False` | apply image augmentation to prediction sources | |
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| `agnostic_nms` | `bool` | `False` | class-agnostic NMS | |
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| `classes` | `list[int]` | `None` | filter results by class, i.e. classes=0, or classes=[0,2,3] | |
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| `retina_masks` | `bool` | `False` | use high-resolution segmentation masks | |
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| `embed` | `list[int]` | `None` | return feature vectors/embeddings from given layers | |
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