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generateSubmissionForAllObjects.py
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generateSubmissionForAllObjects.py
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import json
from pprint import pprint
import cv2
from keras.models import load_model
TEST_BB_FILENAME = "test_stg1.txt"
TEST_IMAGE_FOLDER = "test_stg1/dummy/"
TEST2_BB_FILENAME = "test_stg2.txt"
TEST2_IMAGE_FOLDER = "test_stg2/"
SIZE_OF_BOX_USED = (64,64)
MODEL_FILENAMES = ["modelALLOBJ_0-64.h5", "modelALLOBJ_1-64.h5", "modelALLOBJ_2-64.h5"]
def merge_several_folds_mean(data, nfolds):
a = np.array(data[0])
for i in range(1, nfolds):
a += np.array(data[i])
a /= nfolds
return a.tolist()
def getResult(bbFilename, imgFolder, stage):
print ("Generating Result")
# GENERATE MODELS
models = []
for modelName in MODEL_FILENAMES:
model = load_model(modelName)
models.append(model)
ids = []
with open(bbFilename) as data_file:
data = json.load(data_file)
for oneImage in data:
oneImageFilename = imgFolder + oneImage["filename"]
if stage == 0:
ids.append(oneImage["filename"])
else:
ids.append(oneImageFilename)
print ("Processing: " + oneImageFilename)
img = cv2.imread(oneImageFilename)
h, w, c = img.shape
numBoxesProcessed = 0
test_data = []
X_test_id = []
for box in oneImage["annotations"]:
y = box['y']
x = box['x']
width = box['width']
height = box['height']
if x < 0:
x = 0
if y < 0:
y = 0
if width > w:
width = w
if height > h:
height = h
crop_img = img[y:y+height, x:x+width] # Crop from x, y, w, h -> 100, 200, 300, 400
resized = cv2.resize(crop_img, SIZE_OF_BOX_USED, cv2.INTER_LINEAR)
test_data.append(resized)
X_test_id.append(numBoxesProcessed)
# cv2.imshow("title", resized)
# cv2.waitKey(0)
# cv2.destroyAllWindows()
numBoxesProcessed += 1
test_data = np.array(test_data, dtype=np.uint8)
test_data = test_data.transpose((0, 3, 1, 2))
test_data = test_data.astype('float32')
test_data = test_data / 255
batch_size = 20
yfull_test = []
test_id = []
nfolds = len(models)
for i in range(nfolds):
model = models[i]
test_prediction = model.predict(test_data, batch_size=batch_size, verbose=2)
yfull_test.append(test_prediction)
test_res = merge_several_folds_mean(yfull_test, nfolds)
for oneBoxSol in test_res:
print oneBoxSol
break
return predictions, ids
def main():
prediction1, id1 = getResult(TEST_BB_FILENAME, TEST_IMAGE_FOLDER, 0)
# prediction2, id2 = getResult(TEST2_BB_FILENAME, TEST_IMAGE_FOLDER, 1)
if __name__ == '__main__':
main()