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Open Source Computer Vision Library
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100 lines
3.5 KiB
100 lines
3.5 KiB
4 years ago
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#!/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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