omz · July 8, 2018 10:36 · Jul 8, 2018
diff --git a/CoreML Image Recognition.py b/CoreML Image Recognition.py
@@ -0,0 +1,108 @@
+#!python3
+'''
+This is a demo of how you can use the CoreML framework (via objc_util) to classify images in Pythonista. It downloads the trained 'MobileNet' CoreML model from the Internet, and uses it to classify images that are either taken with the camera, or picked from the photo library.
+'''
+
+import requests
+import os
+import io
+import photos
+import dialogs
+from PIL import Image
+from objc_util import ObjCClass, nsurl, ns
+
+# Configuration (change URL and filename if you want to use a different model):
+MODEL_URL = 'https://docs-assets.developer.apple.com/coreml/models/MobileNet.mlmodel'
+MODEL_FILENAME = 'mobilenet.mlmodel'
+
+# Use a local path for caching the model file (no need to sync this with iCloud):
+MODEL_PATH = os.path.join(os.path.expanduser('~/Documents'), MODEL_FILENAME)
+
+# Declare/import ObjC classes:
+MLModel = ObjCClass('MLModel')
+VNCoreMLModel = ObjCClass('VNCoreMLModel')
+VNCoreMLRequest = ObjCClass('VNCoreMLRequest')
+VNImageRequestHandler = ObjCClass('VNImageRequestHandler')
+
+def load_model():
+	'''Helper method for downloading/caching the mlmodel file'''
+	if not os.path.exists(MODEL_PATH):
+		print(f'Downloading model: {MODEL_FILENAME}...')
+		r = requests.get(MODEL_URL, stream=True)
+		file_size = int(r.headers['content-length'])
+		with open(MODEL_PATH, 'wb') as f:
+			bytes_written = 0
+			for chunk in r.iter_content(1024*100):
+				f.write(chunk)
+				print(f'{bytes_written/file_size*100:.2f}% downloaded')
+				bytes_written += len(chunk)
+		print('Download finished')
+	ml_model_url = nsurl(MODEL_PATH)
+	# Compile the model:
+	c_model_url = MLModel.compileModelAtURL_error_(ml_model_url, None)
+	# Load model from the compiled model file:
+	ml_model = MLModel.modelWithContentsOfURL_error_(c_model_url, None)
+	# Create a VNCoreMLModel from the MLModel for use with the Vision framework:
+	vn_model = VNCoreMLModel.modelForMLModel_error_(ml_model, None)
+	return vn_model
+
+
+def _classify_img_data(img_data):
+	'''The main image classification method, used by `classify_image` (for camera images) and `classify_asset` (for photo library assets).'''
+	vn_model = load_model()
+	# Create and perform the recognition request:
+	req = VNCoreMLRequest.alloc().initWithModel_(vn_model).autorelease()
+	handler = VNImageRequestHandler.alloc().initWithData_options_(img_data, None).autorelease()
+	success = handler.performRequests_error_([req], None)
+	if success:
+		best_result = req.results()[0]
+		label = str(best_result.identifier())
+		confidence = best_result.confidence()
+		return {'label': label, 'confidence': confidence}
+	else:
+		return None
+
+
+def classify_image(img):
+	buffer = io.BytesIO()
+	img.save(buffer, 'JPEG')
+	img_data = ns(buffer.getvalue())
+	return _classify_img_data(img_data)
+
+
+def classify_asset(asset):
+	img_data = ns(asset.get_image_data().getvalue())
+	return _classify_img_data(img_data)
+
+
+def scale_image(img, max_dim):
+	'''Helper function to downscale an image for showing in the console'''
+	scale = max_dim / max(img.size)
+	w = int(img.size[0] * scale)
+	h = int(img.size[1] * scale)
+	return img.resize((w, h), Image.ANTIALIAS)
+
+
+def main():
+	r = dialogs.alert('Classify Image', '', 'Camera', 'Photo Library')
+	if r == 1:
+		img = photos.capture_image()
+		if img is None:
+			return
+		scale_image(img, 224).show()
+		result = classify_image(img)
+	else:
+		asset = photos.pick_asset()
+		if asset is None:
+			return
+		result = classify_asset(asset)
+		asset.get_ui_image((255, 255)).show()
+	if result:
+		print(result)
+	else:
+		print('Image classification failed')
+
+
+if __name__ == '__main__':
+	main()
+
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