dockerfile/openalpr-base
1
0
Fork 0
mirror of https://github.com/kerberos-io/openalpr-base.git synced 2025-10-05 15:57:01 +08:00
Code Issues Packages Projects Releases Wiki Activity

Added "analysis_count" config setting

Browse Source
This commit is contained in:
Matt Hill
2016-03-13 14:02:16 -04:00
parent 067bfc00a2
commit 91091ba71e
8 changed files with 310 additions and 29 deletions
Download Patch File Download Diff File Expand all files Collapse all files

4
config/openalpr.conf.in
View File

@@ -49,6 +49,10 @@ must_match_pattern = 0
; Bypasses plate detection. If this is set to 1, the library assumes that each region provided is a likely plate area.
skip_detection = 0
; OpenALPR can scan the same image multiple times with different randomization. Setting this to a value larger than
; 1 may increase accuracy, but will increase processing time linearly (e.g., analysis_count = 3 is 3x slower)
analysis_count = 1
; OpenALPR detects high-contrast plate crops and uses an alternative edge detection technique. Setting this to 0.0
; would classify ALL images as high-contrast, setting it to 1.0 would classify no images as high-contrast.
contrast_detection_threshold = 0.3

21
src/openalpr/alpr_impl.cpp
View File

@@ -125,22 +125,33 @@ namespace alpr
// Iterate through each country provided (typically just one)
// and aggregate the results if necessary
ResultAggregator aggregator;
ResultAggregator country_aggregator(MERGE_PICK_BEST, topN, config);
for (unsigned int i = 0; i < config->loaded_countries.size(); i++)
{
if (config->debugGeneral)
cout << "Analyzing: " << config->loaded_countries[i] << endl;
config->setCountry(config->loaded_countries[i]);
AlprFullDetails sub_results = analyzeSingleCountry(img, grayImg, warpedRegionsOfInterest);
// Reapply analysis for each multiple analysis value set in the config,
// make a minor imperceptible tweak to the input image each time
ResultAggregator iter_aggregator(MERGE_COMBINE, topN, config);
for (unsigned int iteration = 0; iteration < 2; iteration++)
{
Mat iteration_image = iter_aggregator.applyImperceptibleChange(grayImg, iteration);
//drawAndWait(iteration_image);
AlprFullDetails iter_results = analyzeSingleCountry(img, iteration_image, warpedRegionsOfInterest);
iter_aggregator.addResults(iter_results);
}
AlprFullDetails sub_results = iter_aggregator.getAggregateResults();
sub_results.results.epoch_time = start_time;
sub_results.results.img_width = img.cols;
sub_results.results.img_height = img.rows;
aggregator.addResults(sub_results);
country_aggregator.addResults(sub_results);
}
response = aggregator.getAggregateResults();
response = country_aggregator.getAggregateResults();
timespec endTime;
getTimeMonotonic(&endTime);

2
src/openalpr/config.cpp
View File

@@ -189,6 +189,8 @@ namespace alpr
skipDetection = getBoolean(ini, "", "skip_detection", false);
analysis_count = getInt(ini, "", "analysis_count", 1);
prewarp = getString(ini, "", "prewarp", "");
maxPlateAngleDegrees = getInt(ini, "", "max_plate_angle_degrees", 15);

2
src/openalpr/config.h
View File

@@ -61,6 +61,8 @@ namespace alpr
bool skipDetection;
int analysis_count;
bool auto_invert;
bool always_invert;

21
src/openalpr/prewarp.cpp
View File

@@ -75,6 +75,7 @@ namespace alpr
{
stringstream ss(prewarp_config.substr(first_comma + 1, prewarp_config.length()));
float w, h, rotationx, rotationy, rotationz, panX, panY, stretchX, dist;
ss >> w;
ss.ignore();
ss >> h;
@@ -93,7 +94,7 @@ namespace alpr
ss.ignore(); // Ignore comma
ss >> panY;
this->valid = true;
setTransform(w, h, rotationx, rotationy, rotationz, panX, panY, stretchX, dist);
}
}
@@ -110,6 +111,20 @@ namespace alpr
PreWarp::~PreWarp() {
}
void PreWarp::setTransform(float w, float h, float rotationx, float rotationy, float rotationz, float panX, float panY, float stretchX, float dist)
{
this->w = w;
this->h = h;
this->rotationx = rotationx;
this->rotationy = rotationy;
this->rotationz = rotationz;
this->panX = panX;
this->panY = panY;
this->stretchX = stretchX;
this->dist = dist;
this->valid = true;
}
cv::Mat PreWarp::warpImage(Mat image) {
if (!this->valid)
@@ -129,7 +144,7 @@ namespace alpr
float py = panY / height_ratio;
transform = findTransform(image.cols, image.rows, rx, ry, rotationz, px, py, stretchX, dist);
transform = getTransform(image.cols, image.rows, rx, ry, rotationz, px, py, stretchX, dist);
Mat warped_image;
@@ -208,7 +223,7 @@ namespace alpr
}
}
cv::Mat PreWarp::findTransform(float w, float h,
cv::Mat PreWarp::getTransform(float w, float h,
float rotationx, float rotationy, float rotationz,
float panX, float panY, float stretchX, float dist) {

5
src/openalpr/prewarp.h
View File

@@ -42,15 +42,18 @@ namespace alpr
std::vector<cv::Rect> projectRects(std::vector<cv::Rect> rects, int maxWidth, int maxHeight, bool inverse);
void projectPlateRegions(std::vector<PlateRegion>& plateRegions, int maxWidth, int maxHeight, bool inverse);
void setTransform(float w, float h, float rotationx, float rotationy, float rotationz, float panX, float panY, float stretchX, float dist);
bool valid;
private:
Config* config;
cv::Mat transform;
cv::Mat getTransform(float w, float h, float rotationx, float rotationy, float rotationz, float panX, float panY, float stretchX, float dist);
float w, h, rotationx, rotationy, rotationz, stretchX, dist, panX, panY;
cv::Mat findTransform(float w, float h, float rotationx, float rotationy, float rotationz, float panX, float panY, float stretchX, float dist);
};
}

249
src/openalpr/result_aggregator.cpp
View File

@@ -25,13 +25,16 @@ using namespace cv;
namespace alpr
{
ResultAggregator::ResultAggregator()
ResultAggregator::ResultAggregator(ResultMergeStrategy merge_strategy, int topn, Config* config)
{
this->prewarp = new PreWarp(config);
this->merge_strategy = merge_strategy;
this->topn = topn;
this->config = config;
}
ResultAggregator::~ResultAggregator() {
delete prewarp;
}
@@ -39,7 +42,46 @@ namespace alpr
{
all_results.push_back(full_results);
}
cv::Mat ResultAggregator::applyImperceptibleChange(cv::Mat image, int index) {
const float WIDTH_HEIGHT = 600;
const float NO_MOVE_WIDTH_DIST = 1.0;
const float NO_PAN_VAL = 0;
float step = 0.000035;
// Don't warp the first indexed image
if (index == 0)
return image;
// Use 3 bits to figure out which one is on. Multiply by the modulus of 8
// 000, 001, 010, 011, 100, 101, 110, 111
// if 101, then x_rotation and z_rotation are on.
if (index % 8 == 0)
{
// Do something special for the 0s, so they don't repeat
index--;
step = step / 1.5;
}
int multiplier = (index / 8) + 1;
int bitwise_on = index % 8;
float x_rotation = ((bitwise_on & 1) == 1) * multiplier * step;
float y_rotation = ((bitwise_on & 2) == 2) * multiplier * step;
float z_rotation = ((bitwise_on & 4) == 4) * multiplier * step;
//cout << "Iteration: " << index << ": " << x_rotation << ", " << y_rotation << ", " << z_rotation << endl;
prewarp->setTransform(WIDTH_HEIGHT, WIDTH_HEIGHT, x_rotation, y_rotation, z_rotation,
NO_PAN_VAL, NO_PAN_VAL, NO_MOVE_WIDTH_DIST, NO_MOVE_WIDTH_DIST);
return prewarp->warpImage(image);
}
bool compareScore(const std::pair<float, ResultPlateScore>& firstElem, const std::pair<float, ResultPlateScore>& secondElem) {
return firstElem.first > secondElem.first;
}
AlprFullDetails ResultAggregator::getAggregateResults()
{
assert(all_results.size() > 0);
@@ -68,28 +110,199 @@ namespace alpr
vector<vector<AlprPlateResult> > clusters = findClusters();
// Assume we have multiple results, one cluster for each unique train data (e.g., eu, eu2)
// Now for each cluster of plates, pick the best one
for (unsigned int i = 0; i < clusters.size(); i++)
if (merge_strategy == MERGE_PICK_BEST)
{
float best_confidence = 0;
int best_index = 0;
for (unsigned int k = 0; k < clusters[i].size(); k++)
{
if (clusters[i][k].bestPlate.overall_confidence > best_confidence)
{
best_confidence = clusters[i][k].bestPlate.overall_confidence;
best_index = k;
}
}
// Assume we have multiple results, one cluster for each unique train data (e.g., eu, eu2)
response.results.plates.push_back(clusters[i][best_index]);
// Now for each cluster of plates, pick the best one
for (unsigned int i = 0; i < clusters.size(); i++)
{
float best_confidence = 0;
int best_index = 0;
for (unsigned int k = 0; k < clusters[i].size(); k++)
{
if (clusters[i][k].bestPlate.overall_confidence > best_confidence)
{
best_confidence = clusters[i][k].bestPlate.overall_confidence;
best_index = k;
}
}
response.results.plates.push_back(clusters[i][best_index]);
}
}
else if (merge_strategy == MERGE_COMBINE)
{
// Each cluster is the same plate, just analyzed from a slightly different
// perspective. Merge them together and score them as if they are one
const float MIN_CONFIDENCE = 75;
// Factor in the position of the plate in the topN list, the confidence, and the template match status
// First loop is for clusters of possible plates. If they're in separate clusters, they don't get combined,
// since they are likely separate plates in the same image
for (unsigned int unique_plate_idx = 0; unique_plate_idx < clusters.size(); unique_plate_idx++)
{
std::map<string, ResultPlateScore> score_hash;
// Second loop is for separate plate results for the same plate
for (unsigned int i = 0; i < clusters[unique_plate_idx].size(); i++)
{
// Third loop is the individual topN results for a single plate result
for (unsigned int j = 0; j < clusters[unique_plate_idx][i].topNPlates.size() && j < topn; j++)
{
AlprPlate plateCandidate = clusters[unique_plate_idx][i].topNPlates[j];
if (plateCandidate.overall_confidence < MIN_CONFIDENCE)
continue;
float score = (plateCandidate.overall_confidence - 60) * 4;
// Add a bonus for matching the template
if (plateCandidate.matches_template)
score += 150;
// Add a bonus the higher the plate is to the #1 position
// and how frequently it appears there
float position_score_max_bonus = 65;
float frequency_modifier = ((float) position_score_max_bonus) / topn;
score += position_score_max_bonus - (j * frequency_modifier);
if (score_hash.find(plateCandidate.characters) == score_hash.end())
{
ResultPlateScore newentry;
newentry.plate = plateCandidate;
newentry.score_total = 0;
newentry.count = 0;
score_hash[plateCandidate.characters] = newentry;
}
score_hash[plateCandidate.characters].score_total += score;
score_hash[plateCandidate.characters].count += 1;
// Use the best confidence value for a particular candidate
if (plateCandidate.overall_confidence > score_hash[plateCandidate.characters].plate.overall_confidence)
score_hash[plateCandidate.characters].plate.overall_confidence = plateCandidate.overall_confidence;
}
}
// There is a big list of results that have scores. Sort them by top score
std::vector<std::pair<float, ResultPlateScore> > sorted_results;
std::map<string, ResultPlateScore>::iterator iter;
for (iter = score_hash.begin(); iter != score_hash.end(); iter++) {
std::pair<float,ResultPlateScore> r;
r.second = iter->second;
r.first = iter->second.score_total;
sorted_results.push_back(r);
}
std::sort(sorted_results.begin(), sorted_results.end(), compareScore);
// output the sorted list for debugging:
if (config->debugGeneral)
{
cout << "Result Aggregator Scores: " << endl;
cout << " " << std::setw(14) << "Plate Num"
<< std::setw(15) << "Score"
<< std::setw(10) << "Count"
<< std::setw(10) << "Best conf (%)"
<< endl;
for (int r_idx = 0; r_idx < sorted_results.size(); r_idx++)
{
cout << " " << std::setw(14) << sorted_results[r_idx].second.plate.characters
<< std::setw(15) << sorted_results[r_idx].second.score_total
<< std::setw(10) << sorted_results[r_idx].second.count
<< std::setw(10) << sorted_results[r_idx].second.plate.overall_confidence
<< endl;
}
}
// Figure out the best region for this cluster
ResultRegionScore regionResults = findBestRegion(clusters[unique_plate_idx]);
AlprPlateResult firstResult = clusters[unique_plate_idx][0];
AlprPlateResult copyResult;
copyResult.bestPlate = sorted_results[0].second.plate;
copyResult.plate_index = firstResult.plate_index;
copyResult.region = regionResults.region;
copyResult.regionConfidence = regionResults.confidence;
copyResult.processing_time_ms = firstResult.processing_time_ms;
copyResult.requested_topn = firstResult.requested_topn;
for (int p_idx = 0; p_idx < 4; p_idx++)
copyResult.plate_points[p_idx] = firstResult.plate_points[p_idx];
for (int i = 0; i < sorted_results.size(); i++)
{
if (i >= topn)
break;
copyResult.topNPlates.push_back(sorted_results[i].second.plate);
}
response.results.plates.push_back(copyResult);
}
}
return response;
}
ResultRegionScore ResultAggregator::findBestRegion(std::vector<AlprPlateResult> cluster) {
const float MIN_REGION_CONFIDENCE = 60;
std::map<std::string, float> score_hash;
std::map<std::string, float> score_count;
int max_topn = 10;
ResultRegionScore response;
response.confidence = 0;
response.region = "";
for (unsigned int i = 0; i < cluster.size(); i++)
{
AlprPlateResult plate = cluster[i];
if (plate.bestPlate.overall_confidence < MIN_REGION_CONFIDENCE )
continue;
float score = (float) plate.regionConfidence;
if (score_hash.count(plate.region) == 0)
{
score_hash[plate.region] = 0;
score_count[plate.region] = 0;
}
score_hash[plate.region] = score_hash[plate.region] + score;
score_count[plate.region] = score_count[plate.region] + 1;
}
float best_score = -1;
std::string best_score_val = "";
// Now we have a hash that contains all the scores. Iterate and find the best one and return it.
for(std::map<std::string, float >::iterator hash_iter=score_hash.begin(); hash_iter!=score_hash.end(); ++hash_iter) {
if (hash_iter->second > best_score)
{
best_score = hash_iter->second;
best_score_val = hash_iter->first;
}
}
if (best_score > 0)
{
response.confidence = best_score / score_count[best_score_val];
response.region = best_score_val;
}
return response;
}
// Searches all_plates to find overlapping plates
// Returns an array containing "clusters" (overlapping plates)
std::vector<std::vector<AlprPlateResult> > ResultAggregator::findClusters()

35
src/openalpr/result_aggregator.h
View File

@@ -22,7 +22,7 @@
#include "alpr_impl.h"
#include "prewarp.h"
// Runs the analysis for multiple training sets, and aggregates the results into the best matches
@@ -37,10 +37,30 @@ struct PlateShapeInfo
namespace alpr
{
enum ResultMergeStrategy
{
MERGE_COMBINE, // Used when running an analysis multiple times for accuracy improvement. Merges results together
MERGE_PICK_BEST // Used when analyzing multiple countries. Chooses results from one country or the other
};
struct ResultPlateScore
{
AlprPlate plate;
float score_total;
int count;
};
struct ResultRegionScore
{
std::string region;
float confidence;
};
class ResultAggregator
{
public:
ResultAggregator();
ResultAggregator(ResultMergeStrategy merge_strategy, int topn, Config* config);
virtual ~ResultAggregator();
@@ -48,11 +68,22 @@ namespace alpr
AlprFullDetails getAggregateResults();
cv::Mat applyImperceptibleChange(cv::Mat image, int index);
private:
int topn;
PreWarp* prewarp;
Config* config;
std::vector<AlprFullDetails> all_results;
PlateShapeInfo getShapeInfo(AlprPlateResult plate);
ResultMergeStrategy merge_strategy;
ResultRegionScore findBestRegion(std::vector<AlprPlateResult> cluster);
std::vector<std::vector<AlprPlateResult> > findClusters();
int overlaps(AlprPlateResult plate, std::vector<std::vector<AlprPlateResult> > clusters);
};