Most likely final

app.py

@@ -3,11 +3,15 @@ from PIL import Image
 import gradio as gr
 from ultralytics import YOLO
 import cv2
+import os
 
 # Load the YOLO model
 m_raw_model = YOLO("M-Raw.pt")
 n_raw_model = YOLO("N-Raw.pt")
 s_raw_model = YOLO("S-Raw.pt")
+m_pre_model = YOLO("M-Pre.pt")
+n_pre_model = YOLO("N-Pre.pt")
+s_pre_model = YOLO("S-Pre.pt")
 
 # 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'}
@@ -20,12 +24,13 @@ pageTitle = "Baybayin Instance Detection"
 msgWarning = "This demo was created by Adriel Amoguis and Miguel Flores for PCSC 2023"
 desc = """
 <center>
-Take a picture of some Baybayin text and upload it in the Image Box on the left. You may choose which model to use just below that. You can also choose the confidence threshold and the IoU threshold using the sliders.
+Take a picture of some Baybayin text and upload it in the Image Box on the left. You can choose the confidence threshold and the IoU threshold using the sliders. <br />
+<img src="https://qph.cf2.quoracdn.net/main-qimg-a5ae0e74f5d0ef9980e7b1c59fdf2014-lq" width="60%">
 </center>
 """
 
 
-def snap(upload, model, conf, iou):
+def snap(upload, conf, iou, show_conf=False):
 
     # if webcam is not None:
     #     image = webcam
@@ -36,8 +41,8 @@ def snap(upload, model, conf, iou):
     image = upload
 
     # If no model selected, use M-Raw
-    if model == None:
-        model = "M-Raw"
+    # if model == None:
+    model = "M-Raw"
     
     # Run the selected model
     results = None
@@ -47,6 +52,12 @@ def snap(upload, model, conf, iou):
         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)
 
     # Convert the results list into an output image
     result = results[0]
@@ -60,39 +71,45 @@ def snap(upload, model, conf, iou):
     probs = result.boxes.conf.cpu().numpy()
     boxes = result.boxes.xyxy.cpu().numpy()
 
-    print(classes)
-    print(probs)
-    print(boxes)
+    # print(classes)
+    # print(probs)
+    # print(boxes)
 
     # print(f"Detected {classes} with {probs:.2f} confidence.")
+    words = []
     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"{class_mapping[int(classes[i])]} {probs[i]:.2f}", (x1, y1), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)
+        cv2.rectangle(image, (x1, y1), (x2, y2), (255, 0, 0), int(image.shape[1] * 0.001))
+        if show_conf: cv2.putText(image, f"{class_mapping[int(classes[i])]} {probs[i]:.2f}", (x1, y1), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 0, 0), int(image.shape[1] * 0.001))
+        else: cv2.putText(image, f"{class_mapping[int(classes[i])]}", (x1, y1), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 0, 0), int(image.shape[1] * 0.00075))
+        # words.append([(x1,y1), (x2,y2), class_mapping[int(classes[i])]])
 
-    # Get the labels
-    # labels = results.pandas().xyxy[0]["name"].values
-
-    # Sort the labels by their x-value first and then by their y-value
-    # print(labels)
+    # Sort the words by top to bottom, left to right
+    # words = sorted(words, key=lambda x: (x[0][1], x[0][0]))
+    # words = [word[2] for word in words]
 
     return image
-
+            
+# Get the samples array
+samples_dir = "sample_images"
+samples = os.listdir(samples_dir)
+samples = [os.path.join(samples_dir, sample) for sample in samples]
 
 demo = gr.Interface(
     snap,
     [
     # gr.Webcam(type="numpy", label="Webcam"), 
      gr.Image(source="upload", type="numpy", label="Baybayin Image"),
-     gr.Radio(["M-Raw", "S-Raw", "N-Raw"]), 
+    #  gr.Radio(["M-Raw", "S-Raw", "N-Raw", "M-Pre", "S-Pre", "N-Pre"], label="Model", value="M-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")],
+    #  gr.Checkbox(label="Show Confidence Scores", value=False),
+    [gr.Image(type="numpy", label="Detected Baybayin")], #gr.Textbox(lines=5, label="Detected Latin Syllabes")],
     title="Baybayin Instance Detection",
     article=msgWarning,
     description=desc
 ).queue()
 
 if __name__ == "__main__":
-    demo.launch()
+    demo.launch(share=True)

flagged/log.csv

@@ -1,2 +0,0 @@
-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