site-builders/foto/thumbnailer.py

#!/usr/bin/env python3

import cv2
import numpy as np
import os.path
import sys

def detect(path):
    img = cv2.imread(path)
    cascade = cv2.CascadeClassifier("detectors/haarcascade_frontalface_alt2.xml")
    rects = cascade.detectMultiScale(img, 1.3, 4, cv2.CASCADE_SCALE_IMAGE, (20,20))

    if len(rects) == 0:
        return [], img
    rects[:, 2:] += rects[:, :2]
    return rects, img

def box(rects, img):
    for x1, y1, x2, y2 in rects:
        cv2.rectangle(img, (x1, y1), (x2, y2), (127, 255, 0), 2)
    return img

def smart_crop(rects, img):
    # Initialize
    height, width, _ = img.shape
    left, top = 0, 0
    max_x, max_y, min_x, min_y = left, top, width, height

    # Find extremes of detected features
    if len(rects):
        print("Detected objects:", len(rects))

    for x0, y0, x1, y1 in rects:
        min_x = min_x if min_x < x0 else x0
        min_y = min_y if min_y < y0 else y0
        max_x = max_x if max_x > x1 else x1
        max_y = max_y if max_y > y1 else y1

    # Focus in the center of detected features
    focus = 0.5 * (np.array([min_x, min_y]) + np.array([max_x, max_y]))

    crop_size = width if width < height else height
    x0, y0 = focus - crop_size / 2
    x1, y1 = focus + crop_size / 2

    # Move crop box towards center when it goes beyond
    x_vector, y_vector = 0, 0
    if x0 < 0:
        x_vector = -x0
    elif x1 > width:
        x_vector = width - x1

    if y0 < 0:
        y_vector = -y0
    elif y1 > height:
        y_vector = height - y1

    x0 = int(x0 + x_vector)
    y0 = int(y0 + y_vector)

    return img[y0 : y0 + crop_size,
               x0 : x0 + crop_size]

if __name__ == '__main__':
    file_name = sys.argv[1]
    print("Cropping:", file_name)
    rects, img = detect(file_name)
    thumb = cv2.resize(smart_crop(rects, img), None,
                       fx = 0.3, fy = 0.3,
                       interpolation = cv2.INTER_CUBIC)
    cv2.imwrite(os.path.join("Skan/thumb", os.path.basename(file_name)), thumb)