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Updated script for img recognition

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This commit is contained in:
nlitkowski 2019-06-09 22:49:49 +02:00
parent ed36703151
commit 991398e162
1 changed files with 40 additions and 23 deletions
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63
Logic/TrashRecognition/ImageClassification.py
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@ -72,6 +72,43 @@ def load_labels(label_file):
label.append(l.rstrip())
return label
def classify(model="Model/graph.pb",
label_file="Model/retrained_labels.txt",
input_height=299,
input_width=299,
input_mean=128,
input_std=128,
input_layer="input", #"input",
output_layer="final_result"): # "InceptionV3/Predictions/Reshape_1"):
"""Returns list of tuples consisting of name of file, category and certainity (0 - 1)"""
graph = load_graph(model_file)
files = []
for filename in os.listdir('Images/TestImages'):
t = read_tensor_from_image_file(
f'Images/TestImages/{filename}',
input_height=input_height,
input_width=input_width,
input_mean=input_mean,
input_std=input_std)
input_name = "import/" + input_layer
output_name = "import/" + output_layer
input_operation = graph.get_operation_by_name(input_name)
output_operation = graph.get_operation_by_name(output_name)
with tf.Session(graph=graph) as sess:
results = sess.run(output_operation.outputs[0], {
input_operation.outputs[0]: t
})
results = np.squeeze(results)
top_k = results.argsort()[-5:][::-1]
labels = load_labels(label_file)
files.append((filename, labels[top_k[0]], results[top_k[0]]))
print(f'{filename}: {labels[top_k[0]]} with {results[top_k[0]] * 100}% certainity')
return files
if __name__ == "__main__":
model_file = "Model/graph.pb"
@ -128,29 +165,9 @@ if __name__ == "__main__":
if args.output_layer:
output_layer = args.output_layer
graph = load_graph(model_file)
for filename in os.listdir('Images/TestImages'):
t = read_tensor_from_image_file(
f'Images/TestImages/{filename}',
input_height=input_height,
input_width=input_width,
input_mean=input_mean,
input_std=input_std)
input_name = "import/" + input_layer
output_name = "import/" + output_layer
input_operation = graph.get_operation_by_name(input_name)
output_operation = graph.get_operation_by_name(output_name)
with tf.Session(graph=graph) as sess:
results = sess.run(output_operation.outputs[0], {
input_operation.outputs[0]: t
})
results = np.squeeze(results)
top_k = results.argsort()[-5:][::-1]
labels = load_labels(label_file)
print(f'{filename}: {labels[top_k[0]]} with {results[top_k[0]] * 100}% certainity')
classify(model=model_file, label_file=label_file, input_height=input_height, input_width=input_width,
input_mean=input_mean, input_std=input_std, input_layer=input_layer, output_layer=output_layer)
# for i in top_k:
# print(labels[i], results[i])