关于YOLOv2、OpenCV3.4及yolo_object_detection.cpp的技术咨询：第136行含义与分类修改

Hey there! Let's break down your two questions about the OpenCV YOLO sample code:

1. What does line 136 do? #

Based on the current master branch version of yolo_object_detection.cpp in OpenCV's repo, line 136 is typically the line that pulls out the array of class confidence scores for a single detection candidate box. Here's what the code looks like (and what each part means):

float* scores = (float*)outputData + classIdOffset;

• outputData is the raw tensor output from the YOLO model. Each detection box in this tensor follows the format: [center_x, center_y, width, height, object_confidence, class1_conf, class2_conf, ..., classN_conf]
• classIdOffset is the starting index in the tensor where the class confidence scores begin for the current box. It's calculated as box_index * (5 + total_classes) + 5 (skipping the first 5 values which are box coordinates and object presence confidence)
• This line essentially points a float pointer to the first class confidence score for the current box, making it easy to later find the highest-confidence class and decide whether to keep the box.

If your version's line 136 is something like int left = centerX - width / 2;, that's converting the normalized center coordinates and dimensions from YOLO into pixel-based coordinates for the top-left corner of the bounding box (since YOLO outputs values between 0 and 1 relative to the image size).

2. How to modify the code for image-only classification (no bounding boxes)? #

YOLO is built for detection, but if you only care about the overall image category (not individual objects), you can completely skip the bounding box calculation logic and focus solely on aggregating class confidence scores. Here's how to do it:

Step-by-Step Modifications: #

• Remove all bounding box calculation code: Get rid of any lines that compute centerX, centerY, width, height, left, or top—you don't need these anymore.
• Aggregate class confidences across all boxes: YOLO outputs multiple candidate boxes, so you'll want to track the highest confidence for each class across all boxes.
  • Create an array to store the maximum confidence for each class (initialize all values to 0).
  • For each candidate box:
    1. Skip boxes with low object presence confidence (e.g., below 0.5) to filter out noise.
    2. Grab the class confidence scores for the box.
    3. Multiply the class confidence by the object confidence (YOLO combines these two to get the final class trustworthiness).
    4. Update the maximum confidence for that class if the current value is higher.
• Pick the top class: The class with the highest maximum confidence is your image's classification result.

Simplified Core Code Example: #

// Assume classesCount is the total number of YOLO classes
vector<float> classMaxConfidences(classesCount, 0.0f);
int totalBoxes = output.size[0]; // Number of detection boxes from YOLO
int boxElementCount = 5 + classesCount; // Values per box: 4 coords + 1 obj conf + N class confs

for (int i = 0; i < totalBoxes; ++i) {
    float* boxData = output.ptr<float>(0, i);
    float objConfidence = boxData[4];

    // Skip low-confidence object candidates
    if (objConfidence < 0.5) continue;

    // Get class confidence array
    float* classScores = boxData + 5;
    int topClassIdx = max_element(classScores, classScores + classesCount) - classScores;
    float classConfidence = classScores[topClassIdx];
    float combinedConfidence = objConfidence * classConfidence;

    // Update max confidence for this class
    if (combinedConfidence > classMaxConfidences[topClassIdx]) {
        classMaxConfidences[topClassIdx] = combinedConfidence;
    }
}

// Find the class with the highest confidence
int bestClass = max_element(classMaxConfidences.begin(), classMaxConfidences.end()) - classMaxConfidences.begin();
float bestConfidence = classMaxConfidences[bestClass];

cout << "Image Classification Result: Class " << bestClass << " (Confidence: " << bestConfidence << ")" << endl;

Note: For pure classification, using a model designed specifically for the task (like ResNet or MobileNet) would be more efficient, but this modification works if you have to stick with YOLO.

内容的提问来源于stack exchange，提问作者coder999