Spaces:
Configuration error
Configuration error
File size: 2,770 Bytes
f33a600 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 |
import pandas as pd
from fuzzywuzzy import process
import json
########################
#this is the first part of the process
# the function gonna take result data : and return json data
def create_annotations_from_results(results):
# Initialize dictionaries to store encountered object names and their counts
encountered_names = {}
encountered_ids = set()
# Initialize a list to store the annotations
annotations = []
# Iterate over the predictions and create annotations
for result in results:
for box in result.boxes:
# Extracting bounding box coordinates and rounding them
cords = [round(coord) for coord in box.xyxy[0].tolist()]
# Extracting class ID and confidence score
name_object = result.names[box.cls[0].item()]
conf = round(box.conf[0].item(), 2)
# Extracting tracked ID
id_track = int(box.id)
# Skip if ID is encountered
if id_track in encountered_ids:
continue
else:
encountered_ids.add(id_track)
# Update encountered names and counts
if name_object in encountered_names:
count = encountered_names[name_object] + 1
encountered_names[name_object] = count
name_object_with_count = f"{name_object}_{count}"
else:
count = 1
encountered_names[name_object] = 1
name_object_with_count = name_object
# Calculate bounding box center coordinates
center_x = (cords[0] + cords[2]) / 2
center_y = (cords[1] + cords[3]) / 2
# Determine location relative to the image center
image_center_x = result.orig_shape[1] / 2
image_center_y = result.orig_shape[0] / 2
location_x = "left" if center_x < image_center_x else "right"
location_y = "above" if center_y < image_center_y else "below"
# Calculate dimensions of the bounding box
width = abs(cords[2] - cords[0])
height = abs(cords[3] - cords[1])
# Create annotation dictionary
annotation = {
"id_tracked": id_track,
"object_type_name": name_object_with_count,
"coordinates": cords,
"probability": conf,
"count": count,
"location": (location_x, location_y),
"dimension": (width, height)
}
# Append annotation to the list
annotations.append(annotation)
# Print annotation if needed for debugging
print(annotation)
return annotations
########################
|