82 lines
		
	
	
		
			3.1 KiB
		
	
	
	
		
			Python
		
	
	
		
			Executable File
		
	
	
	
	
			
		
		
	
	
			82 lines
		
	
	
		
			3.1 KiB
		
	
	
	
		
			Python
		
	
	
		
			Executable File
		
	
	
	
	
| #!/usr/bin/env python
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| 
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| import sys, os, os.path, glob, math, cv2, string, random
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| from datetime import datetime
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| from optparse import OptionParser
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| import re
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| import numpy as np
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| from xml.dom import minidom
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| 
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| def resize(image, d_w, d_h):
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|     if (d_h < image.shape[0]) or (d_w < image.shape[1]):
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|         ratio = min(d_h / float(image.shape[0]), d_w / float(image.shape[1]))
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| 
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|         kernel_size = int( 5 / (2 * ratio))
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|         sigma = 0.5 / ratio
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|         image_to_resize = cv2.filter2D(image, cv2.CV_8UC3, cv2.getGaussianKernel(kernel_size, sigma))
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|         interpolation_type = cv2.INTER_AREA
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|     else:
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|         image_to_resize = image
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|         interpolation_type = cv2.INTER_CUBIC
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| 
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|     return cv2.resize(image_to_resize,(d_w, d_h), None, 0, 0, interpolation_type)
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| 
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| def det2negative(xmldoc, opath):
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|     samples = xmldoc.getElementsByTagName('sample')
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|     for sample in samples:
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|         detections = sample.getElementsByTagName('detections')
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|         detections = minidom.parseString(detections[0].toxml())
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|         detections = detections.getElementsByTagName("_")
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|         if len(detections) is not 0:
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|             path = sample.getElementsByTagName("path")
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|             path = path[0].firstChild.nodeValue
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|             mat = cv2.imread(path)
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|             mat_h, mat_w, _ = mat.shape
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| 
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|             for detection in detections:
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|                 detection = detection.childNodes
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|                 for each in detection:
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|                     rect = eval(re.sub( r"\b\s\b", ",", re.sub(r"\n", "[", each.nodeValue )) + "]")
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|                     print rect
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| 
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|                     ratio = 64.0 / rect[3]
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| 
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|                     print rect, ratio
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|                     mat = resize(mat, int(round(mat_w * ratio)), int(round(mat_h * ratio)))
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| 
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|                     rect[0] = int(round(ratio * rect[0])) - 10
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|                     rect[1] = int(round(ratio * rect[1])) - 10
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|                     rect[2] = rect[0] + 32 + 20
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|                     rect[3] = rect[1] + 64 + 20
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|                     try:
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|                         cropped = mat[rect[1]:(rect[3]), rect[0]:(rect[2]), :]
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|                         img = os.path.join(opath, ''.join(random.choice(string.lowercase) for i in range(8)) + ".png")
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|                         cr_h, cr_w, _ = cropped.shape
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|                         if cr_h is 84 and cr_w is 52:
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|                             cv2.imwrite(img, cropped)
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|                     except:
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|                         pass
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| 
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| if __name__ == "__main__":
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| 
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|     parser = OptionParser()
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|     parser.add_option("-i", "--input", dest="input", metavar="DIRECTORY", type="string",
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|                        help="Path to the xml collection folder.")
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| 
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|     parser.add_option("-d", "--output-dir", dest="output", metavar="DIRECTORY", type="string",
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|                        help="Path to store data", default=".")
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| 
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|     (options, args) = parser.parse_args()
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| 
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|     if not options.input:
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|         parser.error("Input folder is required.")
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| 
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|     opath = os.path.join(options.output, datetime.now().strftime("negatives" + "-%Y-%m-%d-%H-%M-%S"))
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|     os.mkdir(opath)
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| 
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|     gl = glob.iglob( os.path.join(options.input, "set[0][0]_V0[0][5].seq.xml"))
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|     for f in gl:
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|         print f
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|         xmldoc = minidom.parse(f)
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|         det2negative(xmldoc, opath)
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