Spaces:
Running
on
T4
Running
on
T4
resize
Browse files
app.py
CHANGED
@@ -59,29 +59,11 @@ athetics_params = {
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"text_offset_original": 8, # distance between text and box
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}
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def resize_image_by_height(image, new_height=500):
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import cv2
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height, width, _ = image.shape
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aspect_ratio = width / height
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new_width = int(new_height * aspect_ratio)
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resized_image = cv2.resize(image, (new_width, new_height))
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return resized_image
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def resize_image_by_width(image, new_width=500):
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import cv2
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height, width, _ = image.shape
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aspect_ratio = height / width
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new_height = int(new_width * aspect_ratio)
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resized_image = cv2.resize(image, (new_width, new_height))
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return resized_image
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def predict(image, text, ground_tokens=""):
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print(image.shape)
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max_len = max(image.shape)
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import math
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athetics_params["text_size"] = math.ceil(
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athetics_params["text_pixel"] = math.ceil(
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ground_tokens = None if ground_tokens.strip() == "" else ground_tokens.strip().split(";")
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result, _ = glip_demo.run_on_web_image(deepcopy(image[:, :, [2, 1, 0]]), text, 0.5, ground_tokens, **athetics_params)
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fiber_result, _ = fiber_demo.run_on_web_image(deepcopy(image[:, :, [2, 1, 0]]), text, 0.5, ground_tokens, **athetics_params)
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"text_offset_original": 8, # distance between text and box
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}
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def predict(image, text, ground_tokens=""):
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img_len = min(image.shape[:2])
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import math
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athetics_params["text_size"] = math.ceil(img_len/1000)
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athetics_params["text_pixel"] = math.ceil(img_len/1000*3)
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ground_tokens = None if ground_tokens.strip() == "" else ground_tokens.strip().split(";")
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result, _ = glip_demo.run_on_web_image(deepcopy(image[:, :, [2, 1, 0]]), text, 0.5, ground_tokens, **athetics_params)
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fiber_result, _ = fiber_demo.run_on_web_image(deepcopy(image[:, :, [2, 1, 0]]), text, 0.5, ground_tokens, **athetics_params)
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