Hey there, let's tackle each of your issues step by step:

1. Compress Image to Target Size Without Changing Resolution #

You're right that cv2.resize and cv2.PyrDown work by altering resolution, which isn't what you need. Instead, you should focus on encoding compression—adjusting how the image is stored rather than its pixel dimensions. Here are the most effective methods:

Method 1: Adjust JPEG/PNG/WebP Encoding Parameters #

OpenCV's cv2.imwrite() lets you pass encoding-specific parameters to control file size while keeping resolution intact:

• JPEG: Use cv2.IMWRITE_JPEG_QUALITY (range 0-100; lower = smaller size, more compression artifacts).
• WebP: Often offers better compression than JPEG at the same quality. Use cv2.IMWRITE_WEBP_QUALITY.
• PNG: Use cv2.IMWRITE_PNG_COMPRESSION (range 0-9; higher = more compression, slower write).

Here's a sample script that adjusts JPEG quality to hit your 75KB target:

import cv2
import os

def compress_to_target_size(image_path, target_size_kb=75, output_path="compressed.jpg"):
    # Read the image (keeps original resolution)
    img = cv2.imread(image_path)
    if img is None:
        raise ValueError("Could not read image")
    
    # Start with a mid-range quality
    quality = 50
    step = 5
    while True:
        # Save with current quality
        cv2.imwrite(output_path, img, [cv2.IMWRITE_JPEG_QUALITY, quality])
        # Check file size
        current_size_kb = os.path.getsize(output_path) / 1024
        if current_size_kb <= target_size_kb:
            break
        # If too big, lower quality
        quality -= step
        if quality < 0:
            quality = 0
            break
    print(f"Final quality: {quality}, Final size: {current_size_kb:.2f}KB")
    return output_path

# Usage
compress_to_target_size("your_image.jpg")

WebP might get you better quality at 75KB—just replace the parameter with cv2.IMWRITE_WEBP_QUALITY and change the output extension to .webp.

Method 2: Lossless Compression for PNG #

If you need lossless compression for PNGs, tools like pngquant (integrate via subprocess) can reduce size without losing quality, though savings are smaller than lossy methods.

2. Fix MTCNN Lag on IP Camera #

MTCNN is powerful but computationally heavy—running it on every IP camera frame often causes lag. Try these optimizations:

• Downscale Frames Before Detection: Resize frames to a smaller resolution (e.g., 320x240) for MTCNN inference, then scale detected face coordinates back to the original frame. This cuts computation time drastically while keeping accuracy mostly intact.
• Process a Subset of Frames: Skip every 2-3 frames (e.g., process frames 0, 3, 6...) instead of every single one. This reduces load without noticeable performance drops for most use cases.
• Switch to a Lighter Detector: OpenCV's DNN module has pre-trained SSD or YOLO face detectors that are faster than MTCNN. Example:

  net = cv2.dnn.readNetFromCaffe("deploy.prototxt", "res10_300x300_ssd_iter_140000.caffemodel")
  # Run SSD inference instead of MTCNN
  

• Optimize IP Camera Stream: Ensure your camera streams at a reasonable frame rate (15-20 FPS) and resolution. Lowering the stream's bitrate can reduce capture latency.
• Use Multithreading: Separate frame capture and inference into different threads. This way, the capture doesn't wait for detection to finish before grabbing the next frame.

3. OpenCV cv2.VideoCapture Frame Matrix Conventions #

Here are the key rules for frames grabbed via cv2.VideoCapture.read():

• Shape: Color frames are NumPy arrays with shape (height, width, channels); grayscale frames use (height, width). A 640x480 color frame will have shape (480, 640, 3).
• Color Channel Order: OpenCV uses BGR by default (unlike libraries like PIL which use RGB). To convert to RGB:

  rgb_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
  

• Coordinate System: The top-left corner is (0, 0). The x-axis increases rightward, y-axis increases downward—so (x, y) maps to column x, row y in the array.
• Data Type: Frames are typically uint8 (8-bit unsigned integers), with pixel values ranging from 0 (black) to 255 (white).

内容的提问来源于stack exchange，提问作者Jeyan