first commit

Author: tanayvarshney

Conceptual_Guide/Part_1-model_deployment/README.md

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+import numpy as np
+import tritonclient.http as httpclient
+from tritonclient.utils import triton_to_np_dtype
+
+import math
+import cv2
+
+def fourPointsTransform(frame, vertices):
+    vertices = np.asarray(vertices)
+    outputSize = (100, 32)
+    targetVertices = np.array([
+        [0, outputSize[1] - 1],
+        [0, 0],
+        [outputSize[0] - 1, 0],
+        [outputSize[0] - 1, outputSize[1] - 1]], dtype="float32")
+
+    rotationMatrix = cv2.getPerspectiveTransform(vertices, targetVertices)
+    result = cv2.warpPerspective(frame, rotationMatrix, outputSize)
+    return result
+
+def decodeBoundingBoxes(scores, geometry, scoreThresh):
+    detections = []
+    confidences = []
+
+    ############ CHECK DIMENSIONS AND SHAPES OF geometry AND scores ############
+    assert len(scores.shape) == 4, "Incorrect dimensions of scores"
+    assert len(geometry.shape) == 4, "Incorrect dimensions of geometry"
+    assert scores.shape[0] == 1, "Invalid dimensions of scores"
+    assert geometry.shape[0] == 1, "Invalid dimensions of geometry"
+    assert scores.shape[1] == 1, "Invalid dimensions of scores"
+    assert geometry.shape[1] == 5, "Invalid dimensions of geometry"
+    assert scores.shape[2] == geometry.shape[2], "Invalid dimensions of scores and geometry"
+    assert scores.shape[3] == geometry.shape[3], "Invalid dimensions of scores and geometry"
+    height = scores.shape[2]
+    width = scores.shape[3]
+    for y in range(0, height):
+    # Extract data from scores
+        scoresData = scores[0][0][y]
+        x0_data = geometry[0][0][y]
+        x1_data = geometry[0][1][y]
+        x2_data = geometry[0][2][y]
+        x3_data = geometry[0][3][y]
+        anglesData = geometry[0][4][y]
+        for x in range(0, width):
+            score = scoresData[x]
+            # If score is lower than threshold score, move to next x
+            if (score < scoreThresh):
+                continue
+            # Calculate offset
+            offsetX = x * 4.0
+            offsetY = y * 4.0
+            angle = anglesData[x]
+
+            # Calculate cos and sin of angle
+            cosA = math.cos(angle)
+            sinA = math.sin(angle)
+            h = x0_data[x] + x2_data[x]
+            w = x1_data[x] + x3_data[x]
+
+            # Calculate offset
+            offset = ([offsetX + cosA * x1_data[x] + sinA * x2_data[x], offsetY - sinA * x1_data[x] + cosA * x2_data[x]])
+
+            # Find points for rectangle
+            p1 = (-sinA * h + offset[0], -cosA * h + offset[1])
+            p3 = (-cosA * w + offset[0], sinA * w + offset[1])
+            center = (0.5 * (p1[0] + p3[0]), 0.5 * (p1[1] + p3[1]))
+            detections.append((center, (w, h), -1 * angle * 180.0 / math.pi))
+            confidences.append(float(score))
+
+    # Return detections and confidences
+    return [detections, confidences]
+
+# read image
+img = cv2.imread("./img1.jpg")
+height_ = img.shape[0]
+width_ = img.shape[1]
+confThreshold = 0.5
+nmsThreshold = 0.4
+inpWidth = 640
+inpHeight = 480
+
+rW = width_ / float(inpWidth)
+rH = height_ / float(inpHeight)
+
+# pre-process image
+blob = cv2.dnn.blobFromImage(img, 1.0, (inpWidth, inpHeight), (123.68, 116.78, 103.94), True, False)
+blob = np.transpose(blob, (0, 2,3,1))
+
+# Setting up client
+client = httpclient.InferenceServerClient(url="localhost:8000")
+
+input_image = httpclient.InferInput("input_images:0", blob.shape, datatype="FP32")
+input_image.set_data_from_numpy(blob, binary_data=True)
+
+scores = httpclient.InferRequestedOutput("feature_fusion/Conv_7/Sigmoid:0", binary_data=True)
+geometry = httpclient.InferRequestedOutput("feature_fusion/concat_3:0", binary_data=True)
+
+# Querying the server
+query = client.infer(model_name="text_detection", inputs=[input_image], outputs=[scores, geometry])
+
+scores_ = np.transpose(query.as_numpy('feature_fusion/Conv_7/Sigmoid:0'), (0,3,1,2))
+geometry_ = np.transpose(query.as_numpy('feature_fusion/concat_3:0'), (0,3,1,2))
+print(scores_.shape)
+
+#print(scores_.shape)
+[boxes, confidences] = decodeBoundingBoxes(scores_, geometry_, confThreshold)
+indices = cv2.dnn.NMSBoxesRotated(boxes, confidences, confThreshold, nmsThreshold)
+ctr = 0
+for i in indices:
+    vertices = cv2.boxPoints(boxes[i])
+    for j in range(4):
+        vertices[j][0] *= rW
+        vertices[j][1] *= rH
+
+    cropped = fourPointsTransform(img, vertices)
+    cv2.imwrite(str(ctr)+".jpg",cropped)
+    ctr+=1

Conceptual_Guide/Part_1-model_deployment/clients/client.py

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+import numpy as np
+import tritonclient.http as httpclient
+from tritonclient.utils import triton_to_np_dtype
+
+import math
+import cv2
+
+
+def decodeText(scores):
+    text = ""
+    alphabet = "0123456789abcdefghijklmnopqrstuvwxyz"
+    for i in range(scores.shape[0]):
+        c = np.argmax(scores[i][0])
+        if c != 0:
+            text += alphabet[c - 1]
+        else:
+            text += '-'
+    # adjacent same letters as well as background text must be removed to get the final output
+    char_list = []
+    for i in range(len(text)):
+        if text[i] != '-' and (not (i > 0 and text[i] == text[i - 1])):
+            char_list.append(text[i])
+    return ''.join(char_list)
+
+# read image
+img = cv2.imread("./0.jpg")
+
+# pre-process image
+gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+blob = cv2.dnn.blobFromImage(gray, size=(100,32), mean=127.5, scalefactor=1 / 127.5)
+print(blob.shape)
+
+#blob = np.transpose(blob, (0, 2,3,1))
+
+# Setting up client
+client = httpclient.InferenceServerClient(url="localhost:8000")
+
+input_image = httpclient.InferInput("input.1", blob.shape, datatype="FP32")
+input_image.set_data_from_numpy(blob, binary_data=True)
+
+text_matrix = httpclient.InferRequestedOutput("308", binary_data=True)
+
+# Querying the server
+query = client.infer(model_name="text_recognition", inputs=[input_image], outputs=[text_matrix])
+text = decodeText(np.transpose(query.as_numpy('308'), (1,0,2)))

Conceptual_Guide/Part_1-model_deployment/clients/client2.py

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+name: "text_detection"
+backend: "onnxruntime"
+max_batch_size : 0
+input [
+  {
+    name: "input_images:0"
+    data_type: TYPE_FP32
+    dims: [ -1, -1, -1, 3 ]
+  }
+]
+output [
+  {
+    name: "feature_fusion/Conv_7/Sigmoid:0"
+    data_type: TYPE_FP32
+    dims: [ -1, -1, -1, 1 ]
+  }
+]
+output [
+  {
+    name: "feature_fusion/concat_3:0"
+    data_type: TYPE_FP32
+    dims: [ -1, -1, -1, 5 ]
+  }
+]

Conceptual_Guide/Part_1-model_deployment/img/multiple_models.PNG

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+name: "text_recognition"
+backend: "onnxruntime"
+max_batch_size : 0
+input [
+  {
+    name: "input.1"
+    data_type: TYPE_FP32
+    dims: [ 1, 1, 32, 100 ]
+  }
+]
+output [
+  {
+    name: "308"
+    data_type: TYPE_FP32
+    dims: [ 1, 26, 37 ]
+  }
+]