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| # File: processing.pyx | |
| # cython: language_level=3, boundscheck=False, wraparound=False, nonecheck=False, cdivision=True | |
| import numpy as np | |
| cimport numpy as np | |
| import pandas as pd | |
| cimport pandas as pd | |
| from libc.math cimport isnan | |
| from cpython.mem cimport PyMem_Malloc, PyMem_Free | |
| # Define C types for better performance | |
| ctypedef np.float32_t DTYPE_t | |
| ctypedef np.int32_t ITYPE_t | |
| def process_bbox_depth_cy(np.ndarray[DTYPE_t, ndim=2] depth_map, | |
| int y_min, int y_max, int x_min, int x_max): | |
| """ | |
| Optimized bbox depth calculations using Cython | |
| """ | |
| cdef: | |
| int i, j, count = 0 | |
| double sum_val = 0.0 | |
| double mean_val = 0.0 | |
| np.ndarray[DTYPE_t, ndim=1] flat_vals | |
| int flat_size = 0 | |
| for i in range(y_min, y_max): | |
| for j in range(x_min, x_max): | |
| if not isnan(depth_map[i, j]): | |
| sum_val += depth_map[i, j] | |
| count += 1 | |
| if count > 0: | |
| mean_val = sum_val / count | |
| # Create array for trimmed mean calculation | |
| flat_vals = np.zeros(count, dtype=np.float32) | |
| flat_size = 0 | |
| for i in range(y_min, y_max): | |
| for j in range(x_min, x_max): | |
| if not isnan(depth_map[i, j]): | |
| flat_vals[flat_size] = depth_map[i, j] | |
| flat_size += 1 | |
| return mean_val, np.median(flat_vals), np.percentile(flat_vals, [20, 80]) | |
| def handle_overlaps_cy(np.ndarray[DTYPE_t, ndim=2] depth_map, | |
| np.ndarray[ITYPE_t, ndim=2] boxes): | |
| """ | |
| Optimized overlap handling using Cython | |
| """ | |
| cdef: | |
| int n_boxes = boxes.shape[0] | |
| int i, j | |
| int y_min1, y_max1, x_min1, x_max1 | |
| int y_min2, y_max2, x_min2, x_max2 | |
| double area1, area2, area_intersection | |
| bint* to_remove = <bint*>PyMem_Malloc(n_boxes * sizeof(bint)) | |
| if not to_remove: | |
| raise MemoryError() | |
| try: | |
| for i in range(n_boxes): | |
| to_remove[i] = False | |
| for i in range(n_boxes): | |
| if to_remove[i]: | |
| continue | |
| y_min1, x_min1, y_max1, x_max1 = boxes[i] | |
| for j in range(i + 1, n_boxes): | |
| if to_remove[j]: | |
| continue | |
| y_min2, x_min2, y_max2, x_max2 = boxes[j] | |
| # Calculate intersection | |
| y_min_int = max(y_min1, y_min2) | |
| y_max_int = min(y_max1, y_max2) | |
| x_min_int = max(x_min1, x_min2) | |
| x_max_int = min(x_max1, x_max2) | |
| if y_min_int < y_max_int and x_min_int < x_max_int: | |
| area1 = (y_max1 - y_min1) * (x_max1 - x_min1) | |
| area2 = (y_max2 - y_min2) * (x_max2 - x_min2) | |
| area_intersection = (y_max_int - y_min_int) * (x_max_int - x_min_int) | |
| if area_intersection / min(area1, area2) >= 0.70: | |
| if area1 < area2: | |
| to_remove[i] = True | |
| break | |
| else: | |
| to_remove[j] = True | |
| return np.array([i for i in range(n_boxes) if not to_remove[i]], dtype=np.int32) | |
| finally: | |
| PyMem_Free(to_remove) | |