Update manga-whisperer.py

manga-whisperer.py

@@ -1,35 +1,35 @@
-from transformers import AutoModel
-import numpy as np
-from PIL import Image
-import torch
-import os
-
-images = [
-    "1.png",
-    "1.jpg",
-]
-
-
-def read_image_as_np_array(image_path):
-    with open(image_path, "rb") as file:
-        image = Image.open(file).convert("L").convert("RGB")
-        image = np.array(image)
-    return image
-
-
-images = [read_image_as_np_array(image) for image in images]
-
-model = AutoModel.from_pretrained(
-    "ragavsachdeva/magi", trust_remote_code=True).cuda()
-# model = AutoModel.from_pretrained(
-#     "./magi", trust_remote_code=True).cuda()
-with torch.no_grad():
-    results = model.predict_detections_and_associations(images)
-    text_bboxes_for_all_images = [x["texts"] for x in results]
-    ocr_results = model.predict_ocr(images, text_bboxes_for_all_images)
-
-for i in range(len(images)):
-    model.visualise_single_image_prediction(
-        images[i], results[i], filename=f"image_{i}.png")
-    model.generate_transcript_for_single_image(
-        results[i], ocr_results[i], filename=f"transcript_{i}.txt")
+from transformers import AutoModel
+import numpy as np
+from PIL import Image
+import torch
+import os
+
+images = [
+    "test/1.png",
+    "test/2.png",
+]
+
+
+def read_image_as_np_array(image_path):
+    with open(image_path, "rb") as file:
+        image = Image.open(file).convert("L").convert("RGB")
+        image = np.array(image)
+    return image
+
+
+images = [read_image_as_np_array(image) for image in images]
+
+model = AutoModel.from_pretrained(
+    "ragavsachdeva/magi", trust_remote_code=True).cuda()
+# model = AutoModel.from_pretrained(
+#     "./magi", trust_remote_code=True).cuda()
+with torch.no_grad():
+    results = model.predict_detections_and_associations(images)
+    text_bboxes_for_all_images = [x["texts"] for x in results]
+    ocr_results = model.predict_ocr(images, text_bboxes_for_all_images)
+
+for i in range(len(images)):
+    model.visualise_single_image_prediction(
+        images[i], results[i], filename=f"image_{i}.png")
+    model.generate_transcript_for_single_image(
+        results[i], ocr_results[i], filename=f"transcript_{i}.txt")