def run_model_on_corpus_image(checkpoint, imagenum, output_blobs):
# This is based on decaf's "imagenet" script:
corpus = get_image_corpus()
image = corpus.get_all_images_data()[imagenum] - corpus.get_mean()
model = get_models()[checkpoint]
arr = image.astype(np.float32)
return model.predict(data=arr, output_blobs=output_blobs)
def run_model_on_corpus_image(checkpoint, imagenum, output_blobs):
# This is based on decaf's "imagenet" script:
corpus = get_image_corpus()
image = corpus.get_all_images_data()[imagenum] - corpus.get_mean()
model = get_models()[checkpoint]
arr = image.astype(np.float32)
return model.predict(data=arr, output_blobs=output_blobs)
def confusion_matrix(checkpoint):
stats = get_model_stats_db().get_stats(checkpoint)
confusion_matrix = stats.confusion_matrix
json_matrix = list(list(float(y) for y in x) for x in confusion_matrix)
label_names = get_image_corpus().label_names
sample_images = stats.images_by_classification
SAMPLE_IMAGE_LIMIT = 9
sample_images = [[x[:SAMPLE_IMAGE_LIMIT] for x in y] for y in sample_images]
return jsonify({"confusionmatrix": json_matrix, "labelnames": label_names, "sampleimages": sample_images})
def predict_for_image(checkpoint, imagenum):
"""
Return predictions for a particular image.
"""
features = run_model_on_corpus_image(checkpoint, imagenum, ["probs_cudanet_out"])
class_number_probs = enumerate(features["probs_cudanet_out"][0])
corpus = get_image_corpus()
class_label_probs = [{"class": corpus.label_names[l], "prob": float(p)} for (l, p) in class_number_probs]
return jsonify({"predictions": class_label_probs})
def layer_filters_channels_image_json(checkpoints, layernames, filters, channels, imagenum):
corpus = get_image_corpus()
image = corpus.get_image(imagenum)
arr = np.array(image.getdata()).reshape(1, 32, 32, 3).astype(np.float32)
out = select_region_query(
get_models(), times=checkpoints, layers=layernames, filters=filters, channels=channels, image=arr
)
images = out
# images = mapterminals(show_multiple, out)
return images
def get_image_from_corpus(image_num):
corpus = get_image_corpus()
image = corpus.get_image(image_num)
scale = int(request.args.get('scale', 1))
if scale != 1:
(width, height) = image.size
image = image.resize((width * scale, height * scale), Image.NEAREST)
png_buffer = StringIO()
image.save(png_buffer, format="PNG")
png = png_buffer.getvalue()
png_buffer.close()
return Response(png, mimetype="image/png")
def get_image_from_corpus(image_num):
corpus = get_image_corpus()
image = corpus.get_image(image_num)
scale = int(request.args.get("scale", 1))
if scale != 1:
(width, height) = image.size
image = image.resize((width * scale, height * scale), Image.NEAREST)
png_buffer = StringIO()
image.save(png_buffer, format="PNG")
png = png_buffer.getvalue()
png_buffer.close()
return Response(png, mimetype="image/png")
def predict_for_image(checkpoint, imagenum):
"""
Return predictions for a particular image.
"""
features = run_model_on_corpus_image(checkpoint, imagenum,
["probs_cudanet_out"])
class_number_probs = enumerate(features["probs_cudanet_out"][0])
corpus = get_image_corpus()
class_label_probs = [{
'class': corpus.label_names[l],
'prob': float(p)
} for (l, p) in class_number_probs]
return jsonify({'predictions': class_label_probs})
def confusion_matrix(checkpoint):
stats = get_model_stats_db().get_stats(checkpoint)
confusion_matrix = stats.confusion_matrix
json_matrix = list(list(float(y) for y in x) for x in confusion_matrix)
label_names = get_image_corpus().label_names
sample_images = stats.images_by_classification
SAMPLE_IMAGE_LIMIT = 9
sample_images = [[x[:SAMPLE_IMAGE_LIMIT] for x in y]
for y in sample_images]
return jsonify({
'confusionmatrix': json_matrix,
'labelnames': label_names,
'sampleimages': sample_images
})
def layer_filters_channels_image_json(checkpoints, layernames, filters,
channels, imagenum):
corpus = get_image_corpus()
image = corpus.get_image(imagenum)
arr = np.array(image.getdata()).reshape(1, 32, 32, 3).astype(np.float32)
out = select_region_query(get_models(),
times=checkpoints,
layers=layernames,
filters=filters,
channels=channels,
image=arr)
images = out
#images = mapterminals(show_multiple, out)
return images
def image_corpus_query(query):
corpus = get_image_corpus()
# TODO: limit the number of search results, or paginate them.
results = dict(corpus.find_images(query))
return jsonify(results)
def image_corpus_query(query):
corpus = get_image_corpus()
# TODO: limit the number of search results, or paginate them.
results = dict(corpus.find_images(query))
return jsonify(results)
