def process_image(img, model=MODEL):
"""
Given an image and a model, this will classify the image and annotate the image
This is used in the GUI to display results to the user
img_with_info: image with annotated text and coloring
frames_class_type: Enum that is either frames.Pipe.DEFECT or frames.Pipe.NONDEFECT
current_defect_frames: List containing the defect frames for use in the FRAME_TRACKER
"""
img_with_info = np.copy(img)
desired_size = (640, 480)
images = np.array(cv2.resize(cv2.cvtColor(img, cv2.COLOR_RGB2BGR), desired_size), dtype=np.uint8)
processed_imgs = ip.preprocess([images], real_time=True)
res = model.predict(processed_imgs)
denom = sum(res[0])
nondefect_chance = res[0][0]/denom
defect_chance = res[0][1]/denom
img_class = np.argmax(res[0])
font = cv2.FONT_HERSHEY_DUPLEX
FRAME_TRACKER.add_frame(img_with_info, img_class)
frames_class_type = FRAME_TRACKER.get_frames_class()
current_defect_frames = []
if frames_class_type == frames_test.Pipe.DEFECT and defect_chance > 0.75:
text = 'DEFECT {:.2f}'.format(defect_chance)
img_with_info = ip.color_edges(img_with_info)
cv2.putText(img_with_info, text, (25, 465), font, 1.25, (255, 0, 0), 2, cv2.LINE_AA)
FRAME_TRACKER.add_previous_defect(img_with_info)
current_defect_frames = FRAME_TRACKER.get_previous_defects()
else:
text = 'NONDEFECT {:.2f}'.format(nondefect_chance)
cv2.putText(img_with_info, text, (25, 465), font, 1.25, (255, 255, 255), 2, cv2.LINE_AA)
return img_with_info, frames_class_type, current_defect_frames
def keras_lenet_predict():
"""
Used for testing your model against a folder of known image classes.
"""
base_dir = 'E:\\autopipe_test\\data\\classifications\\'
defects_dir = base_dir + 'defect\\'
nondefects_dir = base_dir + 'nondefect\\'
defect_imgs = os.listdir(defects_dir)
nondefect_imgs = os.listdir(nondefects_dir)
images = []
labels = []
desired_size = ip.END_IMAGE_SIZE
for image in defect_imgs:
image = cv2.resize(cv2.imread(defects_dir + image), desired_size)
images.append(image)
labels.append(1)
for image in nondefect_imgs:
image = cv2.resize(cv2.imread(nondefects_dir + image), desired_size)
images.append(image)
labels.append(0)
processed_imgs = ip.preprocess(images)
model = kmodel.load_model('E:/autopipe_test/data/models/lenet_64.h5')
res = model.predict(processed_imgs)
print(res)
## print(len(res))
## for scores in res:
## print(np.argmax(scores))
classes = [np.argmax(scores) for scores in res]
number_defects_detected = classes.count(1)
number_nondefects_detected = classes.count(0)
number_defects = labels.count(1)
number_nondefects = labels.count(0)
print(labels)
print(classes)
print(accuracy(labels, classes))
validation_data=(train['valid'][0], train['valid'][1]),
shuffle=True,
epochs=10)
score = model.evaluate(train["test"][0], train['test'][1], verbose=0)
print(score)
model.save('C:/Autopipe//data/models/lenet_' + str(ip.END_IMAGE_SIZE[0]) +
'.h5')
if __name__ == "__main__":
## data_pkl = os.getcwd() + '/data/serialized/autopipe-' + str(ip.END_IMAGE_SIZE[0]) + '-data.pkl'
data_pkl = 'C:/Autopipe/data/serialized/autopipe-' + str(
ip.END_IMAGE_SIZE[0]) + '-data.pkl'
data = srl.load_data(data_pkl)
X_train, y_train = data["train"]
X_valid, y_valid = data["valid"]
X_test, y_test = data['test']
normalized_data = {
"train": [ip.preprocess(X_train),
to_categorical(y_train)],
"valid": [ip.preprocess(X_valid),
to_categorical(y_valid)],
"test": [ip.preprocess(X_test),
to_categorical(y_test)]
}
run_keras_lenet_model(normalized_data)
print(len(train['valid'][0]))
print(len(train['valid'][1]))
model.fit(train['train'][0], train['train'][1],
validation_data=(train['valid'][0], train['valid'][1]),
shuffle=True, nb_epoch=50, batch_size = 124)
## model.fit(train['train'][0], train['train'][1],
## validation_data=(train['valid'][0], train['valid'][1]),
## shuffle=True, epochs=10)
## score = model.evaluate(train["test"][0], train['test'][1], verbose=0)
score = model.evaluate(train["test"][0], train['test'][1], verbose=1, batch_size = 40)
print(score)
model.save('E:/autopipe_test/data/models/lenet_' + str(ip.END_IMAGE_SIZE[0]) + '.h5')
if __name__ == "__main__":
## data_pkl = os.getcwd() + '/data/serialized/autopipe-' + str(ip.END_IMAGE_SIZE[0]) + '-data.pkl'
data_pkl = 'E:/autopipe_test/data/serialized/autopipe-' + str(ip.END_IMAGE_SIZE[0]) + '-data.pkl'
data = srl.load_data(data_pkl)
X_train, y_train = data["train"]
X_valid, y_valid = data["valid"]
X_test, y_test = data['test']
normalized_data = {"train": [ip.preprocess(X_train), to_categorical(y_train)],
"valid": [ip.preprocess(X_valid), to_categorical(y_valid)],
"test": [ip.preprocess(X_test), to_categorical(y_test)]}
run_keras_lenet_model(normalized_data)