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How to use non_empty method in pandera

Best Python code snippet using pandera_python

evaluation.py

Source:evaluation.py

1#!/usr/bin/env python3.52import numpy as np3from PIL import Image, ImageDraw4def two_d_iou(box, boxes):5    """Compute 2D IOU between a 2D bounding box 'box' and a list6    :param box: a numpy array in the form of [x1, y1, x2, y2] where (x1,y1) are7    image coordinates of the top-left corner of the bounding box, and (x2,y2)8    are the image coordinates of the bottom-right corner of the bounding box.9    :param boxes: a numpy array formed as a list of boxes in the form10    [[x1, y1, x2, y2], [x1, y1, x2, y2]].11    :return iou: a numpy array containing 2D IOUs between box and every element12    in numpy array boxes.13    """14    iou = np.zeros(len(boxes), np.float64)15    x1_int = np.maximum(box[0], boxes[:, 0])16    y1_int = np.maximum(box[1], boxes[:, 1])17    x2_int = np.minimum(box[2], boxes[:, 2])18    y2_int = np.minimum(box[3], boxes[:, 3])19    w_int = x2_int - x1_int20    h_int = y2_int - y1_int21    non_empty = np.logical_and(w_int > 0, h_int > 0)22    if non_empty.any():23        intersection_area = np.multiply(w_int[non_empty], h_int[non_empty])24        box_area = (box[2] - box[0]) * (box[3] - box[1])25        boxes_area = np.multiply(26            boxes[non_empty, 2] - boxes[non_empty, 0],27            boxes[non_empty, 3] - boxes[non_empty, 1])28        union_area = box_area + boxes_area - intersection_area29        iou[non_empty] = intersection_area / union_area30    return iou.round(3)31def two_half_d_iou(box, boxes):32    """Computes approximate 2.5D IOU on BEV between a 3D bounding box 'box' and a list33        of 3D bounding boxes 'boxes'. All boxes are assumed to be aligned with34        respect to gravity. Boxes are allowed to rotate only around their z-axis.35        :param box: a numpy array of the form: [ry, l, w, h, tx, ty, tz]36        :param boxes: a numpy array of the form:37            [[ry, l, h, w, tx, ty, tz], [ry, l, w, h, tx, ty, tz]]38        :return iou: a numpy array containing 3D IOUs between box and every element39            in numpy array boxes.40    """41    if len(boxes.shape) == 1:42        boxes = np.array([boxes])43    box_diag = np.sqrt(np.square(box[1]) +44                       np.square(box[2])) / 245    boxes_diag = np.sqrt(np.square(boxes[:, 1]) +46                         np.square(boxes[:, 2])) / 247    dist = np.sqrt(np.square(boxes[:, 4] - box[4]) +48                   np.square(boxes[:, 6] - box[6]))49    non_empty = box_diag + boxes_diag >= dist50    iou = np.zeros(len(boxes), np.float64)51    if non_empty.any():52        # height_int, _ = height_metrics(box, boxes[non_empty])53        intersection = get_rectangular_metrics(box, boxes[non_empty])54        vol_box = np.prod(box[1:3])55        vol_boxes = np.prod(boxes[non_empty, 1:3], axis=1)56        union = vol_box + vol_boxes - intersection57        iou[non_empty] = intersection / union58    if iou.shape[0] == 1:59        iou = iou[0]60    return iou61def three_d_iou(box, boxes):62    """Computes approximate 3D IOU between a 3D bounding box 'box' and a list63    of 3D bounding boxes 'boxes'. All boxes are assumed to be aligned with64    respect to gravity. Boxes are allowed to rotate only around their z-axis.65    :param box: a numpy array of the form: [ry, l, w, h, tx, ty, tz]66    :param boxes: a numpy array of the form:67        [[ry, l, h, w, tx, ty, tz], [ry, l, w, h, tx, ty, tz]]68    :return iou: a numpy array containing 3D IOUs between box and every element69        in numpy array boxes.70    """71    # First, rule out boxes that do not intersect by checking if the spheres72    # which inscribes them intersect.73    if len(boxes.shape) == 1:74        boxes = np.array([boxes])75    box_diag = np.sqrt(np.square(box[1]) +76                       np.square(box[2]) +77                       np.square(box[3])) / 278    boxes_diag = np.sqrt(np.square(boxes[:, 1]) +79                         np.square(boxes[:, 2]) +80                         np.square(boxes[:, 3])) / 281    dist = np.sqrt(np.square(boxes[:, 4] - box[4]) +82                   np.square(boxes[:, 5] - box[5]) +83                   np.square(boxes[:, 6] - box[6]))84    non_empty = box_diag + boxes_diag >= dist85    iou = np.zeros(len(boxes), np.float64)86    if non_empty.any():87        height_int, _ = height_metrics(box, boxes[non_empty])88        rect_int = get_rectangular_metrics(box, boxes[non_empty])89        intersection = np.multiply(height_int, rect_int)90        vol_box = np.prod(box[1:4])91        vol_boxes = np.prod(boxes[non_empty, 1:4], axis=1)92        union = vol_box + vol_boxes - intersection93        iou[non_empty] = intersection / union94    if iou.shape[0] == 1:95        iou = iou[0]96    return iou97def height_metrics(box, boxes):98    """Compute 3D height intersection and union between a box and a list of99    boxes100    :param box: a numpy array of the form: [ry, l, h, w, tx, ty, tz]101    :param boxes: a numpy array of the form: [[ry, l, h, w, tx, ty, tz],.....102                                        [ry, l, h, w, tx, ty, tz]]103    :return height_intersection: a numpy array containing the intersection along104    the gravity axis between the two bbs105    :return height_union: a numpy array containing the union along the gravity106    axis between the two bbs107    """108    boxes_heights = boxes[:, 2]109    boxes_centroid_heights = boxes[:, 5]110    min_y_boxes = boxes_centroid_heights - boxes_heights111    max_y_box = box[5]112    min_y_box = box[5] - box[2]113    max_of_mins = np.maximum(min_y_box, min_y_boxes)114    min_of_maxs = np.minimum(max_y_box, boxes_centroid_heights)115    offsets = min_of_maxs - max_of_mins116    height_intersection = np.maximum(0, offsets)117    height_union = np.maximum(min_y_box, boxes_centroid_heights) \118        - np.minimum(min_y_box, min_y_boxes) - \119        np.maximum(0, -offsets)120    return height_intersection, height_union121def get_rotated_3d_bb(boxes):122    """Compute rotated 3D bounding box coordinates.123    :param boxes: a numpy array of the form: [[ry, l, h, w, tx, ty, tz],...124                                         [ry, l, h, w, tx, ty, tz]]125    :return x: x coordinates of the four corners required to describe a 3D126    bounding box arranged as [[x1, x2, x3, x4],127                     [x1, x2, x3, x4],128                     ... ]129    :return z: z coordinates of the four corners required to describe a 3D130    bounding box arranged as [[z1, z2, z3, z4],131                     [z1, z2, z3, z4],132                     ... ].133    """134    if len(boxes.shape) == 1:135        boxes = np.array([boxes])136    x = np.array([[]])137    z = np.array([[]])138    for i in boxes:139        rot_mat = np.array([[np.cos(i[0]), np.sin(i[0])],140                            [-np.sin(i[0]), np.cos(i[0])]])141        x_corners = np.multiply(i[1] / 2, np.array([1, 1, -1, -1]))142        z_corners = np.multiply(i[3] / 2, np.array([1, -1, -1, 1]))143        temp_coor = np.dot(rot_mat, np.array([x_corners, z_corners]))144        # At the very first iteration, initialize x145        if x.shape[1] < 1:146            x = temp_coor[:1] + i[4]147            z = temp_coor[1:2] + i[6]148        # After that, append to the existing x149        else:150            x = np.append(x, temp_coor[:1] + i[4], axis=0)151            z = np.append(z, temp_coor[1:2] + i[6], axis=0)152    if x.shape[0] == 1:153        x = x[0]154        z = z[0]155    return x, z156def get_rectangular_metrics(box, boxes):157    """ Computes the intersection of the bases of oriented 3D bounding "box"158    and a set boxes of oriented 3D bounding boxes "boxes".159    :param box: a numpy array of the form: [ry, l, h, w, tx, ty, tz]160    :param boxes: a numpy array of the form: [[ry, l, h, w, tx, ty, tz],.....161                                        [ry, l, h, w, tx, ty, tz]]162    :return intersection: a numpy array containing intersection between the163    base of box and all other boxes.164    """165    if len(boxes.shape) == 1:166        boxes = np.array([boxes])167    mask_res = 0.01168    x_box, z_box = get_rotated_3d_bb(box)169    max_x_box = np.max(x_box)170    min_x_box = np.min(x_box)171    max_z_box = np.max(z_box)172    min_z_box = np.min(z_box)173    x_boxes, z_boxes = get_rotated_3d_bb(boxes)174    intersection = np.zeros(np.size(boxes, 0))175    if np.size(np.shape(x_boxes)) == 1:176        x_boxes = np.array([x_boxes])177        z_boxes = np.array([z_boxes])178    for i in range(np.size(boxes, 0)):179        x_i = x_boxes[i, :]180        z_i = z_boxes[i, :]181        test = max_x_box < np.min(x_i) or np.max(x_i) < min_x_box \182            or max_z_box < np.min(z_i) or np.max(z_i) < min_z_box183        if test:184            continue185        x_all = np.append(x_box, x_i)186        z_all = np.append(z_box, z_i)187        maxs = np.array([np.max(x_all), np.max(z_all)])188        mins = np.array([np.min(x_all), np.min(z_all)])189        mask_dims = np.int32(np.ceil((maxs - mins) / mask_res))190        mask_box_x = (x_box - mins[0]) / mask_res191        mask_box_z = (z_box - mins[1]) / mask_res192        mask_i_x = (x_i - mins[0]) / mask_res193        mask_i_z = (z_i - mins[1]) / mask_res194        # Drawing a binary image of the base of the two bounding boxes.195        # Then compute the element wise and of the two images to get the intersection.196        # Minor precision loss due to discretization.197        img = Image.new('L', (mask_dims[0], mask_dims[1]), 0)198        draw = ImageDraw.Draw(img, 'L')199        rect_coordinates = np.reshape(np.transpose(np.array([mask_box_x,200                                                             mask_box_z])), 8)201        rect_coordinates = np.append(rect_coordinates, rect_coordinates[0:2])202        draw.polygon(rect_coordinates.ravel().tolist(), outline=255, fill=255)203        del draw204        mask_box = np.asarray(img)205        img2 = Image.new('L', (mask_dims[0], mask_dims[1]), 0)206        draw = ImageDraw.Draw(img2, 'L')207        i_coordinates = np.reshape(np.transpose(np.array([mask_i_x,208                                                          mask_i_z])), 8)209        i_coordinates = np.append(i_coordinates, i_coordinates[0:2])210        draw.polygon(i_coordinates.ravel().tolist(), outline=255, fill=255)211        del draw212        mask_i = np.asarray(img2)213        mask_intersection = np.logical_and(mask_box, mask_i)214        intersection[i] = min(100, np.size(np.flatnonzero(215            mask_intersection)) * np.square(mask_res))216    if intersection.shape[0] == 1:217        intersection = intersection[0]...

Practica1MartinezBotet.py

Source:Practica1MartinezBotet.py

1from multiprocessing import Process2from multiprocessing import BoundedSemaphore, Semaphore, Lock3from multiprocessing import current_process4from multiprocessing import Value, Array, Queue5from time import sleep6from random import random, randint7"N es cuanto produce cada uno"8N = 209K = 310NPROD = 311NCONS = 112def delay(factor = 3):13    sleep(random()/factor)14"index es a quien le toca"15def add_data(storage, data, pid,  mutex):16    mutex.acquire()17    try:18        storage[pid] = randint(0,5) + data.value19        delay(6)20    finally:21        mutex.release()22"Cogemos el dato y lo cambiamos por 0, la idea del bucle es que te devuleve el min y la pos"23def get_data(storage,mutex,data):24    mutex.acquire()25    print(storage[0],storage[1],storage[2])26    i=127    m=storage[0]28    pos=029    while m==-1:30        m=storage[i]31        pos=i32        i=i+133    try:34        for i,c in enumerate(storage):35            if c<m and c!=-1:36                m=c37                pos=i38        data.value=m39        storage[pos]=040    finally:41        mutex.release()42    return data.value,pos43""44def producer(storage, empty, non_empty, mutex,data):45    for v in range(N):46        print (f"producer {current_process().name} produciendo")47        pid=int(current_process().name.split('_')[1])48        delay(6)49        empty[pid].acquire()50        try:51            add_data(storage,data, pid,52                     mutex)53        finally:    54            non_empty[pid].release()55        print (f"producer {current_process().name} almacenado {v}")56    empty[pid].acquire()57    storage[pid]=-158    non_empty[pid].release()59    60def hay_productor(storage):61    res=False62    i=063    while i<len(storage) and not(res):64        if storage[i]!=-1:65            res=True66        i=i+167    return res68def consumer(storage, data, empty, non_empty, mutex):69    l=[]70    for i in non_empty:71        i.acquire()72    while hay_productor(storage):73        valor,pos=get_data(storage,mutex,data)74        l.append(valor)75        empty[pos].release()76        non_empty[pos].acquire()77    print("Ya no hay productores")78    print(l)79def main():80    storage = Array('i', K)81    data=Value('i',0)82    for i in range(K):83        storage[i] = 084    #print ("almacen inicial", storage[:], "indice", index.value)85    non_empty = [Semaphore(0) for _ in range(NPROD)]86    empty = [Lock() for _ in range(NPROD)]87    mutex = Lock()88    prodlst = [ Process(target=producer,89                        name=f'prod_{i}',90                        args=(storage, empty, non_empty, mutex,data))91                for i in range(NPROD) ]92    conslst = [ Process(target=consumer,93                      name=f"cons_{i}",94                      args=(storage, data,empty, non_empty, mutex))]95    for p in prodlst + conslst:96        p.start()97    for p in prodlst + conslst:98        p.join()99        100if __name__ == '__main__':...

analyze.py

Source:analyze.py

1#!/usr/bin/env python2__author__ = 'greg'3import os4import cPickle as pickle5# for Greg - which computer am I on?6if os.path.exists("/home/ggdhines"):7    base_directory = "/home/ggdhines"8    code_directory = base_directory + "/github"9elif os.path.exists("/Users/greg"):10    base_directory = "/Users/greg"11    code_directory = base_directory + "/Code"12else:13    base_directory = "/home/greg"14    code_directory = base_directory + "/github"15results = pickle.load(open(base_directory+"/Databases/serengeti/results.pickle","rb"))16skipped = 017non_empty = 018for zooniverse_id in results:19    status = results[zooniverse_id]["status"]20    if status == "skipped":21        skipped += 122        continue23    classification = results[zooniverse_id]["name"]24    if "deer" in classification:25        non_empty += 126    elif "hyena" in classification:27        non_empty += 128    elif ("wildebeest" in classification) or ("wildebeast" in classification):29        non_empty += 130    elif "zebra" in classification:31        non_empty += 132    elif "impala" in classification:33        non_empty += 134    elif "gazelle" in classification:35        non_empty += 136    elif "buffalo" in classification:37        non_empty += 138    elif "elephant" in classification:39        non_empty += 140    elif "animal" in classification:41        non_empty += 142    elif "lion" in classification:43        non_empty += 144    elif ("aardvark" in classification) or ("aadvark" in classification):45        non_empty += 146    elif "horse" in classification:47        non_empty += 148    elif ("birds" in classification) or ("bird" in classification):49        non_empty += 150    elif "oryx" in classification:51        non_empty += 152    elif "antelope" in classification:53        non_empty += 154    elif "emu" in classification:55        non_empty += 156    elif "fowl" in classification:57        non_empty += 158    elif ("suv" in classification) or ("rover" in classification) or ("vehicle" in classification):59        non_empty += 160    elif "peacock" in classification:61        non_empty += 162    elif "dikdik" in classification:63        non_empty += 164    elif "giraffe" in classification:65        non_empty += 166    elif "bull" in classification:67        non_empty += 168    elif ("jeans" in classification) or ("board" in classification) or ("pants" in classification) or ("jacket" in classification):69        non_empty += 170    elif "cow" in classification:71        non_empty += 172    elif "warthog" in classification:73        non_empty += 174    elif "baboon" in classification:75        non_empty += 176    elif "mammal" in classification:77        non_empty += 178    elif "boar" in classification:79        non_empty += 180    else:81        print classification...

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