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README.md

@@ -1,12 +1,13 @@
 ---
-title: Image Object
+title: Specific Object Recognition In The Wild
 emoji: ⚡
-colorFrom: purple
+colorFrom: red
 colorTo: pink
 sdk: gradio
-sdk_version: 4.31.5
+sdk_version: 3.42.0
 app_file: app.py
 pinned: false
+license: openrail
 ---
 
 Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+import gradio as gr
+import torch
+import numpy as np
+from transformers import OwlViTProcessor, OwlViTForObjectDetection
+from PIL import Image, ImageDraw
+import cv2
+import torch.nn.functional as F
+import tempfile
+import matplotlib.pyplot as plt
+import matplotlib.cm as cm
+from io import BytesIO
+from SuperGluePretrainedNetwork.models.matching import Matching
+from SuperGluePretrainedNetwork.models.utils import read_image
+
+# Set device
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+# Load models
+model = OwlViTForObjectDetection.from_pretrained("google/owlvit-base-patch32").to(device)
+processor = OwlViTProcessor.from_pretrained("google/owlvit-base-patch32")
+
+matching = Matching({
+    'superpoint': {'nms_radius': 4, 'keypoint_threshold': 0.005, 'max_keypoints': 1024},
+    'superglue': {'weights': 'outdoor', 'sinkhorn_iterations': 20, 'match_threshold': 0.2}
+}).eval().to(device)
+
+# Utility functions
+def save_array_to_temp_image(arr):
+    rgb_arr = cv2.cvtColor(arr, cv2.COLOR_BGR2RGB)
+    img = Image.fromarray(rgb_arr)
+    temp_file = tempfile.NamedTemporaryFile(delete=False, suffix='.png')
+    temp_file_name = temp_file.name
+    temp_file.close()
+    img.save(temp_file_name)
+    return temp_file_name
+
+def unified_matching_plot2(image0, image1, kpts0, kpts1, mkpts0, mkpts1, color, text, path=None, show_keypoints=False, fast_viz=False, opencv_display=False, opencv_title='matches', small_text=[]):
+    height = min(image0.shape[0], image1.shape[0])
+    image0_resized = cv2.resize(image0, (int(image0.shape[1] * height / image0.shape[0]), height))
+    image1_resized = cv2.resize(image1, (int(image1.shape[1] * height / image1.shape[0]), height))
+
+    plt.figure(figsize=(15, 15))
+    plt.subplot(1, 2, 1)
+    plt.imshow(image0_resized)
+    plt.scatter(kpts0[:, 0], kpts0[:, 1], color='r', s=1)
+    plt.axis('off')
+
+    plt.subplot(1, 2, 2)
+    plt.imshow(image1_resized)
+    plt.scatter(kpts1[:, 0], kpts1[:, 1], color='r', s=1)
+    plt.axis('off')
+
+    fig, ax = plt.subplots(figsize=(20, 20))
+    plt.plot([mkpts0[:, 0], mkpts1[:, 0] + image0_resized.shape[1]], [mkpts0[:, 1], mkpts1[:, 1]], 'r', lw=0.5)
+    plt.scatter(mkpts0[:, 0], mkpts0[:, 1], s=2, marker='o', color='b')
+    plt.scatter(mkpts1[:, 0] + image0_resized.shape[1], mkpts1[:, 1], s=2, marker='o', color='g')
+    plt.imshow(np.hstack([image0_resized, image1_resized]), aspect='auto')
+
+    plt.suptitle('\n'.join(text), fontsize=20, fontweight='bold')
+    plt.tight_layout()
+    plt.show()
+
+    buf = BytesIO()
+    plt.savefig(buf, format='png')
+    buf.seek(0)
+    img_arr = np.frombuffer(buf.getvalue(), dtype=np.uint8)
+    buf.close()
+    img = cv2.imdecode(img_arr, 1)
+    img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
+    plt.close(fig)
+
+    return img
+
+def stitch_images(images):
+    """Stitches a list of images vertically."""
+    if not images:
+        return Image.new('RGB', (100, 100), color='gray')
+
+    max_width = max([img.width for img in images])
+    total_height = sum(img.height for img in images)
+
+    composite = Image.new('RGB', (max_width, total_height))
+
+    y_offset = 0
+    for img in images:
+        composite.paste(img, (0, y_offset))
+        y_offset += img.height
+
+    return composite
+
+# Main functions
+def detect_and_crop(target_image, query_image, threshold=0.5, nms_threshold=0.3):
+    target_sizes = torch.Tensor([target_image.size[::-1]])
+    inputs = processor(images=target_image, query_images=query_image, return_tensors="pt").to(device)
+    with torch.no_grad():
+        outputs = model.image_guided_detection(**inputs)
+
+    img = cv2.cvtColor(np.array(target_image), cv2.COLOR_BGR2RGB)
+    outputs.logits = outputs.logits.cpu()
+    outputs.target_pred_boxes = outputs.target_pred_boxes.cpu()
+
+    results = processor.post_process_image_guided_detection(outputs=outputs, threshold=threshold, nms_threshold=nms_threshold, target_sizes=target_sizes)
+    boxes, scores = results[0]["boxes"], results[0]["scores"]
+
+    if len(boxes) == 0:
+        return [], None
+
+    filtered_boxes = []
+    for box in boxes:
+        x1, y1, x2, y2 = [int(i) for i in box.tolist()]
+        cropped_img = img[y1:y2, x1:x2]
+        if cropped_img.size != 0:
+            filtered_boxes.append(cropped_img)
+
+    draw = ImageDraw.Draw(target_image)
+    for box in boxes:
+        draw.rectangle(box.tolist(), outline="red", width=3)
+
+    return filtered_boxes, target_image
+
+def image_matching_no_pyramid(query_img, target_img, visualize=True):
+    temp_query = save_array_to_temp_image(np.array(query_img))
+    temp_target = save_array_to_temp_image(np.array(target_img))
+    
+    image1, inp1, scales1 = read_image(temp_target, device, [640*2], 0, True)
+    image0, inp0, scales0 = read_image(temp_query, device, [640*2], 0, True)
+
+    if image0 is None or image1 is None:
+        return None
+
+    pred = matching({'image0': inp0, 'image1': inp1})
+    pred = {k: v[0] for k, v in pred.items()}
+    kpts0, kpts1 = pred['keypoints0'], pred['keypoints1']
+    matches, conf = pred['matches0'], pred['matching_scores0']
+
+    valid = matches > -1
+    mkpts0 = kpts0[valid]
+    mkpts1 = kpts1[matches[valid]]
+    mconf = conf[valid]
+    color = cm.jet(mconf.detach().cpu().numpy())[:len(mkpts0)]
+
+    valid_count = np.sum(valid.tolist())
+
+    mkpts0_np = mkpts0.cpu().numpy()
+    mkpts1_np = mkpts1.cpu().numpy()
+
+    try:
+        H, inliers = cv2.findHomography(mkpts0_np, mkpts1_np, cv2.RANSAC, 5.0)
+    except:
+        inliers = 0
+
+    num_inliers = np.sum(inliers)
+
+    if visualize:
+        visualized_img = unified_matching_plot2(
+            image0, image1, kpts0, kpts1, mkpts0, mkpts1, color, ['Matches'], True, False, True, 'Matches', [])
+    else:
+        visualized_img = None
+
+    return {
+        'valid': [valid_count],
+        'inliers': [num_inliers],
+        'visualized_image': [visualized_img]
+    }
+
+def check_object_in_image(query_image, target_image, threshold=50, scale_factor=[0.33, 0.66, 1]):
+    images_to_return = []
+    cropped_images, bbox_image = detect_and_crop(target_image, query_image)
+
+    temp_files = [save_array_to_temp_image(i) for i in cropped_images]
+    crop_results = [image_matching_no_pyramid(query_image, Image.open(i), visualize=True) for i in temp_files]
+
+    cropped_visuals = []
+    cropped_inliers = []
+    for result in crop_results:
+        if result:
+            for img in result['visualized_image']:
+                cropped_visuals.append(Image.fromarray(img))
+            for inliers_ in result['inliers']:
+                cropped_inliers.append(inliers_)
+
+    images_to_return.append(stitch_images(cropped_visuals))
+
+    is_present = any(value >= threshold for value in cropped_inliers)
+
+    return {
+        'is_present': is_present,
+        'image_with_boxes': bbox_image,
+        'object_detection_inliers': [int(i) for i in cropped_inliers],
+    }
+
+def interface(poster_source, media_source, threshold, scale_factor):
+    result1 = check_object_in_image(poster_source, media_source, threshold, scale_factor)
+    if result1['is_present']:
+        return result1['is_present'], result1['image_with_boxes']
+
+    result2 = check_object_in_image(poster_source, media_source, threshold, scale_factor)
+    return result2['is_present'], result2['image_with_boxes']
+
+iface = gr.Interface(
+    fn=interface,
+    inputs=[
+        gr.Image(type="pil", label="Upload a Query Image (Poster)"),
+        gr.Image(type="pil", label="Upload a Target Image (Media)"),
+        gr.Slider(minimum=0, maximum=100, step=1, value=50, label="Threshold"),
+        gr.CheckboxGroup(choices=["0.33", "0.66", "1.0"], value=["0.33", "0.66", "1.0"], label="Scale Factors"),
+    ],
+    outputs=[
+        gr.Label(label="Object Presence"),
+        gr.Image(type="pil", label="Detected Bounding Boxes"),
+    ],
+    title="Object Detection in Images",
+    description="""
+    This application allows you to check if an object in a query image (poster) is present in a target image (media).
+    Steps:
+    1. Upload a Query Image (Poster)
+    2. Upload a Target Image (Media)
+    3. Set Threshold
+    4. Set Scale Factors
+    5. View Results
+    """
+)
+
+if __name__ == "__main__":
+    iface.launch()

gitattributes

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requirements.txt

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+gradio
+transformers
+torch
+torchvision
+numpy
+Pillow
+opencv-python
+matplotlib