Initial Code Commit

2025-10-24 02:07:59 -04:00
commit f099f36838
63 changed files with 4425 additions and 0 deletions
--- a/Training/main.py
+++ b/Training/main.py
@@ -0,0 +1,96 @@
+import dask.dataframe as dd
+
+from azure.cognitiveservices.vision.customvision.training import CustomVisionTrainingClient
+from azure.cognitiveservices.vision.customvision.prediction import CustomVisionPredictionClient
+from azure.cognitiveservices.vision.customvision.training.models import ImageFileCreateBatch, ImageFileCreateEntry, Region
+from msrest.authentication import ApiKeyCredentials
+import os, time, uuid
+
+ENDPOINT = "https://trashvision.cognitiveservices.azure.com/"
+training_key = "611e786a785648e38f346f18e7f7e7ed"
+prediction_key = "611e786a785648e38f346f18e7f7e7ed"
+project_id = "a67f7d7b-c980-49bd-b57d-0bd1367b29d0"
+
+credentials = ApiKeyCredentials(in_headers={"Training-key": training_key})
+trainer = CustomVisionTrainingClient(ENDPOINT, credentials)
+prediction_credentials = ApiKeyCredentials(in_headers={"Prediction-key": prediction_key})
+predictor = CustomVisionPredictionClient(ENDPOINT, prediction_credentials)
+
+df = dd.read_parquet("hf://datasets/edwinpalegre/trashnet_enhanced/data/train-*.parquet")
+
+# ## iterate over the the first 5 rows of the dataframe and decode the image bytes to an image and save it to a file
+
+tags = trainer.get_tags(project_id)
+
+biodegradable_tag = None
+cardboard_tag = None
+glass_tag = None
+metal_tag = None
+paper_tag = None
+plastic_tag = None
+
+for tag in tags:
+    if tag.name == "biodegradable":
+        biodegradable_tag = tag
+    elif tag.name == "cardboard":
+        cardboard_tag = tag
+    elif tag.name == "glass":
+        glass_tag = tag
+    elif tag.name == "metal":
+        metal_tag = tag
+    elif tag.name == "paper":
+        paper_tag = tag
+    elif tag.name == "plastic":
+        plastic_tag = tag
+        
+print(biodegradable_tag)
+print(cardboard_tag)
+print(glass_tag)
+print(metal_tag)
+print(paper_tag)
+print(plastic_tag)
+
+# get all images from in the current dir and upload them to the custom vision project
+
+# base_image_location = os.path.join (os.path.dirname(__file__), "images")
+
+# tagged_images_with_regions = []
+
+# for image in os.listdir(base_image_location):
+#     print(image)
+#     with open(os.path.join(base_image_location, image), "rb") as image_contents:
+#         trainer.create_images_from_data(project_id, image_contents.read(), [biodegradable_tag.id])
+#         print("Uploaded image: ", image)
+#         time.sleep(5)
+   
+skip = 10031
+count = 0
+    
+for index, row in df.iterrows():
+    
+    if count < skip:
+        count += 1
+        continue
+    else: 
+        count += 1
+    
+    image = row["image"]["bytes"]
+    label = row["label"]
+    
+    if label == 0:
+        trainer.create_images_from_data(project_id, image, [biodegradable_tag.id])
+    elif label == 1:
+        trainer.create_images_from_data(project_id, image, [cardboard_tag.id])
+    elif label == 2:
+        trainer.create_images_from_data(project_id, image, [glass_tag.id])
+    elif label == 3:
+        trainer.create_images_from_data(project_id, image, [metal_tag.id])
+    elif label == 4:
+        trainer.create_images_from_data(project_id, image, [paper_tag.id])
+    elif label == 5:
+        trainer.create_images_from_data(project_id, image, [plastic_tag.id])
+    
+    print(f"C: {count}, I: {index}, L: {label}, Uploaded image")
+    time.sleep(1)
+    
+print("Done uploading images")