something someething works now

M-Pre.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:2baad89d1c7a9a02ee3a639f9e5e932f874471ac8ca210cf96fef07593833521
+size 52099424

N-Pre.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:116d8ddbdd37eeba30a231947f3faaab597fdef0a8e4ade40c9bd3a5dd203ecd
+size 22557880

S-Pre.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:5df9846e215318de5e880efcaf7549bea622f583fc5693e340e2566204842edc
+size 22557944

app.py

@@ -2,7 +2,6 @@ import numpy as np
 from PIL import Image
 import gradio as gr
 from ultralytics import YOLO
-from ultralytics.yolo.utils.ops import scale_image
 import cv2
 
 # Load the YOLO model
@@ -10,7 +9,21 @@ m_raw_model = YOLO("M-Raw.pt")
 n_raw_model = YOLO("N-Raw.pt")
 s_raw_model = YOLO("S-Raw.pt")
 
-def snap(image, model, conf, iou):
+# Class to syllable map
+class_mapping = {0: 'Baybayin Character', 1: 'a', 2: 'b', 3: 'ba', 4: 'be', 5: 'bi', 6: 'bo', 7: 'bu', 8: 'd', 9: 'da', 10: 'di', 11: 'do', 12: 'du', 13: 'e', 14: 'g', 15: 'ga', 16: 'gi', 17: 'go', 18: 'gu', 19: 'ha', 20: 'he', 21: 'hi', 22: 'ho', 23: 'hu', 24: 'i', 25: 'k', 26: 'ka', 27: 'ki', 28: 'ko', 29: 'ku', 30: 'l', 31: 'la', 32: 'le', 33: 'li', 34: 'lo', 35: 'lu', 36: 'm', 37: 'ma', 38: 'me', 39: 'mi', 40: 'mo', 41: 'mu', 42: 'n', 43: 'na', 44: 'ng', 45: 'nga', 46: 'ngi', 47: 'ngo', 48: 'ngu', 49: 'ni', 50: 'no', 51: 'nu', 52: 'o', 53: 'p', 54: 'pa', 55: 'pe', 56: 'pi', 57: 'po', 58: 'pu', 59: 'r', 60: 'ra', 61: 're', 62: 'ri', 63: 'ro', 64: 'ru', 65: 's', 66: 'sa', 67: 'se', 68: 'si', 69: 'so', 70: 'su', 71: 't', 72: 'ta', 73: 'te', 74: 'ti', 75: 'to', 76: 'tu', 77: 'u', 78: 'w', 79: 'wa', 80: 'we', 81: 'wi', 82: 'y', 83: 'ya', 84: 'yi', 85: 'yo', 86: 'yu'}
+
+def snap(webcam, upload, model, conf, iou):
+
+    if webcam is not None:
+        image = webcam
+    elif upload is not None:
+        image = upload
+    else:
+        image = webcam
+
+    # If no model selected, use M-Raw
+    if model == None:
+        model = "M-Raw"
     
     # Run the selected model
     results = None
@@ -25,27 +38,24 @@ def snap(image, model, conf, iou):
     result = results[0]
 
     if result.boxes.cls.cpu().numpy().size == 0:
-        return [image]
-    classes = result.boxes.cls.cpu().numpy()[0]
-    probs = result.boxes.conf.cpu().numpy()[0]
+        print("No detections.")
+        return image
+    
+    
+    classes = result.boxes.cls.cpu().numpy()
+    probs = result.boxes.conf.cpu().numpy()
     boxes = result.boxes.xyxy.cpu().numpy()
 
-    print("-------------------")
     print(classes)
-    print("-------------------")
     print(probs)
-    print("-------------------")
     print(boxes)
 
-    print(image)
+    # print(f"Detected {classes} with {probs:.2f} confidence.")
     for i in range(len(boxes)):
         x1, y1, x2, y2 = boxes[i]
         x1, y1, x2, y2 = int(x1), int(y1), int(x2), int(y2)
         cv2.rectangle(image, (x1, y1), (x2, y2), (0, 255, 0), 2)
-        cv2.putText(image, f"{classes} {probs:.2f}", (x1, y1), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)
-
-    # Convert the resulting image to a PIL image
-    resulting_image = image
+        cv2.putText(image, f"{class_mapping[int(classes[i])]} {probs[i]:.2f}", (x1, y1), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)
 
     # Get the labels
     # labels = results.pandas().xyxy[0]["name"].values
@@ -53,15 +63,19 @@ def snap(image, model, conf, iou):
     # Sort the labels by their x-value first and then by their y-value
     # print(labels)
 
-    return [resulting_image]
+    return image
 
 
 demo = gr.Interface(
     snap,
-    [gr.Image(source="webcam", tool=None, streaming=True), gr.Radio(["M-Raw", "S-Raw", "N-Raw"]), gr.Slider(0, 1, value=0.6, label="Classifier Confidence Threshold"), gr.Slider(0, 1, value=0.7, label="IoU Threshold")],
-    ["image"],
+    [gr.Webcam(type="numpy", label="Webcam"), 
+     gr.Image(source="upload", type="numpy", label="Baybayin Image"),
+     gr.Radio(["M-Raw", "S-Raw", "N-Raw"]), 
+     gr.Slider(0, 1, value=0.6, label="Classifier Confidence Threshold"), 
+     gr.Slider(0, 1, value=0.7, label="IoU Threshold")],
+    [gr.Image(type="numpy", label="Detected Baybayin")],
     title="Baybayin Instance Detection"
-)
+).queue()
 
 if __name__ == "__main__":
     demo.launch()

app_test.py

@@ -0,0 +1,34 @@
+import gradio as gr
+import torch
+from PIL import Image
+import json
+
+m_raw_model = torch.hub.load('ultralytics/yolov8', 'custom', path='M-Raw.pt', source="local") 
+s_raw_model = torch.hub.load('ultralytics/yolov8', 'custom', path='S-Raw.pt', source="local") 
+n_raw_model = torch.hub.load('ultralytics/yolov8', 'custom', path='N-Raw.pt', source="local")
+m_pre_model = torch.hub.load('ultralytics/yolov8', 'custom', path='M-Pre.pt', source="local") 
+s_pre_model = torch.hub.load('ultralytics/yolov8', 'custom', path='S-Pre.pt', source="local") 
+n_pre_model = torch.hub.load('ultralytics/yolov8', 'custom', path='N-Pre.pt', source="local")
+
+def snap(image, model, conf, iou):
+    
+        # If no model selected, use M-Raw
+        if model == None:
+            model = "M-Raw"
+        
+        # Run the selected model
+        results = None
+        if model == "M-Raw":
+            results = m_raw_model(image, conf=conf, iou=iou)
+        elif model == "N-Raw":
+            results = n_raw_model(image, conf=conf, iou=iou)
+        elif model == "S-Raw":
+            results = s_raw_model(image, conf=conf, iou=iou)
+        elif model == "M-Pre":
+            results = m_pre_model(image, conf=conf, iou=iou)
+        elif model == "N-Pre":
+            results = n_pre_model(image, conf=conf, iou=iou)
+        elif model == "S-Pre":
+            results = s_pre_model(image, conf=conf, iou=iou)
+
+        

flagged/log.csv

@@ -0,0 +1,2 @@
+image,model,Classifier Confidence Threshold,IoU Threshold,output,flag,username,timestamp
+/Users/adrielamoguis/Documents/Academics/De La Salle University/Publications/Baybayin OCR - PCSC/Baybayin-Instance-Detection/flagged/image/tmpyu6iq1kf.png,N-Raw,0.6,0.7,,,,2023-03-23 19:23:28.934630