Update

app.py

@@ -1,10 +1,155 @@
 import gradio as gr
 
+import numpy as np
+import os
+import requests
+import torch
+import torchvision.transforms as T
+from PIL import Image
+from transformers import AutoProcessor, AutoModelForVision2Seq
+import cv2
+
+
+def is_overlapping(rect1, rect2):
+    x1, y1, x2, y2 = rect1
+    x3, y3, x4, y4 = rect2
+    return not (x2 < x3 or x1 > x4 or y2 < y3 or y1 > y4)
+
+
+def draw_entity_boxes_on_image(image, entities, show=False, save_path=None):
+    """_summary_
+    Args:
+        image (_type_): image or image path
+        collect_entity_location (_type_): _description_
+    """
+    if isinstance(image, Image.Image):
+        image_h = image.height
+        image_w = image.width
+        image = np.array(image)[:, :, [2, 1, 0]]
+    elif isinstance(image, str):
+        if os.path.exists(image):
+            pil_img = Image.open(image).convert("RGB")
+            image = np.array(pil_img)[:, :, [2, 1, 0]]
+            image_h = pil_img.height
+            image_w = pil_img.width
+        else:
+            raise ValueError(f"invaild image path, {image}")
+    elif isinstance(image, torch.Tensor):
+        # pdb.set_trace()
+        image_tensor = image.cpu()
+        reverse_norm_mean = torch.tensor([0.48145466, 0.4578275, 0.40821073])[:, None, None]
+        reverse_norm_std = torch.tensor([0.26862954, 0.26130258, 0.27577711])[:, None, None]
+        image_tensor = image_tensor * reverse_norm_std + reverse_norm_mean
+        pil_img = T.ToPILImage()(image_tensor)
+        image_h = pil_img.height
+        image_w = pil_img.width
+        image = np.array(pil_img)[:, :, [2, 1, 0]]
+    else:
+        raise ValueError(f"invaild image format, {type(image)} for {image}")
+
+    if len(entities) == 0:
+        return image
+
+    new_image = image.copy()
+    previous_bboxes = []
+    # size of text
+    text_size = 2
+    # thickness of text
+    text_line = 1  # int(max(1 * min(image_h, image_w) / 512, 1))
+    box_line = 3
+    (c_width, text_height), _ = cv2.getTextSize("F", cv2.FONT_HERSHEY_COMPLEX, text_size, text_line)
+    base_height = int(text_height * 0.675)
+    text_offset_original = text_height - base_height
+    text_spaces = 3
+
+    for entity_name, (start, end), bboxes in entities:
+        for (x1_norm, y1_norm, x2_norm, y2_norm) in bboxes:
+            orig_x1, orig_y1, orig_x2, orig_y2 = int(x1_norm * image_w), int(y1_norm * image_h), int(x2_norm * image_w), int(y2_norm * image_h)
+            # draw bbox
+            # random color
+            color = tuple(np.random.randint(0, 255, size=3).tolist())
+            new_image = cv2.rectangle(new_image, (orig_x1, orig_y1), (orig_x2, orig_y2), color, box_line)
+
+            l_o, r_o = box_line // 2 + box_line % 2, box_line // 2 + box_line % 2 + 1
+
+            x1 = orig_x1 - l_o
+            y1 = orig_y1 - l_o
+
+            if y1 < text_height + text_offset_original + 2 * text_spaces:
+                y1 = orig_y1 + r_o + text_height + text_offset_original + 2 * text_spaces
+                x1 = orig_x1 + r_o
+
+            # add text background
+            (text_width, text_height), _ = cv2.getTextSize(f"  {entity_name}", cv2.FONT_HERSHEY_COMPLEX, text_size, text_line)
+            text_bg_x1, text_bg_y1, text_bg_x2, text_bg_y2 = x1, y1 - (text_height + text_offset_original + 2 * text_spaces), x1 + text_width, y1
+
+            for prev_bbox in previous_bboxes:
+                while is_overlapping((text_bg_x1, text_bg_y1, text_bg_x2, text_bg_y2), prev_bbox):
+                    text_bg_y1 += (text_height + text_offset_original + 2 * text_spaces)
+                    text_bg_y2 += (text_height + text_offset_original + 2 * text_spaces)
+                    y1 += (text_height + text_offset_original + 2 * text_spaces)
+
+                    if text_bg_y2 >= image_h:
+                        text_bg_y1 = max(0, image_h - (text_height + text_offset_original + 2 * text_spaces))
+                        text_bg_y2 = image_h
+                        y1 = image_h
+                        break
+
+            alpha = 0.5
+            for i in range(text_bg_y1, text_bg_y2):
+                for j in range(text_bg_x1, text_bg_x2):
+                    if i < image_h and j < image_w:
+                        if j < text_bg_x1 + 1.35 * c_width:
+                            # original color
+                            bg_color = color
+                        else:
+                            # white
+                            bg_color = [255, 255, 255]
+                        new_image[i, j] = (alpha * new_image[i, j] + (1 - alpha) * np.array(bg_color)).astype(np.uint8)
+
+            cv2.putText(
+                new_image, f"  {entity_name}", (x1, y1 - text_offset_original - 1 * text_spaces), cv2.FONT_HERSHEY_COMPLEX, text_size, (0, 0, 0), text_line, cv2.LINE_AA
+            )
+            # previous_locations.append((x1, y1))
+            previous_bboxes.append((text_bg_x1, text_bg_y1, text_bg_x2, text_bg_y2))
+
+    pil_image = Image.fromarray(new_image[:, :, [2, 1, 0]])
+    if save_path:
+        pil_image.save(save_path)
+    if show:
+        pil_image.show()
+
+    return new_image
+
+
 def main():
 
+    ckpt = "ydshieh/kosmos-2-patch14-224"
+
+    model = AutoModelForVision2Seq.from_pretrained(ckpt, trust_remote_code=True)
+    processor = AutoProcessor.from_pretrained(ckpt, trust_remote_code=True)
+
     def generate_predictions(image_input, text_input, do_sample, sampling_topp, sampling_temperature):
 
-        return None, None
+        inputs = processor(text=text_input, images=image_input, return_tensors="pt")
+
+        generated_ids = model.generate(
+            pixel_values=inputs["pixel_values"],
+            input_ids=inputs["input_ids"][:, :-1],
+            attention_mask=inputs["attention_mask"][:, :-1],
+            img_features=None,
+            img_attn_mask=inputs["img_attn_mask"][:, :-1],
+            use_cache=True,
+            max_new_tokens=128,
+        )
+        generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
+
+        # By default, the generated  text is cleanup and the entities are extracted.
+        processed_text, entities = processor.post_processor_generation(generated_text)
+
+        annotated_image = draw_entity_boxes_on_image(image_input, entities, show=True)
+
+        return annotated_image, processed_text
 
     term_of_use = """
     ### Terms of use