Update experiments.py

experiments.py

@@ -4,161 +4,6 @@ import phoenix_helpers
 import helpers 
 
 
-
-import os
-abspath = os.path.abspath('') ## String which contains absolute path to the script file
-os.chdir(abspath) ## Setting up working directory
-import copy
-
-from transformers import AutoProcessor, AutoModelForCausalLM
-from PIL import Image
-import requests
-import utils
-from PIL import Image, ImageDraw, ImageFont
-
-image = Image.open(r"C:\Users\thrin\Downloads\rohitha\floerence.png").convert("RGB")
-
-
-model_id = 'microsoft/Florence-2-large'
-model = AutoModelForCausalLM.from_pretrained(model_id, trust_remote_code=True).eval().cuda()
-processor = AutoProcessor.from_pretrained(model_id, trust_remote_code=True)
-
-utils.set_model_info(model, processor)
-
-image
-
-img_list = ["/content/images_ocr_image_3.jpg",
- "/content/images_ocr_image_2.jpg",
- "/content/images_ocr_image_1.jpg",
- "/content/images_ocr_image_4.png",
- "/content/pci complliance and Credit card Authorization forms Hand written - Copy.jpg",
- "/content/Charlemagne.png",
- "/content/Frederick II.png",
- "/content/Henry VIII.png",
- "/content/Louis XIV.png",
- "/content/William IV.png",
- "/content/pci complliance and Credit card Authorization forms Hand written - Copy.jpg"
-]
-img_list = [r"C:\Users\thrin\Downloads\rohitha\floerence.png",
-r"C:\Users\thrin\Downloads\rohitha\credit-card-auth-form-xout.jpg"]
-
-for item in img_list:
-    path = item
-    print("<================>")
-    image = Image.open(path)
-    image_rgb = Image.open(path).convert("RGB")
-    
-    tasks = [utils.TaskType.CAPTION,
-           utils.TaskType.DETAILED_CAPTION,
-           utils.TaskType.MORE_DETAILED_CAPTION,]
-    
-    for task in tasks:
-      results = utils.run_example(task, image_rgb)
-      print(f'{task.value}{results[task]}')
-    
-    task = utils.TaskType.OCR
-    results = utils.run_example(task, image_rgb)
-    print('Detected Text: ', results[task])
-
-
-task = utils.TaskType.OCR
-results = utils.run_example(task, image_rgb)
-print('Detected Text: ', results[task])
-
-
-task = utils.TaskType.OCR_WITH_REGION
-results = utils.run_example(task, image_rgb)
-
-# Boxes drawn directly to image, so copy to avoid adulterating image for later tasks
-image_copy = copy.deepcopy(image)
-utils.draw_ocr_bboxes(image_copy, results[task])
-
-
-
-task = utils.TaskType.OCR
-results = utils.run_example(task, image_rgb)
-print('Detected Text: ', results[task])
-
-task = utils.TaskType.OCR_WITH_REGION
-results = utils.run_example(task, image_rgb)
-print('Detected Text: ', results[task])
-
-counter = 0
-for item in img_list:
-    path = item
-    print("<======>",counter,"<======>")
-    image = Image.open(path)
-    image_rgb = Image.open(path).convert("RGB")
-    
-    tasks = [utils.TaskType.CAPTION,
-           utils.TaskType.DETAILED_CAPTION,
-           utils.TaskType.MORE_DETAILED_CAPTION,]
-    
-    for task in tasks:
-      results = utils.run_example(task, image_rgb)
-      print(counter,"<======>",f'{task.value}{results[task]}')
-    
-    task = utils.TaskType.OCR
-    results = utils.run_example(task, image_rgb)
-    print(counter,"<======>",'Detected Text: ', results[task])
-    
-    task = utils.TaskType.OCR_WITH_REGION
-    results = utils.run_example(task, image_rgb)
-    print(counter,"<======>",'Detected Text: ', results[task])
-    
-    
-results[task].items()
-
-results[task].keys()
-
-len(results[task]["quad_boxes"])
-len(results[task]["labels"])
-
-OCR = '<OCR>'
-""" OCR for entire image """
-OCR_WITH_REGION = '<OCR_WITH_REGION>'
-
-from PIL import ImageDraw, Image
-
-{'quad_boxes': [
-    [78.91199493408203,
-     249.8040008544922,
-     332.35198974609375,
-     251.7480010986328,
-     332.35198974609375,
-     299.70001220703125,
-     78.91199493408203,
-     297.7560119628906]
-    ],
-    'labels': ['</s>3702692432']}
-
-def draw_multiple_bounding_boxes(image, coords_and_labels):
-   draw = ImageDraw.Draw(image)
-   width, height = image.size
-   for obj in coords_and_labels:
-       # Extract the bounding box coordinates
-       y1, x1, y2, x2 = obj['bbox'][0] * height, obj['bbox'][1] * width, obj['bbox'][2] * height, obj['bbox'][3] * width
-       # Draw bounding box and label
-       draw.rectangle([x1, y1, x2, y2], outline="red", width=3)
-       draw.text((x1, y1), obj['label'], fill="red")
-   return image
-
-image  = r"C:\Users\thrin\Downloads\rohitha\floerence.png"
-image  = Image.open(image)
-
-quad_box =  [(78.91199493408203,249.8040008544922),
-             (332.35198974609375,251.7480010986328),
-             (332.35198974609375,299.70001220703125),
-             (78.91199493408203,297.7560119628906)]
-
-draw = ImageDraw.Draw(image)
-
-draw.polygon(quad_box,outline="green",width=2)
-image.show()
-image.save(r"C:\Users\thrin\Downloads\florence-2-master (1)\florence-2-master/florence_bounding.png")
-# 
-
-
 load_dotenv()
 models = helpers.fetch_models()