Object Detection using Region Proposal Algorithm
Object Detection using Region Proposal Algorithm
import tensorflow as tf
import numpy as np
import argparse
import cv2
print(tf.__version__)
print(cv2.__version__)
import matplotlib.pyplot as plt
import time
from imutils.object_detection import non_max_suppression
ap = argparse.ArgumentParser()
ap.add_argument("-i", "--image", default = "images/hens.jpg", required = False,
help="path to the input image")
ap.add_argument("-m", "--method", type=str, default="fast",
choices=["fast", "quality"],
help="selective search method")
ap.add_argument("-c", "--conf", type=float, default=0.7,
help="minimum probability to consider a classification/detection")
ap.add_argument("-f", "--filter", type=str, default=None,
help="comma separated list of ImageNet labels to filter on")
args = vars(ap.parse_args([]))
labelFilters = args["filter"]
image = cv2.imread(args["image"])
image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
w = 255
scale = w/(image.shape[1])
h = int(scale * image.shape[0])
image = cv2.resize(image, (w, h))
plt.imshow(image)
(H, W) = image.shape[:2]
# initialize OpenCV's selective search implementation and set the
# input image
ss = cv2.ximgproc.segmentation.createSelectiveSearchSegmentation()
ss.setBaseImage(image)
# check to see if we are using the *fast* but *less accurate* version
# of selective search
if args["method"] == "fast":
print("[INFO] using *fast* selective search")
ss.switchToSelectiveSearchFast()
# otherwise we are using the *slower* but *more accurate* version
else:
print("[INFO] using *quality* selective search")
ss.switchToSelectiveSearchQuality()
# run selective search on the input image
start = time.time()
rects = ss.process()
end = time.time()
# show how along selective search took to run along with the total
# number of returned region proposals
print("[INFO] selective search took {:.4f} seconds".format(end - start))
print("[INFO] {} total region proposals".format(len(rects)))
# loop over the region proposals in chunks (so we can better
# visualize them)
for i in range(0, len(rects), 100):
# clone the original image so we can draw on it
output = image.copy()
# loop over the current subset of region proposals
for (x, y, w, h) in rects[i:i + 100]:
# draw the region proposal bounding box on the image
color = [np.random.randint(0, 255) for j in range(0, 3)]
cv2.rectangle(output, (x, y), (x + w, y + h), color, 2)
# show the output image
#cv2.imshow("Output", output) #plt.imshow("selectiveSearch.jpg") #cv2.imshow("Output", output)
#key = cv2.waitKey(0) & 0xFF
# if the `q` key was pressed, break from the loop
#if key == ord("q"):
#break
model = tf.keras.applications.ResNet50(weights = "imagenet", include_top = True)
proposals = []
boxes = []
# loop over the region proposal bounding box coordinates generated by
# running selective search
for (x, y, w, h) in rects:
# if the width or height of the region is less than 10% of the
# image width or height, ignore it (i.e., filter out small
# objects that are likely false-positives)
if w / float(W) < 0.1 or h / float(H) < 0.1:
continue
# extract the region from the input image, convert it from BGR to
# RGB channel ordering, and then resize it to 224x224 (the input
# dimensions required by our pre-trained CNN)
roi = image[y:y + h, x:x + w]
roi = cv2.cvtColor(roi, cv2.COLOR_BGR2RGB)
roi = cv2.resize(roi, (224, 224))
# further preprocess by the ROI
roi = tf.keras.preprocessing.image.img_to_array(roi)
roi = tf.keras.applications.resnet50.preprocess_input(roi)
# update our proposals and bounding boxes lists
proposals.append(roi)
boxes.append((x, y, w, h))
proposals = np.array(proposals)
print("[INFO] proposal shape: {}".format(proposals.shape))
print("[INFO] proposal number: {}".format(len(proposals)))
# classify each of the proposal ROIs using ResNet and then decode the
# predictions
print("[INFO] classifying proposals...")
preds = model.predict(proposals)
preds = tf.keras.applications.imagenet_utils.decode_predictions(preds, top=1)
# initialize a dictionary which maps class labels (keys) to any
# bounding box associated with that label (values)
labels = {}
preds[15:30][0]
for (i, p) in enumerate(preds):
# grab the prediction information for the current region proposal
(imagenetID, label, prob) = p[0]
# only if the label filters are not empty *and* the label does not
# exist in the list, then ignore it
if labelFilters is not None and label not in labelFilters:
continue
# filter out weak detections by ensuring the predicted probability
# is greater than the minimum probability
if prob >= args["conf"]:
# grab the bounding box associated with the prediction and
# convert the coordinates
(x, y, w, h) = boxes[i]
box = (x, y, x + w, y + h)
# grab the list of predictions for the label and add the
# bounding box + probability to the list
L = labels.get(label, [])
L.append((box, prob))
labels[label] = L
for label in labels.keys():
# clone the original image so that we can draw on it
print("[INFO] showing results for '{}'".format(label))
clone = image.copy()
# loop over all bounding boxes for the current label
for (box, prob) in labels[label]:
# draw the bounding box on the image
(startX, startY, endX, endY) = box
cv2.rectangle(clone, (startX, startY), (endX, endY),(0, 255, 0), 2)
# show the results *before* applying non-maxima suppression, then
# clone the image again so we can display the results *after*
# applying non-maxima suppression
#cv2.imshow("Before", clone)
plt.imshow(clone)
plt.savefig("images/keras_detection/hens_before.jpg")
clone = image.copy()
# extract the bounding boxes and associated prediction
# probabilities, then apply non-maxima suppression
boxes = np.array([p[0] for p in labels[label]])
proba = np.array([p[1] for p in labels[label]])
boxes = non_max_suppression(boxes, proba)
# loop over all bounding boxes that were kept after applying
# non-maxima suppression
for (startX, startY, endX, endY) in boxes:
# draw the bounding box and label on the image
cv2.rectangle(clone, (startX, startY), (endX, endY),(0, 255, 0), 2)
y = startY - 10 if startY - 10 > 10 else startY + 10
cv2.putText(clone, label, (startX, y),cv2.FONT_HERSHEY_SIMPLEX, 0.45, (0, 255, 0), 2)
# show the output after apply non-maxima suppression
plt.imshow(clone)
plt.savefig("images/keras_detection/hens_rpa_after.jpg")
allclone = image.copy()
for label in labels.keys():
# clone the original image so that we can draw on it
print("[INFO] showing results for '{}'".format(label))
clone = image.copy()
# loop over all bounding boxes for the current label
for (box, prob) in labels[label]:
# draw the bounding box on the image
(startX, startY, endX, endY) = box
cv2.rectangle(clone, (startX, startY), (endX, endY),(0, 255, 0), 2)
# show the results *before* applying non-maxima suppression, then
# clone the image again so we can display the results *after*
# applying non-maxima suppression
#plt.imshow(clone)
#cv2.imshow("Before", clone)
clone = image.copy()
# extract the bounding boxes and associated prediction
# probabilities, then apply non-maxima suppression
boxes = np.array([p[0] for p in labels[label]])
proba = np.array([p[1] for p in labels[label]])
boxes = non_max_suppression(boxes, proba)
# loop over all bounding boxes that were kept after applying
# non-maxima suppression
for (startX, startY, endX, endY) in boxes:
# draw the bounding box and label on the image
cv2.rectangle(clone, (startX, startY), (endX, endY),(0, 255, 0), 2)
cv2.rectangle(allclone, (startX, startY), (endX, endY),(0, 255, 0), 2)
y = startY - 10 if startY - 10 > 10 else startY + 10
cv2.putText(clone, label, (startX, y+30),cv2.FONT_HERSHEY_SIMPLEX, 0.45, (0, 255, 0), 2)
cv2.putText(clone, "{:.2f}".format(prob), (startX, y),cv2.FONT_HERSHEY_SIMPLEX, 0.45, (0, 255, 0), 2)
cv2.putText(allclone, label, (startX, y+30),cv2.FONT_HERSHEY_SIMPLEX, 0.45, (0, 255, 0), 2)
cv2.putText(allclone, "{:.2f}".format(prob), (startX, y),cv2.FONT_HERSHEY_SIMPLEX, 0.45, (0, 255, 0), 2)
# show the output after apply non-maxima suppression
plt.imshow(clone)
plt.imsave("images/keras_detection/_rpa"+ "_hens_" + label + ".jpg", clone)
plt.imshow(allclone)
plt.imsave("images/keras_detection/_rpa_allclone" + "_hens" + ".jpg", allclone)
#plt.imshow(clone)
#plt.imsave("images/_res03.jpg", clone)
#cv2.imshow("After", clone)
#cv2.waitKey(0)
