From 1f0ee10efefa6c06629e0e534fc974b2e2bc57cf Mon Sep 17 00:00:00 2001
From: dscripka <david.scripka@gmail.com>
Date: Sat, 28 Jan 2023 13:58:57 -0500
Subject: [PATCH] Adjusted some example scripts to fix bugs and removed
 'plotext' usage as it wasn't functioning right on low-power hardware

---
 examples/README.md                 |  2 +-
 examples/capture_activations.py    |  4 ++++
 examples/detect_from_microphone.py | 32 +++++++++++++++++++-----------
 3 files changed, 25 insertions(+), 13 deletions(-)

diff --git a/examples/README.md b/examples/README.md
index 0d6e168..e100c6f 100644
--- a/examples/README.md
+++ b/examples/README.md
@@ -6,7 +6,7 @@ Included are several example scripts demonstrating the usage of openWakeWord. So
 
 This is a simple example which allows you to test openWakeWord by using a locally connected microphone. To run the script, follow these steps:
 
-1) Install the example-specific requirements: `pip install plotext pyaudio`
+1) Install the example-specific requirements: `pip install pyaudio`
 
 2) Run the script: `python detect_from_microphone.py`.
 
diff --git a/examples/capture_activations.py b/examples/capture_activations.py
index f44855c..59f0339 100644
--- a/examples/capture_activations.py
+++ b/examples/capture_activations.py
@@ -88,6 +88,10 @@ save_delay = 1  # seconds
 # Set cooldown period before another clip can be saved
 cooldown = 4  # seconds
 
+# Create output directory if it does not already exist
+if not os.path.exists(args.output_dir):
+    os.mkdir(args.output_dir)
+
 # Run capture loop, checking for hotwords
 if __name__ == "__main__":
     # Predict continuously on audio stream
diff --git a/examples/detect_from_microphone.py b/examples/detect_from_microphone.py
index 740e063..7390bfb 100644
--- a/examples/detect_from_microphone.py
+++ b/examples/detect_from_microphone.py
@@ -13,7 +13,6 @@
 # limitations under the License.
 
 # Imports
-import plotext as plt
 import pyaudio
 import numpy as np
 from openwakeword.model import Model
@@ -32,6 +31,12 @@ owwModel = Model()
 # Run capture loop, checking for hotwords
 if __name__ == "__main__":
     # Predict continuously on audio stream
+    print("\n\n")
+    print("#"*100)
+    print("Listening for wakewords...")
+    print("#"*100)
+    print("\n"*13)
+    
     while True:
         # Get audio
         audio = np.frombuffer(mic_stream.read(CHUNK), dtype=np.int16)
@@ -39,18 +44,21 @@ if __name__ == "__main__":
         # Feed to openWakeWord model
         prediction = owwModel.predict(audio)
 
-        # Get predictions from prediction buffers and plot
-        plt.cld()
-        plt.clt()
+        # Generate output string header
+        n_spaces = 16
+        output_string_header = """
+            Model Name         | Score | Wakeword Status
+            --------------------------------------
+            """
+
         for mdl in owwModel.prediction_buffer.keys():
-            # Plot scores in graph
+            # Add scores in formatted table
             scores = list(owwModel.prediction_buffer[mdl])
-            plt.plot(scores)
+            curr_score = format(scores[-1], '.20f').replace("-", "")
 
-            # Plot text showing name of model with scores >= 0.5 (default threshold)
-            if max(scores) >= 0.5:
-                plt.text(mdl, 15, 0.9, alignment="center", color = "blue", style="bold")
+            output_string_header += f"""{mdl}{" "*(n_spaces - len(mdl))}   | {curr_score[0:5]} | {"--"+" "*20 if scores[-1] <= 0.5 else "Wakeword Detected!"}
+            """
 
-        plt.ylim(0,1)
-        plt.show()
-        plt.sleep(0.005)
+        # Print results table
+        print("\033[F"*14)
+        print(output_string_header, "                             ", end='\r')