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Open Source Computer Vision Library
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99 lines
3.5 KiB
99 lines
3.5 KiB
#!/usr/bin/env python3 |
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import sys |
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import subprocess |
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import re |
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from enum import Enum |
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## Helper functions ################################################## |
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## |
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def fmt_bool(x): |
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return ("true" if x else "false") |
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def fmt_bin(base, prec, model): |
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return "%s/%s/%s/%s.xml" % (base, model, prec, model) |
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## The script itself ################################################# |
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## |
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if len(sys.argv) != 3: |
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print("Usage: %s /path/to/input/video /path/to/models" % sys.argv[0]) |
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exit(1) |
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input_file_path = sys.argv[1] |
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intel_models_path = sys.argv[2] |
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app = "bin/example_gapi_privacy_masking_camera" |
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intel_fd_model = "face-detection-retail-0005" |
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intel_lpd_model = "vehicle-license-plate-detection-barrier-0106" |
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output_file = "out_results.csv" |
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tgts = [ ("CPU", "INT8") |
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, ("CPU", "FP32") |
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, ("GPU", "FP16") |
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] |
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class Policy(Enum): |
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Traditional = 1 |
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Streaming = 2 |
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# From mode to cmd arg |
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mods = [ (Policy.Traditional, True) |
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, (Policy.Streaming, False) |
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] |
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class UI(Enum): |
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With = 1 |
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Without = 2 |
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# From mode to cmd arg |
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ui = [ (UI.With, False) |
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, (UI.Without, True) |
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] |
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fd_fmt_bin = lambda prec : fmt_bin(intel_models_path, prec, intel_fd_model) |
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lpd_fmt_bin = lambda prec : fmt_bin(intel_models_path, prec, intel_lpd_model) |
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# Performance comparison table |
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table={} |
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# Collect the performance data |
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for m in mods: # Execution mode (trad/stream) |
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for u in ui: # UI mode (on/off) |
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for f in tgts: # FD model |
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for p in tgts: # LPD model |
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cmd = [ app |
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, ("--input=%s" % input_file_path) # input file |
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, ("--faced=%s" % f[0]) # FD device target |
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, ("--facem=%s" % fd_fmt_bin(f[1])) # FD model @ precision |
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, ("--platd=%s" % p[0]) # LPD device target |
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, ("--platm=%s" % lpd_fmt_bin(p[1])) # LPD model @ precision |
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, ("--trad=%s" % fmt_bool(m[1])) # Execution policy |
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, ("--noshow=%s" % fmt_bool(u[1])) # UI mode (show/no show) |
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] |
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out = str(subprocess.check_output(cmd)) |
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match = re.search('Processed [0-9]+ frames \(([0-9]+\.[0-9]+) FPS\)', out) |
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fps = float(match.group(1)) |
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print(cmd, fps, "FPS") |
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table[m[0],u[0],f,p] = fps |
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# Write the performance summary |
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# Columns: all other components (mode, ui) |
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with open(output_file, 'w') as csv: |
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# CSV header |
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csv.write("FD,LPD,Serial(UI),Serial(no-UI),Streaming(UI),Streaming(no-UI),Effect(UI),Effect(no-UI)\n") |
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for f in tgts: # FD model |
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for p in tgts: # LPD model |
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row = "%s/%s,%s/%s" % (f[0], f[1], p[0], p[1]) # FD precision, LPD precision |
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row += ",%f" % table[Policy.Traditional,UI.With, f,p] # Serial/UI |
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row += ",%f" % table[Policy.Traditional,UI.Without,f,p] # Serial/no UI |
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row += ",%f" % table[Policy.Streaming, UI.With, f,p] # Streaming/UI |
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row += ",%f" % table[Policy.Streaming, UI.Without,f,p] # Streaming/no UI |
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effect_ui = table[Policy.Streaming,UI.With, f,p] / table[Policy.Traditional,UI.With, f,p] |
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effect_noui = table[Policy.Streaming,UI.Without,f,p] / table[Policy.Traditional,UI.Without,f,p] |
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row += ",%f,%f" % (effect_ui,effect_noui) |
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row += "\n" |
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csv.write(row) |
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print("DONE: ", output_file)
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