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Main loop profiling and disabling unnecessary parts #61

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84 changes: 71 additions & 13 deletions virtual_webcam.py
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Original file line number Diff line number Diff line change
Expand Up @@ -18,6 +18,7 @@
from bodypix_functions import to_mask_tensor

import filters
import time


def load_config(config_mtime, oldconfig={}):
Expand Down Expand Up @@ -147,8 +148,11 @@ def reload_layers(config):
if config['real_video_device'].lower().endswith(extension):
success, static_image = cap.read()

frameNo = 0

def mainloop():
global config, masks, layers, config_mtime
global config, masks, layers, config_mtime, frameNo
time_0_start = time.time()

config, config_mtime_new = load_config(config_mtime, config)
if config_mtime != config_mtime_new:
Expand All @@ -158,6 +162,8 @@ def mainloop():
layers = reload_layers(config)
config_mtime = config_mtime_new

time_1_config = time.time()

if static_image is not None:
success, frame = True, static_image
else:
Expand All @@ -169,6 +175,8 @@ def mainloop():
frame = frame[...,::-1]
frame = frame.astype(np.float)

time_2_capture = time.time()

input_height, input_width = frame.shape[:2]
internal_resolution = config.get("internal_resolution", 0.5)

Expand All @@ -184,6 +192,8 @@ def mainloop():

resized_height, resized_width = resized_frame.shape[:2]

time_3_resize = time.time()

# Preprocessing
if model_type == "mobilenet":
resized_frame = np.divide(resized_frame, 127.5)
Expand All @@ -196,9 +206,12 @@ def mainloop():

sample_image = resized_frame[tf.newaxis, ...]

time_4_preprocess = time.time()

results = sess.run(output_tensor_names,
feed_dict={input_tensor: sample_image})

time_5_network = time.time()

if model_type == "mobilenet":
segment_logits = results[1]
Expand All @@ -208,30 +221,50 @@ def mainloop():
segment_logits = results[6]
part_heatmaps = results[5]
heatmaps = results[2]

time_6_result1 = time.time()

scaled_segment_scores = scale_and_crop_to_input_tensor_shape(
segment_logits, input_height, input_width,
padT, padB, padL, padR, True
)

time_6_result2 = time.time()

scaled_part_heatmap_scores = scale_and_crop_to_input_tensor_shape(
part_heatmaps, input_height, input_width,
padT, padB, padL, padR, True
)
# not needed when doing only background replace
# scaled_part_heatmap_scores = scale_and_crop_to_input_tensor_shape(
# part_heatmaps, input_height, input_width,
# padT, padB, padL, padR, True
# )

scaled_heatmap_scores = scale_and_crop_to_input_tensor_shape(
heatmaps, input_height, input_width,
padT, padB, padL, padR, True
)
time_6_result3 = time.time()

# not needed when doing only background replace
# scaled_heatmap_scores = scale_and_crop_to_input_tensor_shape(
# heatmaps, input_height, input_width,
# padT, padB, padL, padR, True
# )

time_6_result4 = time.time()

mask = to_mask_tensor(scaled_segment_scores,
config.get("segmentation_threshold", 0.75))

time_6_result5 = time.time()

mask = np.reshape(mask, mask.shape[:2])

part_masks = to_mask_tensor(scaled_part_heatmap_scores, 0.999)
part_masks = np.array(part_masks)
heatmap_masks = to_mask_tensor(scaled_heatmap_scores, 0.99)
heatmap_masks = np.array(heatmap_masks)
time_6_result6 = time.time()

# not needed when doing only background replace
#part_masks = to_mask_tensor(scaled_part_heatmap_scores, 0.999)
#part_masks = np.array(part_masks)
#heatmap_masks = to_mask_tensor(scaled_heatmap_scores, 0.99)
#heatmap_masks = np.array(heatmap_masks)
part_masks = None
heatmap_masks = None

time_6_result = time.time()

# Average over the last N masks to reduce flickering
# (at the cost of seeing afterimages)
Expand Down Expand Up @@ -259,6 +292,8 @@ def mainloop():

frame = np.append(frame, np.expand_dims(mask, axis=2), axis=2)

time_7_postprocess = time.time()

input_frame = frame.copy()
frame = np.zeros(input_frame.shape)
for layer_type, layer_filters in layers:
Expand Down Expand Up @@ -287,6 +322,8 @@ def mainloop():
else:
frame[:,:,:3] = layer_frame[:,:,:3].copy()

time_8_compose = time.time()

# Remove alpha channel
frame = frame[:,:,:3]

Expand All @@ -308,6 +345,27 @@ def mainloop():
frame = frame.astype(np.uint8)
fakewebcam.schedule_frame(frame)

time_9_show = time.time()
# Output the theoretical FPS for each processing stage
if (frameNo % 100) == 0:
print("Config {}".format(1/(time_1_config - time_0_start)))
print("Capture {}".format(1/(time_2_capture - time_1_config)))
print("Resize {}".format(1/(time_3_resize - time_2_capture)))
print("Preprocess {}".format(1/(time_4_preprocess - time_3_resize)))
print("Network {}".format(1/(time_5_network - time_4_preprocess)))
print("Result {}".format(1/(time_6_result - time_5_network)))
print("Result 1 {}".format(1/(time_6_result1 - time_5_network)))
print("Result 2 {}".format(1/(time_6_result2 - time_6_result1)))
#print("Result 3 {}".format(1/(time_6_result3 - time_6_result2)))
#print("Result 4 {}".format(1/(time_6_result4 - time_6_result3)))
print("Result 5 {}".format(1/(time_6_result5 - time_6_result4)))
print("Result 6 {}".format(1/(time_6_result6 - time_6_result5)))
print("Postprocess {}".format(1/(time_7_postprocess - time_6_result)))
print("Compose {}".format(1/(time_8_compose - time_7_postprocess)))
print("Show {}".format(1/(time_9_show - time_8_compose)))
print("Total {}".format(1/(time_9_show - time_0_start)))
print()
frameNo = frameNo + 1

if __name__ == "__main__":
while True:
Expand Down