| Combining **OpenCV** with **TMSU** creates a powerful workflow for analyzing images, generating tags automatically, and then managing those tags with TMSU. Below is a step-by-step guide:
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| ### **Step 1: Install OpenCV and TMSU**
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| 1. **Install OpenCV**:
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| ```bash
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| pip3 install opencv-python opencv-python-headless
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| ```
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| 2. **Install TMSU**:
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| ```bash
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| sudo apt install tmsu
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| ```
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| ---
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| ### **Step 2: Image Analysis with OpenCV**
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| We’ll use OpenCV to analyze images, detect objects (e.g., using YOLO or MobileNet-SSD), and generate tags.
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| Save the following Python script as `opencv_tmsu.py`:
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| ```python
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| import cv2
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| import numpy as np
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| import os
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| import subprocess
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| # Load YOLO model
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| config_path = "yolov4.cfg" # Path to YOLO config file
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| weights_path = "yolov4.weights" # Path to YOLO weights
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| labels_path = "coco.names" # Path to class labels
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| # Load class labels
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| with open(labels_path, "r") as f:
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| labels = [line.strip() for line in f.readlines()]
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| # Load the network
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| net = cv2.dnn.readNetFromDarknet(config_path, weights_path)
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| net.setPreferableBackend(cv2.dnn.DNN_BACKEND_OPENCV)
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| net.setPreferableTarget(cv2.dnn.DNN_TARGET_CPU)
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| # Function to analyze image and generate tags
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| def analyze_image(image_path):
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| image = cv2.imread(image_path)
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| (h, w) = image.shape[:2]
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| # Prepare the input blob
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| blob = cv2.dnn.blobFromImage(image, 1 / 255.0, (416, 416), swapRB=True, crop=False)
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| net.setInput(blob)
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| # Perform inference
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| layer_names = net.getLayerNames()
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| output_layer_indices = net.getUnconnectedOutLayers()
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| output_layers = [layer_names[i - 1] for i in output_layer_indices.flatten()]
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| detections = net.forward(output_layers)
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| # Extract tags
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| tags = []
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| confidence_threshold = 0.5
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| for output in detections:
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| for detection in output:
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| scores = detection[5:]
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| class_id = np.argmax(scores)
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| confidence = scores[class_id]
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| if confidence > confidence_threshold:
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| tag = labels[class_id]
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| tags.append(tag)
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| return set(tags) # Return unique tags
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| # Function to assign tags to files using TMSU
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| def tag_with_tmsu(file_path, tags):
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| for tag in tags:
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| cmd = ["tmsu", "tag", file_path, tag]
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| subprocess.run(cmd)
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| # Main processing
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| if __name__ == "__main__":
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| image_dir = "images" # Directory containing images
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| for image_file in os.listdir(image_dir):
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| if image_file.endswith((".jpg", ".png", ".jpeg")):
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| image_path = os.path.join(image_dir, image_file)
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| print(f"Analyzing {image_file}...")
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| tags = analyze_image(image_path)
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| print(f"Tags for {image_file}: {tags}")
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| # Assign tags to file using TMSU
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| tag_with_tmsu(image_path, tags)
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| print(f"Tags added to {image_file} in TMSU.\n")
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| ```
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| ---
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| ### **Step 3: Prepare the Environment**
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| 1. **Ensure YOLO Files are Available**:
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| Download the YOLO model files (as in the previous steps):
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| ```bash
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| wget https://github.com/pjreddie/darknet/blob/master/cfg/yolov4.cfg
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| wget https://pjreddie.com/media/files/yolov4.weights
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| wget https://github.com/pjreddie/darknet/blob/master/data/coco.names
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| ```
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| 2. **Organize Images**:
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| Place your images in a directory named `images/`.
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| 3. **Initialize TMSU**:
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| Initialize TMSU in the directory where the `images` folder is located:
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| ```bash
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| tmsu init
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| ```
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| ---
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| ### **Step 4: Run the Script**
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| Run the Python script to analyze the images and tag them using TMSU:
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| ```bash
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| python3 opencv_tmsu.py
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| ```
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| ---
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| ### **Step 5: Verify Tags**
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| To check the tags assigned to a file:
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| ```bash
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| tmsu tags images/your_image.jpg
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| ```
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| To search for files by tags:
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| ```bash
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| tmsu files person
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| ```
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| ---
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| ### **Optional: Virtual Directory for Tags**
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| Mount a virtual directory to browse files by tags:
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| ```bash
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| tmsu mount ~/tmsu-virtual
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| ```
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| Navigate to `~/tmsu-virtual` to see files organized by tags.
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| Unmount when done:
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| ```bash
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| fusermount -u ~/tmsu-virtual
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| ```
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| ---
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| ### **Summary**
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| This setup uses OpenCV to analyze images and extract object tags, which are then assigned to files using TMSU. The tags can be used for efficient file organization and retrieval through TMSU's tagging and query system. Let me know if you need help customizing this workflow!
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