build structure

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+*.xlsx filter=lfs diff=lfs merge=lfs -text

data.xlsx

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:721c0ebb1a67f5ef2b9117ca10f1a1d2f7b4581590d565bab4ef5eefe6ab9e08
+size 93002

model/pepsodent.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:2b719cd41b7a50fa54a3f98f4a52dcac1f93745b7868e9b82d52a61f8cad9724
+size 195243089

sys.py

@@ -0,0 +1,56 @@
+import os
+import pandas as pd
+from utils.main import main
+import asyncio
+
+data = pd.read_excel('data.xlsx')
+data["Pepsodent Sensitive Expert"]=pd.NA
+data["Pepsodent Germicheck"]=pd.NA
+data["Shelf Talker"]=pd.NA
+
+
+outputData = "aiGen.xlsx"
+data["Pepsodent AI Qty."] = pd.NA
+
+def is_valid_file(file_path):
+    return os.path.isfile(file_path)
+
+async def det(data):
+    for index, row in data.iterrows():
+        outlet = row["Outlet code"]
+        img_path_underscore = f"img/_{row['Image No.']}"
+        img_path_no_underscore = f"img/{row['Image No.']}"
+
+        try:
+            if is_valid_file(img_path_underscore):
+                img = img_path_underscore
+            elif is_valid_file(img_path_no_underscore):
+                img = img_path_no_underscore
+            else:
+                continue  # Skip to the next image if both paths are invalid
+
+            print(img)
+            result = await main(img)
+            for name,count in result.items():
+                data.at[index,"Shelf Talker"]="No"
+                if name=="Pepsodent Germicheck":
+                    data.at[index, "Pepsodent Germicheck"] = result["Pepsodent Germicheck"]
+
+                elif name=="Pepsodent Sensitive Expert":
+                    data.at[index, "Pepsodent Sensitive Expert"] = result["Pepsodent Sensitive Expert"]
+                
+                elif name=="Pepsodent Shelf Talker":
+                    data.at[index,"Shelf Talker"]="Yes"
+            print(result)
+
+        except Exception as e:
+            print(f"Error processing image {row['Image No.']}: {e}")
+            continue  # Continue to the next image if an error occurs
+
+async def main_async():
+    await det(data)
+    data.to_excel(outputData, index=False)
+
+# Entry point for the script
+if __name__ == "__main__":
+    asyncio.run(main_async())

utils/data.py

@@ -0,0 +1,8 @@
+pepsodent_qpds_data = {
+                        "pepsodent_germicheck":"Pepsodent Germicheck",
+                        "pepsodent_sensitive_expert":"Pepsodent Sensitive Expert",
+                        "pepsodent_shelf_talker":"Pepsodent Shelf Talker",
+                        }
+
+
+# pepsodent_qpds_data = {"PEPSODENT":"Pepsodent"}

utils/main.py

@@ -0,0 +1,55 @@
+import json
+import pandas as pd
+from PIL import Image
+from .model import model
+from .data import pepsodent_qpds_data
+
+
+
+
+
+
+async def detection(model, img_content, confidence):
+    try:
+        img = Image.open(img_content)
+        result = model(source=img, device=0, conf=confidence)
+        detection = {}
+        data = json.loads(result[0].tojson())
+        if not result or not data:
+            return {}
+        else:
+            name_counts = {}
+            for item in data:
+                name = item['name']
+                if name in name_counts:
+                    name_counts[name] += 1
+                else:
+                    name_counts[name] = 1
+            detection.update(name_counts)
+
+        return detection
+    except Exception as e:
+        return {}
+
+
+async def format_result(convertData,resData):
+    try:
+        result = {}
+        for aiName,sysName in convertData.items():
+            if aiName in resData:
+                result.update({sysName:resData[aiName]})
+        return result
+    except Exception as e:
+        pass
+
+
+
+
+async def main(img):
+    try:
+        result = await detection(model,img,0.3)
+        formattedResult = await format_result(pepsodent_qpds_data,result)
+
+        return formattedResult
+    except Exception as e:
+        pass

utils/model.py

@@ -0,0 +1,5 @@
+from ultralytics import YOLO
+
+model_init= YOLO('model/pepsodent.pt').cuda()
+
+model = model_init.to(device=0)