reformat with black; looks better
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parent
1122f5a96f
commit
0b9d50af47
11 changed files with 240 additions and 244 deletions
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@ -6,6 +6,7 @@ import numpy as np
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def inference(image_path):
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from transformers import Pix2StructProcessor, Pix2StructForConditionalGeneration
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image = Image.open(image_path)
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question = "How many living rooms are displayed on this floor plan?" # not sure if it even has an effect
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processor = Pix2StructProcessor.from_pretrained("google/deplot")
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@ -35,15 +36,17 @@ def calculate_model(image_path):
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def improve_img_for_ocr(img: Image):
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img2 = np.array(img.convert('L'))
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img2 = np.array(img.convert("L"))
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cv2.resize(img2, None, fx=1.2, fy=1.2, interpolation=cv2.INTER_CUBIC)
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thresh = cv2.adaptiveThreshold(img2, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C,
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cv2.THRESH_BINARY, 11, 2)
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thresh = cv2.adaptiveThreshold(
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img2, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY, 11, 2
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)
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return Image.fromarray(thresh)
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def calculate_ocr(image_path):
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import pytesseract
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img = Image.open(image_path)
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text = pytesseract.image_to_string(img)
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estimated_sqm = extract_total_sqm(text)
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@ -52,9 +55,7 @@ def calculate_ocr(image_path):
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text2 = pytesseract.image_to_string(improved_img)
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estimated_sqm2 = extract_total_sqm(text2)
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with open("recalculating.log", "a") as f:
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f.write(
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f"before: {estimated_sqm} after: {estimated_sqm2} - {image_path}\n"
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)
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f.write(f"before: {estimated_sqm} after: {estimated_sqm2} - {image_path}\n")
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return estimated_sqm2, text2
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return estimated_sqm, text
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@ -30,19 +30,13 @@ def transit_route(
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header = {
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"X-Goog-Api-Key": api_key,
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"Content-Type": "application/json",
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"X-Goog-FieldMask": # "routes.*",
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"routes.distanceMeters,routes.duration,routes.staticDuration,routes.legs.steps.distanceMeters,routes.legs.steps.staticDuration,routes.legs.steps.travelMode",
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"X-Goog-FieldMask": "routes.distanceMeters,routes.duration,routes.staticDuration,routes.legs.steps.distanceMeters,routes.legs.steps.staticDuration,routes.legs.steps.travelMode", # "routes.*",
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}
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body = {
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"origin": {
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# "address": origin_address
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"location": {
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"latLng": {
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"latitude": origin_lat,
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"longitude": origin_lon
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}
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}
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"location": {"latLng": {"latitude": origin_lat, "longitude": origin_lon}}
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},
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"destination": {
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"address": dest_address
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