Open Source Computer Vision Library
https://opencv.org/
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79 lines
1.9 KiB
79 lines
1.9 KiB
import numpy as np |
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import cv2, cv |
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import common |
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def detect(img, cascade): |
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min_size = (20, 20) |
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haar_scale = 1.1 |
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min_neighbors = 3 |
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haar_flags = 0 |
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rects = cascade.detectMultiScale(img, haar_scale, min_neighbors, haar_flags, min_size) |
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if len(rects) == 0: |
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return |
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rects[:,2:] += rects[:,:2] |
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return rects |
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def detect_turned(img, cascade): |
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img_t = cv2.transpose(img) |
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img_cw = cv2.flip(img_t, 1) |
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img_ccw = cv2.flip(img_t, 0) |
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r = detect(img, cascade) |
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r_cw = detect(img_cw, cascade) |
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r_ccw = detect(img_ccw, cascade) |
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h, w = img.shape[:2] |
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if r_cw is not None: |
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r_cw[:,[0, 2]] = h - r_cw[:,[0, 2]] - 1 |
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r_cw = r_cw[:,[1,0,3,2]] |
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if r_ccw is not None: |
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r_ccw[:,[1, 3]] = w - r_ccw[:,[1, 3]] - 1 |
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r_ccw = r_ccw[:,[1,0,3,2]] |
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rects = np.vstack( [a for a in [r, r_cw, r_ccw] if a is not None] ) |
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return rects |
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def process_image(fn, cascade): |
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pass |
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if __name__ == '__main__': |
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import sys |
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import getopt |
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args, img_mask = getopt.getopt(sys.argv[1:], '', ['cascade=']) |
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args = dict(args) |
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cascade_fn = args.get('--cascade', "../../data/haarcascades/haarcascade_frontalface_alt.xml") |
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cascade = cv2.CascadeClassifier(cascade_fn) |
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img = cv2.imread('test.jpg') |
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h, w = img.shape[:2] |
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r = 512.0 / max(h, w) |
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small = cv2.resize(img, (int(w*r), int(h*r)), interpolation=cv2.INTER_AREA) |
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rects = detect_turned(small, cascade) |
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print rects |
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for x1, y1, x2, y2 in rects: |
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cv2.rectangle(small, (x1, y1), (x2, y2), (0, 255, 0)) |
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cv2.circle(small, (x1, y1), 2, (0, 0, 255), -1) |
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cv2.imshow('img', small) |
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cv2.waitKey() |
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''' |
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img = cv2.imread('test.jpg') |
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h, w = img.shape[:2] |
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r = 512.0 / max(h, w) |
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small = cv2.resize(img, (w*r, h*r), interpolation=cv2.INTER_AREA) |
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cv2.imshow('img', small) |
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cv2.waitKey() |
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''' |