Update sroie_inference.py

sroie_inference.py

@@ -1,114 +1,114 @@
-import torch
-import cv2
-import numpy as np
-from PIL import Image, ImageDraw, ImageFont
-from transformers import LayoutLMv3TokenizerFast, LayoutLMv3Processor, LayoutLMv3ForTokenClassification
-from utils import OCR, unnormalize_box
-
-
-labels = ["O", "B-COMPANY", "I-COMPANY", "B-DATE", "I-DATE", "B-ADDRESS", "I-ADDRESS", "B-TOTAL", "I-TOTAL"]
-id2label = {v: k for v, k in enumerate(labels)}
-label2id = {k: v for v, k in enumerate(labels)}
-
-tokenizer = LayoutLMv3TokenizerFast.from_pretrained("Theivaprakasham/layoutlmv3-finetuned-sroie", apply_ocr=False)
-processor = LayoutLMv3Processor.from_pretrained("Theivaprakasham/layoutlmv3-finetuned-sroie", apply_ocr=False)
-model = LayoutLMv3ForTokenClassification.from_pretrained("Theivaprakasham/layoutlmv3-finetuned-sroie")
-
-device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
-model.to(device)
-
-
-def blur(image, boxes):
-    image = np.array(image)
-    for box in boxes:
-
-        blur_x = int(box[0])
-        blur_y = int(box[1])
-        blur_width = int(box[2]-box[0])
-        blur_height = int(box[3]-box[1])
-
-        roi = image[blur_y:blur_y + blur_height, blur_x:blur_x + blur_width]
-        blur_image = cv2.GaussianBlur(roi, (201, 201), 0)
-        image[blur_y:blur_y + blur_height, blur_x:blur_x + blur_width] = blur_image
-
-    return Image.fromarray(image, 'RGB')
-
-
-def prediction(image):
-    boxes, words = OCR(image)
-    encoding = processor(image, words, boxes=boxes, return_offsets_mapping=True, return_tensors="pt", truncation=True)
-    offset_mapping = encoding.pop('offset_mapping')
-
-    for k, v in encoding.items():
-        encoding[k] = v.to(device)
-
-    outputs = model(**encoding)
-
-    predictions = outputs.logits.argmax(-1).squeeze().tolist()
-    token_boxes = encoding.bbox.squeeze().tolist()
-
-    inp_ids = encoding.input_ids.squeeze().tolist()
-    inp_words = [tokenizer.decode(i) for i in inp_ids]
-
-    width, height = image.size
-    is_subword = np.array(offset_mapping.squeeze().tolist())[:, 0] != 0
-
-    true_predictions = [id2label[pred] for idx, pred in enumerate(predictions) if not is_subword[idx]]
-    true_boxes = [unnormalize_box(box, width, height) for idx, box in enumerate(token_boxes) if not is_subword[idx]]
-    true_words = []
-
-    for id, i in enumerate(inp_words):
-        if not is_subword[id]:
-            true_words.append(i)
-        else:
-            true_words[-1] = true_words[-1]+i
-
-    true_predictions = true_predictions[1:-1]
-    true_boxes = true_boxes[1:-1]
-    true_words = true_words[1:-1]
-
-    preds = []
-    l_words = []
-    bboxes = []
-
-    for i, j in enumerate(true_predictions):
-        if j != 'others':
-            preds.append(true_predictions[i])
-            l_words.append(true_words[i])
-            bboxes.append(true_boxes[i])
-
-    d = {}
-    for id, i in enumerate(preds):
-        if i not in d.keys():
-            d[i] = l_words[id]
-        else:
-            d[i] = d[i] + ", " + l_words[id]
-
-    d = {k: v.strip() for (k, v) in d.items()}
-
-    keys_to_pop = []
-    for k, v in d.items():
-        if k[:2] == "I-":
-            d["B-" + k[2:]] = d["B-" + k[2:]] + ", " + v
-            keys_to_pop.append(k)
-
-    if "O" in d: d.pop("O")
-    if "B-TOTAL" in d: d.pop("B-TOTAL")
-    for k in keys_to_pop: d.pop(k)
-
-    blur_boxes = []
-    for prediction, box in zip(preds, bboxes):
-        if prediction != 'O' and prediction[2:] != 'TOTAL':
-            blur_boxes.append(box)
-
-    image = (blur(image, blur_boxes))
-
-    draw = ImageDraw.Draw(image, "RGBA")
-    font = ImageFont.load_default()
-
-    for prediction, box in zip(preds, bboxes):
-        draw.rectangle(box)
-        draw.text((box[0]+10, box[1]-10), text=prediction, font=font, fill="black", font_size="8")
-
-    return d, image
-
+import torch
+import cv2
+import numpy as np
+from PIL import Image, ImageDraw, ImageFont
+from transformers import LayoutLMv3TokenizerFast, LayoutLMv3Processor, LayoutLMv3ForTokenClassification
+from utils import OCR, unnormalize_box
+
+
+labels = ["O", "B-COMPANY", "I-COMPANY", "B-DATE", "I-DATE", "B-ADDRESS", "I-ADDRESS", "B-TOTAL", "I-TOTAL"]
+id2label = {v: k for v, k in enumerate(labels)}
+label2id = {k: v for v, k in enumerate(labels)}
+
+tokenizer = LayoutLMv3TokenizerFast.from_pretrained("Theivaprakasham/layoutlmv3-finetuned-sroie", apply_ocr=False)
+processor = LayoutLMv3Processor.from_pretrained("Theivaprakasham/layoutlmv3-finetuned-sroie", apply_ocr=False)
+model = LayoutLMv3ForTokenClassification.from_pretrained("Theivaprakasham/layoutlmv3-finetuned-sroie")
+
+device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+model.to(device)
+
+
+def blur(image, boxes):
+    image = np.array(image)
+    for box in boxes:
+
+        blur_x = int(box[0])
+        blur_y = int(box[1])
+        blur_width = int(box[2]-box[0])
+        blur_height = int(box[3]-box[1])
+
+        roi = image[blur_y:blur_y + blur_height, blur_x:blur_x + blur_width]
+        blur_image = cv2.GaussianBlur(roi, (201, 201), 0)
+        image[blur_y:blur_y + blur_height, blur_x:blur_x + blur_width] = blur_image
+
+    return Image.fromarray(image, 'RGB')
+
+
+def prediction(image):
+    boxes, words = OCR(image)
+    encoding = processor(image, words, boxes=boxes, return_offsets_mapping=True, return_tensors="pt", truncation=True)
+    offset_mapping = encoding.pop('offset_mapping')
+
+    for k, v in encoding.items():
+        encoding[k] = v.to(device)
+
+    outputs = model(**encoding)
+
+    predictions = outputs.logits.argmax(-1).squeeze().tolist()
+    token_boxes = encoding.bbox.squeeze().tolist()
+
+    inp_ids = encoding.input_ids.squeeze().tolist()
+    inp_words = [tokenizer.decode(i) for i in inp_ids]
+
+    width, height = image.size
+    is_subword = np.array(offset_mapping.squeeze().tolist())[:, 0] != 0
+
+    true_predictions = [id2label[pred] for idx, pred in enumerate(predictions) if not is_subword[idx]]
+    true_boxes = [unnormalize_box(box, width, height) for idx, box in enumerate(token_boxes) if not is_subword[idx]]
+    true_words = []
+
+    for id, i in enumerate(inp_words):
+        if not is_subword[id]:
+            true_words.append(i)
+        else:
+            true_words[-1] = true_words[-1]+i
+
+    true_predictions = true_predictions[1:-1]
+    true_boxes = true_boxes[1:-1]
+    true_words = true_words[1:-1]
+
+    preds = []
+    l_words = []
+    bboxes = []
+
+    for i, j in enumerate(true_predictions):
+        if j != 'others':
+            preds.append(true_predictions[i])
+            l_words.append(true_words[i])
+            bboxes.append(true_boxes[i])
+
+    d = {}
+    for id, i in enumerate(preds):
+        if i not in d.keys():
+            d[i] = l_words[id]
+        else:
+            d[i] = d[i] + ", " + l_words[id]
+
+    d = {k: v.strip() for (k, v) in d.items()}
+
+    keys_to_pop = []
+    for k, v in d.items():
+        if k[:2] == "I-":
+            d["B-" + k[2:]] = d["B-" + k[2:]] + ", " + v
+            keys_to_pop.append(k)
+
+    if "O" in d: d.pop("O")
+    if "B-TOTAL" in d: d.pop("B-TOTAL")
+    for k in keys_to_pop: d.pop(k)
+
+    blur_boxes = []
+    for prediction, box in zip(preds, bboxes):
+        if prediction != 'O' and prediction[2:] != 'TOTAL':
+            blur_boxes.append(box)
+
+    image = (blur(image, blur_boxes))
+
+    #draw = ImageDraw.Draw(image, "RGBA")
+    #font = ImageFont.load_default()
+
+    #for prediction, box in zip(preds, bboxes):
+    #    draw.rectangle(box)
+    #    draw.text((box[0]+10, box[1]-10), text=prediction, font=font, fill="black", font_size="8")
+
+    return d, image
+