What’s nice about this approach is that we can “guide” what the output image looks like. You’ll also need a working installation of Caffe. If you don’t already have bat-country installed, either pull down the code from the GitHub repo or use pip to install it on your system: pip install bat-country or pip install -upgrade bat-country. Iter_n=20, objective_fn=BatCountry.guided_objective, Image = bc.dream(np.float32(Image.open(args.image)), end=args.layer, # construct the argument parser and parse the argumentsĪp.add_argument("-b", "-base-model", required=True, help="base model path")Īp.add_argument("-l", "-layer", type=str, default="inception_4c/output",Īp.add_argument("-i", "-image", required=True, help="path to base image")Īp.add_argument("-g", "-guide-image", required=True, help="path to guide image")Īp.add_argument("-o", "-output", required=True, help="path to output image")įeatures = bc.prepare_guide(Image.open(args.guide_image), end=args.layer) Here’s some quick sample code: # import the necessary packages Using bat-country, it’s just as easy to perform guided dreaming as deep dreaming. This method passes your input image through the network in a similar manner, but this time using your seed image to guide and influence the output. Last Friday the Google Research team posted an update to their deep dream working demonstrating it was possible to guide your dreaming process by supplying a seed image. In the remainder of this blog post we’ll play around with the new bat-country update to perform guided dreaming - and even use it to generate our own art using guided deep dreaming! Guided deep dreaming I simply defined a new objective function, allowing the step function to be further customized, and we’re done! In fact, defining your own custom objective function is the the exact route I took when extending bat-country. Want to change the objective function? Again, just define your own objective and you’re good to go. Define your own custom processor and pass it in. Want to change how each image is pre-processed or post-processed? No problem. The secret to this quick turnaround is the extensibility of the BatCountry class where nearly every function and every method can be overridden and extended. I honestly spent more time running the Python scripts to gather example images and updating the documentation than I did updating the codebase. Truth be told, it only took ~20 minutes from start-to-finish to get the code together. The results were quite impressive - so I decided to port the functionality to bat-country. This shows the clear change of direction Mercedes are now taking with their 2023 challenger.One of the main benefits of the bat-country Python package for deep dreaming and visualization is its ease of use, extensibility, and customization.Īnd let me tell you, that customization really came in handy last Friday when the Google Research team released an update to their deep dream work, demonstrating a method to “guide” your input images to visualize the features of a target image. In any F1 car, the front suspension is essential to how the car works with the airflow as a whole since the front suspension acts as a bridge and sets the tone with which the whole car will ultimately interpret, use and distribute the airflow. Consisting of a new front suspension, floor and sidepods, Mercedes are hoping this will give them much-needed rear downforce, which is a particular area where the W14 is struggling with. In a bid to change their current situation as swiftly as possible, Mercedes have brought the W14’s upgrade package to the Monaco Grand Prix weekend. Though this decision was likely made before the technical team changed as parts needed to be designed and put in the wind tunnel. With the swapping of Mike Elliot’s and James Allison’s positions within the team and with the turn-around in the design philosophy of the car, Mercedes have decided to abandon their innovative and courageous, yet failed no sidepod philosophy.
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