detect_with_classifier.py

import tensorflow as tf
from tensorflow.keras.applications.resnet import preprocess_input
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from tensorflow.keras.preprocessing.image import img_to_array
from tensorflow.keras.applications import imagenet_utils
from imutils.object_detection import non_max_suppression
from tensorflow.keras.models import load_model
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from tensorflwo.keras.preprocessing.image import img_to_array
from tensorflow.keras.applications import imagenet_utils
from imutils.object_detection import non_max_suppression
from pyimagesearch.detection_helpers import sliding_window
from pyimagesearch.detection_helpers import image_pyramid
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import numpy as np
import argparse
import imutils
import time
import cv2

# import the necessary packages
import imutils
def sliding_window(image, step, ws):
	# slide a window across the image
	for y in range(0, image.shape[0] - ws[1], step):
		for x in range(0, image.shape[1] - ws[0], step):
			# yield the current window
			yield (x, y, image[y:y + ws[1], x:x + ws[0]])

def image_pyramid(image, scale=1.5, minSize=(224, 224)):
	# yield the original image
	yield image
	# keep looping over the image pyramid
	while True:
		# compute the dimensions of the next image in the pyramid
		w = int(image.shape[1] / scale)
		image = imutils.resize(image, width=w)
		# if the resized image does not meet the supplied minimum
		# size, then stop constructing the pyramid
		if image.shape[0] < minSize[1] or image.shape[1] < minSize[0]:
			break
		# yield the next image in the pyramid
		yield image

ap = argparse.ArgumentParser()
ap.add_argument("-i", "--image", required=True,
	help="path to the input image")
ap.add_argument("-s", "--size", type=str, default="(200, 150)",
	help="ROI size (in pixels)")
ap.add_argument("-c", "--min-conf", type=float, default=0.9,
	help="minimum probability to filter weak detections")
ap.add_argument("-v", "--visualize", type=int, default=-1,
	help="whether or not to show extra visualizations for debugging")
args = vars(ap.parse_args())

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WIDTH = 430
PYR_SCALE = 1.5
WIN_STEP = 16
ROI_SIZE = eval(args["size"])
INPUT_SIZE = (32, 32)

model = load_model("classification.model")
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WIDTH = 600
PYR_SCALE = 1.5
WIN_STEP = 16
ROI_SIZE = eval(args["size"])
INPUT_SIZE = (224, 224)

classifier = tf.saved_model.load("classification.model/saved_model.pb")
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orig = cv2.imread(args["image"])
orig = imutils.resize(orig, width=WIDTH)
(H, W) = orig.shape[:2]

# initialize the image pyramid
pyramid = image_pyramid(orig, scale=PYR_SCALE, minSize=ROI_SIZE)
# initialize two lists, one to hold the ROIs generated from the image
# pyramid and sliding window, and another list used to store the
# (x, y)-coordinates of where the ROI was in the original image
rois = []
locs = []
# time how long it takes to loop over the image pyramid layers and
# sliding window locations
start = time.time()

# loop over the image pyramid
for image in pyramid:
	# determine the scale factor between the *original* image
	# dimensions and the *current* layer of the pyramid
	scale = W / float(image.shape[1])
	# for each layer of the image pyramid, loop over the sliding
	# window locations
	for (x, y, roiOrig) in sliding_window(image, WIN_STEP, ROI_SIZE):
		# scale the (x, y)-coordinates of the ROI with respect to the
		# *original* image dimensions
		x = int(x * scale)
		y = int(y * scale)
		w = int(ROI_SIZE[0] * scale)
		h = int(ROI_SIZE[1] * scale)
		# take the ROI and preprocess it so we can later classify
		# the region using Keras/TensorFlow
		roi = cv2.resize(roiOrig, INPUT_SIZE)
		roi = img_to_array(roi)
		roi = preprocess_input(roi)
		# update our list of ROIs and associated coordinates
		rois.append(roi)
		locs.append((x, y, x + w, y + h))
        # check to see if we are visualizing each of the sliding
		# windows in the image pyramid
		if args["visualize"] > 0:
			# clone the original image and then draw a bounding box
			# surrounding the current region
			clone = orig.copy()
			cv2.rectangle(clone, (x, y), (x + w, y + h),
				(0, 255, 0), 2)
			# show the visualization and current ROI
			cv2.imshow("Visualization", clone)
			cv2.imshow("ROI", roiOrig)
			cv2.waitKey(0)

# show how long it took to loop over the image pyramid layers and
# sliding window locations
end = time.time()
print("[INFO] looping over pyramid/windows took {:.5f} seconds".format(
	end - start))
# convert the ROIs to a NumPy array
rois = np.array(rois, dtype="float32")
# classify each of the proposal ROIs using ResNet and then show how
# long the classifications took
print("[INFO] classifying ROIs...")
start = time.time()
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preds = model.predict_on_batch(rois)
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preds = model.predict(rois)
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end = time.time()
print("[INFO] classifying ROIs took {:.5f} seconds".format(
	end - start))
# decode the predictions and initialize a dictionary which maps class
# labels (keys) to any ROIs associated with that label (values)
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print(preds.shape[1])
print(preds.shape)

class_index = {}
x = "0"
for line in open("/content/mathOCRdataset/coco.names"):
  class_index[x] = [line[:-1]]
  x = str(int(x) + 1)

top = 200
results = []
for pred in preds:
  top_indices = pred.argsort()[-top:][::-1]
  result = [tuple(class_index[str(i)]) + (pred[i],) for i in top_indices]
  result.sort(key=lambda x: x[1], reverse=True)
  results.append(result)
preds = results

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preds = imagenet_utils.decode_predictions(preds, top=1)
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labels = {}

# loop over the predictions
for (i, p) in enumerate(preds):
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  # grab the prediction information for the current ROI
  (label, prob) = p[0]
	# filter out weak detections by ensuring the predicted probability
	# is greater than the minimum probability
  if prob >= args["min_conf"]:
		# grab the bounding box associated with the prediction and
		# convert the coordinates
    box = locs[i]
    # grab the list of predictions for the label and add the
    # bounding box and probability to the list
    L = labels.get(label, [])
    L.append((box, prob))
    labels[label] = L

# loop over the labels for each of detected objects in the image
for label in labels.keys():
  # clone the original image so that we can draw on it
  print("[INFO] showing results for '{}'".format(label))
  clone = orig.copy()
  # loop over all bounding boxes for the current label
  for (box, prob) in labels[label]:
    (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.imwrite("before.jpg", clone)
  clone = orig.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
  cv2.imwrite("after.jpg", clone)
  cv2.waitKey(0)
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	# grab the prediction information for the current ROI
	(imagenetID, label, prob) = p[0]
	# filter out weak detections by ensuring the predicted probability
	# is greater than the minimum probability
	if prob >= args["min_conf"]:
		# grab the bounding box associated with the prediction and
		# convert the coordinates
		box = locs[i]
		# grab the list of predictions for the label and add the
		# bounding box and probability to the list
		L = labels.get(label, [])
		L.append((box, prob))
		labels[label] = L

# loop over the labels for each of detected objects in the image
for label in labels.keys():
	# clone the original image so that we can draw on it
	print("[INFO] showing results for '{}'".format(label))
	clone = orig.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.imwrite("before.jpg", clone)
	clone = orig.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
	cv2.imwrite("after.jpg", clone)
	cv2.waitKey(0)
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