Btukfyl

 batch = each[2][i:i + batch_size]

def predict_on_frames(frames_folder, model_file, input_layer, output_layer, batch_size):

input_height = 299

input_width = 299

input_mean = 0

input_std = 255

batch_size = batch_size

graph = load_graph(model_file)



labels_in_dir = os.listdir(frames_folder)

frames = [each for each in os.walk(frames_folder) if os.path.basename(each[0]) in labels_in_dir]



predictions = 

for each in frames:

    label = each[0]

    print("Predicting on frame of %sn" % (label))

    for i in tqdm(range(0, len(each[2]), batch_size), ascii=True):

        batch = each[2][i:i + batch_size]

        try:

            batch = [os.path.join(label, frame) for frame in batch]

            frames_tensors = read_tensor_from_image_file(batch, input_height=input_height, input_width=input_width, input_mean=input_mean, input_std=input_std)

            pred = predict(graph, frames_tensors, input_layer, output_layer)

            pred = [[each.tolist(), os.path.basename(label)] for each in pred]

            predictions.extend(pred)



        except KeyboardInterrupt:

            print("You quit with ctrl+c")

            sys.exit()



        except Exception as e:

            print("Error making prediction: %s" % (e))

            x = input("nDo You Want to continue on other samples: y/n")

            if x.lower() == 'y':

                continue

            else:

                sys.exit()

return predictions

 batch = each[2][i:i + batch_size]

def predict_on_frames(frames_folder, model_file, input_layer, output_layer, batch_size):

input_height = 299

input_width = 299

input_mean = 0

input_std = 255

batch_size = batch_size

graph = load_graph(model_file)



labels_in_dir = os.listdir(frames_folder)

frames = [each for each in os.walk(frames_folder) if os.path.basename(each[0]) in labels_in_dir]



predictions = 

for each in frames:

    label = each[0]

    print("Predicting on frame of %sn" % (label))

    for i in tqdm(range(0, len(each[2]), batch_size), ascii=True):

        batch = each[2][i:i + batch_size]

        try:

            batch = [os.path.join(label, frame) for frame in batch]

            frames_tensors = read_tensor_from_image_file(batch, input_height=input_height, input_width=input_width, input_mean=input_mean, input_std=input_std)

            pred = predict(graph, frames_tensors, input_layer, output_layer)

            pred = [[each.tolist(), os.path.basename(label)] for each in pred]

            predictions.extend(pred)



        except KeyboardInterrupt:

            print("You quit with ctrl+c")

            sys.exit()



        except Exception as e:

            print("Error making prediction: %s" % (e))

            x = input("nDo You Want to continue on other samples: y/n")

            if x.lower() == 'y':

                continue

            else:

                sys.exit()

return predictions

 batch = each[2][i:i + batch_size]

def predict_on_frames(frames_folder, model_file, input_layer, output_layer, batch_size):

input_height = 299

input_width = 299

input_mean = 0

input_std = 255

batch_size = batch_size

graph = load_graph(model_file)



labels_in_dir = os.listdir(frames_folder)

frames = [each for each in os.walk(frames_folder) if os.path.basename(each[0]) in labels_in_dir]



predictions = 

for each in frames:

    label = each[0]

    print("Predicting on frame of %sn" % (label))

    for i in tqdm(range(0, len(each[2]), batch_size), ascii=True):

        batch = each[2][i:i + batch_size]

        try:

            batch = [os.path.join(label, frame) for frame in batch]

            frames_tensors = read_tensor_from_image_file(batch, input_height=input_height, input_width=input_width, input_mean=input_mean, input_std=input_std)

            pred = predict(graph, frames_tensors, input_layer, output_layer)

            pred = [[each.tolist(), os.path.basename(label)] for each in pred]

            predictions.extend(pred)



        except KeyboardInterrupt:

            print("You quit with ctrl+c")

            sys.exit()



        except Exception as e:

            print("Error making prediction: %s" % (e))

            x = input("nDo You Want to continue on other samples: y/n")

            if x.lower() == 'y':

                continue

            else:

                sys.exit()

return predictions

 batch = each[2][i:i + batch_size]

def predict_on_frames(frames_folder, model_file, input_layer, output_layer, batch_size):

input_height = 299

input_width = 299

input_mean = 0

input_std = 255

batch_size = batch_size

graph = load_graph(model_file)



labels_in_dir = os.listdir(frames_folder)

frames = [each for each in os.walk(frames_folder) if os.path.basename(each[0]) in labels_in_dir]



predictions = 

for each in frames:

    label = each[0]

    print("Predicting on frame of %sn" % (label))

    for i in tqdm(range(0, len(each[2]), batch_size), ascii=True):

        batch = each[2][i:i + batch_size]

        try:

            batch = [os.path.join(label, frame) for frame in batch]

            frames_tensors = read_tensor_from_image_file(batch, input_height=input_height, input_width=input_width, input_mean=input_mean, input_std=input_std)

            pred = predict(graph, frames_tensors, input_layer, output_layer)

            pred = [[each.tolist(), os.path.basename(label)] for each in pred]

            predictions.extend(pred)



        except KeyboardInterrupt:

            print("You quit with ctrl+c")

            sys.exit()



        except Exception as e:

            print("Error making prediction: %s" % (e))

            x = input("nDo You Want to continue on other samples: y/n")

            if x.lower() == 'y':

                continue

            else:

                sys.exit()

return predictions

Your Answer

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