Update app.py

app.py

@@ -12,7 +12,6 @@ import matplotlib.patches as patches
 
 import random
 import numpy as np
-import cv2
 
 import subprocess
 subprocess.run('pip install flash-attn --no-build-isolation', env={'FLASH_ATTENTION_SKIP_CUDA_BUILD': "TRUE"}, shell=True)
@@ -22,7 +21,7 @@ model = AutoModelForCausalLM.from_pretrained(model_id, trust_remote_code=True).t
 processor = AutoProcessor.from_pretrained(model_id, trust_remote_code=True)
 
 
-DESCRIPTION = "# [Florence-2 Video Demo](https://huggingface.co/microsoft/Florence-2-large)"
+DESCRIPTION = "# [Florence-2 Demo](https://huggingface.co/microsoft/Florence-2-large)"
 
 colormap = ['blue','orange','green','purple','brown','pink','gray','olive','cyan','red',
             'lime','indigo','violet','aqua','magenta','coral','gold','tan','skyblue']
@@ -68,6 +67,7 @@ def plot_bbox(image, data):
     return fig
 
 def draw_polygons(image, prediction, fill_mask=False):
+
     draw = ImageDraw.Draw(image)
     scale = 1
     for polygons, label in zip(prediction['polygons'], prediction['labels']):
@@ -86,6 +86,15 @@ def draw_polygons(image, prediction, fill_mask=False):
             draw.text((_polygon[0] + 8, _polygon[1] + 2), label, fill=color)
     return image
 
+def convert_to_od_format(data):
+    bboxes = data.get('bboxes', [])
+    labels = data.get('bboxes_labels', [])
+    od_results = {
+        'bboxes': bboxes,
+        'labels': labels
+    }
+    return od_results
+
 def draw_ocr_bboxes(image, prediction):
     scale = 1
     draw = ImageDraw.Draw(image)
@@ -100,98 +109,118 @@ def draw_ocr_bboxes(image, prediction):
                   fill=color)
     return image
 
-def process_video(video_path, task_prompt, text_input=None):
-    video = cv2.VideoCapture(video_path)
-    fps = video.get(cv2.CAP_PROP_FPS)
-    width = int(video.get(cv2.CAP_PROP_FRAME_WIDTH))
-    height = int(video.get(cv2.CAP_PROP_FRAME_HEIGHT))
-
-    output_frames = []
-
-    while True:
-        ret, frame = video.read()
-        if not ret:
-            break
-
-        image = Image.fromarray(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))
-
-        if task_prompt == 'Caption':
-            task_prompt = '<CAPTION>'
-            result = run_example(task_prompt, image)
-            output_frames.append(cv2.cvtColor(np.array(image), cv2.COLOR_RGB2BGR))
-        elif task_prompt == 'Detailed Caption':
-            task_prompt = '<DETAILED_CAPTION>'
-            result = run_example(task_prompt, image)
-            output_frames.append(cv2.cvtColor(np.array(image), cv2.COLOR_RGB2BGR))
-        elif task_prompt == 'More Detailed Caption':
-            task_prompt = '<MORE_DETAILED_CAPTION>'
-            result = run_example(task_prompt, image)
-                        output_frames.append(cv2.cvtColor(np.array(image), cv2.COLOR_RGB2BGR))
-        elif task_prompt == 'Object Detection':
-            task_prompt = '<OD>'
-            results = run_example(task_prompt, image)
-            fig = plot_bbox(image, results['<OD>'])
-            output_frames.append(cv2.cvtColor(np.array(fig_to_pil(fig)), cv2.COLOR_RGB2BGR))
-        elif task_prompt == 'Referring Expression Segmentation':
-            task_prompt = '<REF_SEG>'
-            results = run_example(task_prompt, image, text_input)
-            annotated_image = draw_polygons(image.copy(), results['<REF_SEG>'])
-            output_frames.append(cv2.cvtColor(np.array(annotated_image), cv2.COLOR_RGB2BGR))
-        elif task_prompt == 'OCR':
-            task_prompt = '<OCR>'
-            results = run_example(task_prompt, image)
-            annotated_image = draw_ocr_bboxes(image.copy(), results['<OCR>'])
-            output_frames.append(cv2.cvtColor(np.array(annotated_image), cv2.COLOR_RGB2BGR))
-        else:
-            raise ValueError(f"Unsupported task prompt: {task_prompt}")
-
-    video.release()
-
-    output_path = 'output_video.mp4'
-    out = cv2.VideoWriter(output_path, cv2.VideoWriter_fourcc(*'mp4v'), fps, (width, height))
-    for frame in output_frames:
-        out.write(frame)
-    out.release()
-
-    return output_path
-
-task_prompts = ['Caption', 'Detailed Caption', 'More Detailed Caption', 'Object Detection', 'Referring Expression Segmentation', 'OCR']
-
-with gr.Blocks(css="style.css") as demo:
-    with gr.Group():
-        with gr.Row():
-            video_input = gr.Video(
-                label='Input Video',
-                format='mp4',
-                source='upload',
-                interactive=True
-            )
+def process_image(image, task_prompt, text_input=None):
+    image = Image.fromarray(image)  # Convert NumPy array to PIL Image
+    if task_prompt == 'Caption':
+        task_prompt = '<CAPTION>'
+        result = run_example(task_prompt, image)
+        return result, None
+    elif task_prompt == 'Detailed Caption':
+        task_prompt = '<DETAILED_CAPTION>'
+        result = run_example(task_prompt, image)
+        return result, None
+    elif task_prompt == 'More Detailed Caption':
+        task_prompt = '<MORE_DETAILED_CAPTION>'
+        result = run_example(task_prompt, image)
+        return result, None
+    elif task_prompt == 'Object Detection':
+        task_prompt = '<OD>'
+        results = run_example(task_prompt, image)
+        fig = plot_bbox(image, results['<OD>'])
+        return results, fig_to_pil(fig)
+    elif task_prompt == 'Dense Region Caption':
+        task_prompt = '<DENSE_REGION_CAPTION>'
+        results = run_example(task_prompt, image)
+        fig = plot_bbox(image, results['<DENSE_REGION_CAPTION>'])
+        return results, fig_to_pil(fig)
+    elif task_prompt == 'Region Proposal':
+        task_prompt = '<REGION_PROPOSAL>'
+        results = run_example(task_prompt, image)
+        fig = plot_bbox(image, results['<REGION_PROPOSAL>'])
+        return results, fig_to_pil(fig)
+    elif task_prompt == 'Caption to Phrase Grounding':
+        task_prompt = '<CAPTION_TO_PHRASE_GROUNDING>'
+        results = run_example(task_prompt, image, text_input)
+        fig = plot_bbox(image, results['<CAPTION_TO_PHRASE_GROUNDING>'])
+        return results, fig_to_pil(fig)
+    elif task_prompt == 'Referring Expression Segmentation':
+        task_prompt = '<REFERRING_EXPRESSION_SEGMENTATION>'
+        results = run_example(task_prompt, image, text_input)
+        output_image = copy.deepcopy(image)
+        output_image = draw_polygons(output_image, results['<REFERRING_EXPRESSION_SEGMENTATION>'], fill_mask=True)
+        return results, output_image
+    elif task_prompt == 'Region to Segmentation':
+        task_prompt = '<REGION_TO_SEGMENTATION>'
+        results = run_example(task_prompt, image, text_input)
+        output_image = copy.deepcopy(image)
+        output_image = draw_polygons(output_image, results['<REGION_TO_SEGMENTATION>'], fill_mask=True)
+        return results, output_image
+    elif task_prompt == 'Open Vocabulary Detection':
+        task_prompt = '<OPEN_VOCABULARY_DETECTION>'
+        results = run_example(task_prompt, image, text_input)
+        bbox_results = convert_to_od_format(results['<OPEN_VOCABULARY_DETECTION>'])
+        fig = plot_bbox(image, bbox_results)
+        return results, fig_to_pil(fig)
+    elif task_prompt == 'Region to Category':
+        task_prompt = '<REGION_TO_CATEGORY>'
+        results = run_example(task_prompt, image, text_input)
+        return results, None
+    elif task_prompt == 'Region to Description':
+        task_prompt = '<REGION_TO_DESCRIPTION>'
+        results = run_example(task_prompt, image, text_input)
+        return results, None
+    elif task_prompt == 'OCR':
+        task_prompt = '<OCR>'
+        result = run_example(task_prompt, image)
+        return result, None
+    elif task_prompt == 'OCR with Region':
+        task_prompt = '<OCR_WITH_REGION>'
+        results = run_example(task_prompt, image)
+        output_image = copy.deepcopy(image)
+        output_image = draw_ocr_bboxes(output_image, results['<OCR_WITH_REGION>'])
+        return results, output_image
+    else:
+        return "", None  # Return empty string and None for unknown task prompts
+
+css = """
+  #output {
+    height: 500px; 
+    overflow: auto; 
+    border: 1px solid #ccc; 
+  }
+"""
+
+with gr.Blocks(css=css) as demo:
+    gr.Markdown(DESCRIPTION)
+    with gr.Tab(label="Florence-2 Image Captioning"):
         with gr.Row():
-            select_task = gr.Dropdown(
-                label='Task Prompt',
-                choices=task_prompts,
-                value=task_prompts[0],
-                interactive=True
-            )
-            text_input = gr.Textbox(
-                label='Text Input (optional)',
-                visible=False
-            )
-            submit = gr.Button(
-                label='Process Video',
-                scale=1,
-                variant='primary'
-            )
-    video_output = gr.Video(
-        label='Florence-2 Video Demo',
-        format='mp4',
-        interactive=False
-    )
-
-    submit.click(
-        fn=process_video,
-        inputs=[video_input, select_task, text_input],
-        outputs=video_output,
-    )
-
-demo.queue().launch()
+            with gr.Column():
+                input_img = gr.Image(label="Input Picture")
+                task_prompt = gr.Dropdown(choices=[
+                    'Caption', 'Detailed Caption', 'More Detailed Caption', 'Object Detection',
+                    'Dense Region Caption', 'Region Proposal', 'Caption to Phrase Grounding',
+                    'Referring Expression Segmentation', 'Region to Segmentation',
+                    'Open Vocabulary Detection', 'Region to Category', 'Region to Description',
+                    'OCR', 'OCR with Region'
+                ], label="Task Prompt", value= 'Caption')
+                text_input = gr.Textbox(label="Text Input (optional)")
+                submit_btn = gr.Button(value="Submit")
+            with gr.Column():
+                output_text = gr.Textbox(label="Output Text")
+                output_img = gr.Image(label="Output Image")
+
+        gr.Examples(
+            examples=[
+                ["image1.jpg", 'Object Detection'],
+                ["image2.jpg", 'OCR with Region']
+            ],
+            inputs=[input_img, task_prompt],
+            outputs=[output_text, output_img],
+            fn=process_image,
+            cache_examples=True,
+            label='Try examples'
+        )
+
+        submit_btn.click(process_image, [input_img, task_prompt, text_input], [output_text, output_img])
+
+demo.launch(debug=True)