Instructions Write a method classify.py that takes in an image and returns a pre
ID: 3742377 • Letter: I
Question
Instructions
Write a method classify.py that takes in an image and returns a prediction - ball, brick, or cylinder.
An example script in located in challenge/sample_student.py
Your script will be automatically evaluated on a set of test images.
The testing images are quite similar to the training images, and organized into the same difficulty categories.
You are allowed 10 submissions to the evaluation server, which will provide immediate feedback.
The Data
Easy Examples
print("Score = ", score)
## ---------------------------- ## ## ## sample_student.py ## import numpy as np #It's kk to import whatever you want from the local util module if you would like: #from util.X import ... def classify(im): ''' Example submission for coding challenge. Args: im (nxmx3) unsigned 8-bit color image Returns: One of three strings: 'brick', 'ball', or 'cylinder' ''' #Let's guess randomly! Maybe we'll get lucky. labels = ['brick', 'ball', 'cylinder'] random_integer = np.random.randint(low = 0, high = 3) return labels[random_integer]Explanation / Answer
import numpy as np import os import sys import argparse import glob import time import caffe def main(argv): pycaffe_dir = os.path.dirname(__file__) parser = argparse.ArgumentParser() # Required arguments: input and output files. parser.add_argument( "input_file", help="Input image, directory, or npy." ) parser.add_argument( "output_file", help="Output npy filename." ) # Optional arguments. parser.add_argument( "--model_def", default=os.path.join(pycaffe_dir, "../models/bvlc_reference_caffenet/deploy.prototxt"), help="Model definition file." ) parser.add_argument( "--pretrained_model", default=os.path.join(pycaffe_dir, "../models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel"), help="Trained model weights file." ) parser.add_argument( "--gpu", action='store_true', help="Switch for gpu computation." ) parser.add_argument( "--center_only", action='store_true', help="Switch for prediction from center crop alone instead of " + "averaging predictions across crops (default)." ) parser.add_argument( "--images_dim", default='256,256', help="Canonical 'height,width' dimensions of input images." ) parser.add_argument( "--mean_file", default=os.path.join(pycaffe_dir, 'caffe/imagenet/ilsvrc_2012_mean.npy'), help="Data set image mean of [Channels x Height x Width] dimensions " + "(numpy array). Set to '' for no mean subtraction." ) parser.add_argument( "--input_scale", type=float, help="Multiply input features by this scale to finish preprocessing." ) parser.add_argument( "--raw_scale", type=float, default=255.0, help="Multiply raw input by this scale before preprocessing." ) parser.add_argument( "--channel_swap", default='2,1,0', help="Order to permute input channels. The default converts " + "RGB -> BGR since BGR is the Caffe default by way of OpenCV." ) parser.add_argument( "--ext", default='jpg', help="Image file extension to take as input when a directory " + "is given as the input file." ) args = parser.parse_args() image_dims = [int(s) for s in args.images_dim.split(',')] mean, channel_swap = None, None if args.mean_file: mean = np.load(args.mean_file) if args.channel_swap: channel_swap = [int(s) for s in args.channel_swap.split(',')] if args.gpu: caffe.set_mode_gpu() print("GPU mode") else: caffe.set_mode_cpu() print("CPU mode") # Make classifier. classifier = caffe.Classifier(args.model_def, args.pretrained_model, image_dims=image_dims, mean=mean, input_scale=args.input_scale, raw_scale=args.raw_scale, channel_swap=channel_swap) # Load numpy array (.npy), directory glob (*.jpg), or image file. args.input_file = os.path.expanduser(args.input_file) if args.input_file.endswith('npy'): print("Loading file: %s" % args.input_file) inputs = np.load(args.input_file) elif os.path.isdir(args.input_file): print("Loading folder: %s" % args.input_file) inputs =[caffe.io.load_image(im_f) for im_f in glob.glob(args.input_file + '/*.' + args.ext)] else: print("Loading file: %s" % args.input_file) inputs = [caffe.io.load_image(args.input_file)] print("Classifying %d inputs." % len(inputs)) # Classify. start = time.time() predictions = classifier.predict(inputs, not args.center_only) print("Done in %.2f s." % (time.time() - start)) # Save print("Saving results into %s" % args.output_file) np.save(args.output_file, predictions) if __name__ == '__main__': main(sys.argv)